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The document discusses various types of controllers used in industrial control applications. It describes PI, PD, and PID controllers, which use proportional, integral, and derivative terms to adjust the control signal. Ziegler-Nichols and Cohen-Coon tuning methods are presented for optimizing controller parameters. Both analog and digital implementations of PID controllers are discussed.

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SENSORS

The document discusses different types of sensors, including displacement sensors like linear potentiometers and LVDTs, ultrasonic sensors, force sensors, temperature sensors, and pressure sensors. It provides details on how each sensor type works and describes common applications. The key points covered are the operating principles of common sensor technologies, their uses in fields like automation, medical, and manufacturing, and how sensors convert physical properties into electrical signals that can be measured.

Automation.pptx

PLC (Programmable Logic Controllers) are digital computers used to automate industrial processes. PLCs control machinery and processes through input and output modules connected to sensors and actuators. They are programmed using ladder logic and are widely used across industries like manufacturing. PLCs improve safety, increase productivity, and reduce costs by providing reliable control of automated industrial processes. DCS (Distributed Control Systems) are control systems used to automate large, complex industrial processes. DCS consists of multiple controllers connected to a central computer that work together to control a process. DCS offers flexibility, scalability, redundancy and integration with other systems, making it suitable for controlling complex industrial applications.

Actuators.pptx

The document discusses several types of actuators, including electric, hydraulic, pneumatic, piezoelectric, DC motors, servo motors, and stepper motors. Actuators are devices that convert various types of energy into motion or force and are used widely in applications such as manufacturing, robotics, aerospace, and medicine to control or move physical systems. Each actuator type has distinct advantages and uses depending on the precision, force, speed and other requirements of the application.

Signal Conditioning.pptx

This document discusses signal conditioning techniques including filtering, amplification, isolation, analog-to-digital conversion, and digital-to-analog conversion. It also discusses sensor protection circuits. Specifically:
Filtering involves removing unwanted frequencies from a signal using filters like low-pass, high-pass, band-pass, and notch filters. Amplification increases the amplitude of a signal using devices like op-amps while preserving its waveform. Isolation breaks electrical connections to prevent unwanted interactions, achieved using techniques like optical couplers and capacitive coupling. Analog-to-digital converters sample and quantize analog signals into digital formats. Digital-to-analog converters perform the reverse conversion from digital to analog. Sensor protection circuits like overvoltage

PID Controller and its design

The document discusses PID controllers, which are commonly used in industrial control systems. It describes the five main modes of PID control: on-off, proportional (P), proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID). The PID controller combines proportional, integral, and derivative actions to provide stable system response without steady-state error for various process control applications. Design of a PID controller involves tuning the proportional, integral, and derivative gains to achieve the desired closed-loop response.

Pid control

PID control is commonly used in robotics for motion control of drive train motors and servo actuators. It calculates error by comparing the actual state of the robot to the desired state, then minimizes error by adjusting the process. The P term provides action proportional to current error. I term reduces steady-state error by taking account of past errors over time. D term improves stability by considering current rate of change of error. Together these terms allow faster response while maintaining stability during disturbances. Careful tuning of the PID parameters is required for optimal performance without overshoot or oscillations.

Proportional integral and derivative PID controller

The document discusses PID controllers and their origins. It provides information on:
1) The basic components and functions of PID controllers, including proportional, integral and derivative terms that react to error, accumulated error over time, and rate of change of error respectively.
2) The benefits and limitations of proportional, integral and derivative control modes individually and in combination. PID controllers can reduce rise time, settling time and steady state error.
3) Applications of different PID variations and guidelines for controller design depending on process characteristics like temperature, flow or liquid level control.
4) Tips for designing PID controllers including obtaining an open-loop response and adjusting gains to achieve desired closed-loop performance.

Pid controllers

This document discusses different types of control systems and controllers, specifically focusing on PID controllers. It defines key terms like systems, processes, open-loop and closed-loop control. It then describes the different types of controllers - proportional (P), proportional-derivative (PD), proportional-integral (PI), and proportional-integral-derivative (PID). For each controller type, it provides the mathematical equation and discusses the properties and advantages, such as how adding integral control can eliminate steady-state error in PI controllers. Finally, it concludes with tips for designing PID controllers and the effects of increasing individual gains.

SENSORS

The document discusses different types of sensors, including displacement sensors like linear potentiometers and LVDTs, ultrasonic sensors, force sensors, temperature sensors, and pressure sensors. It provides details on how each sensor type works and describes common applications. The key points covered are the operating principles of common sensor technologies, their uses in fields like automation, medical, and manufacturing, and how sensors convert physical properties into electrical signals that can be measured.

Automation.pptx

PLC (Programmable Logic Controllers) are digital computers used to automate industrial processes. PLCs control machinery and processes through input and output modules connected to sensors and actuators. They are programmed using ladder logic and are widely used across industries like manufacturing. PLCs improve safety, increase productivity, and reduce costs by providing reliable control of automated industrial processes. DCS (Distributed Control Systems) are control systems used to automate large, complex industrial processes. DCS consists of multiple controllers connected to a central computer that work together to control a process. DCS offers flexibility, scalability, redundancy and integration with other systems, making it suitable for controlling complex industrial applications.

Actuators.pptx

The document discusses several types of actuators, including electric, hydraulic, pneumatic, piezoelectric, DC motors, servo motors, and stepper motors. Actuators are devices that convert various types of energy into motion or force and are used widely in applications such as manufacturing, robotics, aerospace, and medicine to control or move physical systems. Each actuator type has distinct advantages and uses depending on the precision, force, speed and other requirements of the application.

Signal Conditioning.pptx

This document discusses signal conditioning techniques including filtering, amplification, isolation, analog-to-digital conversion, and digital-to-analog conversion. It also discusses sensor protection circuits. Specifically:
Filtering involves removing unwanted frequencies from a signal using filters like low-pass, high-pass, band-pass, and notch filters. Amplification increases the amplitude of a signal using devices like op-amps while preserving its waveform. Isolation breaks electrical connections to prevent unwanted interactions, achieved using techniques like optical couplers and capacitive coupling. Analog-to-digital converters sample and quantize analog signals into digital formats. Digital-to-analog converters perform the reverse conversion from digital to analog. Sensor protection circuits like overvoltage

PID Controller and its design

The document discusses PID controllers, which are commonly used in industrial control systems. It describes the five main modes of PID control: on-off, proportional (P), proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID). The PID controller combines proportional, integral, and derivative actions to provide stable system response without steady-state error for various process control applications. Design of a PID controller involves tuning the proportional, integral, and derivative gains to achieve the desired closed-loop response.

Pid control

PID control is commonly used in robotics for motion control of drive train motors and servo actuators. It calculates error by comparing the actual state of the robot to the desired state, then minimizes error by adjusting the process. The P term provides action proportional to current error. I term reduces steady-state error by taking account of past errors over time. D term improves stability by considering current rate of change of error. Together these terms allow faster response while maintaining stability during disturbances. Careful tuning of the PID parameters is required for optimal performance without overshoot or oscillations.

Proportional integral and derivative PID controller

The document discusses PID controllers and their origins. It provides information on:
1) The basic components and functions of PID controllers, including proportional, integral and derivative terms that react to error, accumulated error over time, and rate of change of error respectively.
2) The benefits and limitations of proportional, integral and derivative control modes individually and in combination. PID controllers can reduce rise time, settling time and steady state error.
3) Applications of different PID variations and guidelines for controller design depending on process characteristics like temperature, flow or liquid level control.
4) Tips for designing PID controllers including obtaining an open-loop response and adjusting gains to achieve desired closed-loop performance.

Pid controllers

This document discusses different types of control systems and controllers, specifically focusing on PID controllers. It defines key terms like systems, processes, open-loop and closed-loop control. It then describes the different types of controllers - proportional (P), proportional-derivative (PD), proportional-integral (PI), and proportional-integral-derivative (PID). For each controller type, it provides the mathematical equation and discusses the properties and advantages, such as how adding integral control can eliminate steady-state error in PI controllers. Finally, it concludes with tips for designing PID controllers and the effects of increasing individual gains.

Discrete time control systems

This document provides a summary of the contents of a book on discrete-time control systems. It includes a table of contents that lists 8 chapters covering topics such as the z-transform, state-space representations, and optimal control. The preface introduces discrete-time control systems and notes their increasing use in industrial applications. It also defines key terms like continuous-time signals, discrete-time signals, sampled-data signals, and digital signals.

Pid controller

This document provides an overview of PID controllers, including:
- The basic feedback loop and proportional, integral, and derivative algorithms
- Implementation issues like set-point weighing, windup, and digital implementation
- Practical operational aspects like bumpless transfer between manual and automatic modes

Sensors and its types

A sensor is a device that detects and responds to some type of input from the physical environment.
The specific input could be light, heat, motion, moisture, pressure, or any one of a great number of other environmental phenomena.
The output is generally a signal that is converted to human-readable display at the sensor location or transmitted electronically over a network for reading or further processing.

Pid controller

A PID controller uses proportional, integral and derivative terms to minimize error over time between a measured process variable and desired setpoint. It continuously calculates an error value as the difference between the process variable and setpoint, and applies a correction based on proportional, integral and derivative terms for the error. The proportional term responds to current error, the integral term responds to accumulated historical error, and the derivative term responds to the rate of change of error. PID controllers are commonly used to control temperature, pressure, flow and other process variables due to their robustness and ability to achieve zero steady-state error.

PID controller

A PID controller is a control mechanism widely used in industrial systems that attempts to correct the error between a measured process variable and desired setpoint. It does this by calculating and outputting a corrective action based on proportional, integral, and derivative terms that can rapidly adjust the process and keep the error minimal. The weighted sum of these three terms is used to control an element like a valve or heating element position. Tuning the gains of each term provides control tailored to the specific process requirements.

Working Principals of Various Sensors

A light sensor detects ambient light levels and can include photoresistors, photodiodes, or phototransistors. It works by measuring changes in electrical resistance, voltage, or current caused by exposure to light. Light sensors have a wide range of applications including in street lights, cameras, alarms, and automatic lighting controls.

BMS COMMISSIONING CODE C_CIBSE

This code deals with the work satges required to commission automatic control systems in HVAC constuction industry. Represents standards for good practice in the form of recomantations and guidance. Is applicable for stand alone, BMS, DDC networked DDC and Integrated BMS systems.

Pid controller

The document discusses different types of controllers:
1) On-Off, P, PI, PD, and PID controllers. On-Off controllers have only two modes while P controllers use proportional gain. PI controllers add integral action to eliminate steady-state error. PD controllers use derivative action and PID controllers combine all three actions.
2) Block diagrams and transfer functions are presented to show how each controller type is modeled and its effect on the closed loop system. The proportional, integral, and derivative gains (Kp, Ki, Kd) determine each controller's effect.
3) PID controllers combine proportional, integral and derivative actions and are commonly used in industrial control systems due to their robust performance.

Unit 1 telemetry principles

Telemetry is the process of measuring a physical quantity at a remote location and transmitting the data to a central station. This document discusses different types of telemetry systems based on transmission medium (wire, radio, optical fiber), modulation method (DC, AC, pulse), input signal (analog, digital), and number of channels (single, multi). It also describes specific systems including pneumatic, electrical, hydraulic, and pulse telemetry. Common frequency ranges used for telemetry applications are identified.

Advanced Sensors

This document discusses several types of advanced sensors. It begins by distinguishing between analog and digital sensors, with analog sensors producing continuous outputs and digital sensors producing discrete outputs. It then examines several specific sensor types in more detail, including position sensors like potentiometers; temperature sensors like thermostats and thermocouples; light sensors such as photoresistors; and motion sensors including passive infrared and ultrasonic sensors. The document concludes by noting the wide variety of advanced sensors that can perform different functions.

Application of sensors : Thermistors and potentiometer

Application of sensors
Applications of potentiometer:
1. Audio Control The potentiometer is used in radio and television (TV) receiver for volume control, tone control and linearity control.
2. Continuous Balance DVM – The basic block diagram of a servo balancing potentiometer type DVM The input voltage is applied to one side of a mechanical chopper comparator, the other side being connected to the variable arm of a precision potentiometer.
3. Lighting
We can use a potentiometer to control the lighting level of a television, or the brightness of a computer screen.
RTD
1.Air and Gas Temperature Measurement with RTD Sensors
2.liquids Temperature Measurement with Flexible RTDs
The RTD temperature sensors are more accurate and precise then normally used temperature sensors and uses resistance concept to detect the temperature and convert to the digital value.
THERMISTOR
1.NTC Thermistors For Cooling Applications ((PCB)
2. Thermistors Temperature Detection in Fire Alarms.
The most cost effective fire alarm is one utilizing the thermistor method.

Electronic Measurement Flow Measurement

This document discusses various types of flow measurement. It begins by defining flowrate and explaining that flow occurs due to a pressure difference. The Hagen-Poiseuille equation relates flowrate to pressure difference, pipe diameter, fluid viscosity and length. Reynolds number determines if flow is laminar or turbulent. Differential pressure flow meters like venturi tubes and orifices use a restriction to create a pressure difference proportional to flowrate. Other meter types discussed include magnetic, ultrasonic, turbine and positive displacement meters. Effects like Coanda and Coriolis are also summarized.

PID controller, P, I and D control Comparison PI, PD and PID Controller P, I,...

P, I and D control Comparison
PI, PD and PID Controller
P, I, D, PI, PD, PID using OP-AMP
The Characteristics of P, I, and D controllers

Basics of Sensors & Transducers

This article provides an introduction to the fundamental of Sensors and Transducers. It illustrates the different classifications of sensors and transducers. Explains capacitive, resistive and inductive transducers in brief. Also shows the examples under these types of transducers.

Dcs vs scada

The document discusses distributed control systems (DCS) and supervisory control and data acquisition (SCADA) systems. It provides an introduction and overview of key concepts for both DCS and SCADA. For DCS, it describes the components, functions, applications and how a DCS works. For SCADA, it outlines where SCADA is used, hardware and software architectures, and how SCADA systems function through data acquisition, communication, presentation and control.

PID Control

This document describes an experiment to build an analog PID controller circuit using op-amps. It includes:
1. An introduction to the basic concepts of proportional, integral and derivative control and how they are implemented using op-amps.
2. Analysis of the circuit diagrams for differential input, derivative, and integrator op-amps used to build the PID controller.
3. Procedures to assemble the circuit, test it using a function generator and oscilloscope, and calculate gains.
4. Results showing the input-output signal relationships for each component, along with calculated gains and pole locations. Square and sine wave responses are shown for the complete PID controller.
5. A conclusion that the

Automation PLC & SCADA

This document provides an overview of programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems. It discusses the history and evolution of automation and PLCs, describes common PLC components and programming, and reviews the MicroLogix 1000 PLC and RSLogix5000 programming software. Key features of SCADA systems are also summarized, including dynamic graphics, alarms, recipe management, security, connectivity, databases, and scripting. The document is submitted by Nitish Kumar Singh for review by KL Pursnani and covers automation, PLCs, ladder logic, MicroLogix1000, and SCADA systems at a high level.

Types of sensors

Types of sensors- mechatronics
Download link:
http://skillcruise.com/academics/robotics-mechatronics/sensors/

Class 30 controller tuning

This document describes the open-loop transient response method for tuning controllers. It involves disconnecting the controller, making a small manual disturbance to the process, and recording the response of the controlled variable over time. The lag time and process reaction time are determined from the response curve. These values along with the disturbance size are used with Ziegler-Nichols tuning formulas to calculate controller settings for proportional, PI, and PID control modes. The document provides details on applying this tuning technique and references for further information.

Measurements lecture 1

1. Measurement involves comparing an unknown value to a known standard using an instrument. Common instruments include indicators, recorders, and integrators.
2. Calibration ensures accurate measurements by comparing instrument readings to a primary or secondary standard over the measurement range.
3. Damping minimizes oscillations to provide steady, accurate readings by introducing opposing forces through methods like air friction, eddy currents, or fluid friction.

Paper id 21201482

This document provides an overview of different approaches for tuning PID controllers. It first introduces PID controllers and their proportional, integral and derivative terms. It then describes several common methods for tuning PID controllers, including manual tuning on-site, Ziegler-Nichols reaction curve method, Ziegler-Nichols oscillation method, and Cohen-Coon method. These tuning methods are compared based on their performance and applicability to different process control systems.

pid controller

This document provides an overview of PID controllers, including:
- The three components of a PID controller are proportional, integral, and derivative terms.
- PID controllers are widely used in industrial control systems due to their general applicability even without a mathematical model of the system.
- Ziegler-Nichols tuning rules can be used to experimentally determine initial PID parameters to provide a stable initial response for the system. Fine-tuning is then used to optimize the response.

Discrete time control systems

This document provides a summary of the contents of a book on discrete-time control systems. It includes a table of contents that lists 8 chapters covering topics such as the z-transform, state-space representations, and optimal control. The preface introduces discrete-time control systems and notes their increasing use in industrial applications. It also defines key terms like continuous-time signals, discrete-time signals, sampled-data signals, and digital signals.

Pid controller

This document provides an overview of PID controllers, including:
- The basic feedback loop and proportional, integral, and derivative algorithms
- Implementation issues like set-point weighing, windup, and digital implementation
- Practical operational aspects like bumpless transfer between manual and automatic modes

Sensors and its types

A sensor is a device that detects and responds to some type of input from the physical environment.
The specific input could be light, heat, motion, moisture, pressure, or any one of a great number of other environmental phenomena.
The output is generally a signal that is converted to human-readable display at the sensor location or transmitted electronically over a network for reading or further processing.

Pid controller

A PID controller uses proportional, integral and derivative terms to minimize error over time between a measured process variable and desired setpoint. It continuously calculates an error value as the difference between the process variable and setpoint, and applies a correction based on proportional, integral and derivative terms for the error. The proportional term responds to current error, the integral term responds to accumulated historical error, and the derivative term responds to the rate of change of error. PID controllers are commonly used to control temperature, pressure, flow and other process variables due to their robustness and ability to achieve zero steady-state error.

PID controller

A PID controller is a control mechanism widely used in industrial systems that attempts to correct the error between a measured process variable and desired setpoint. It does this by calculating and outputting a corrective action based on proportional, integral, and derivative terms that can rapidly adjust the process and keep the error minimal. The weighted sum of these three terms is used to control an element like a valve or heating element position. Tuning the gains of each term provides control tailored to the specific process requirements.

Working Principals of Various Sensors

A light sensor detects ambient light levels and can include photoresistors, photodiodes, or phototransistors. It works by measuring changes in electrical resistance, voltage, or current caused by exposure to light. Light sensors have a wide range of applications including in street lights, cameras, alarms, and automatic lighting controls.

BMS COMMISSIONING CODE C_CIBSE

This code deals with the work satges required to commission automatic control systems in HVAC constuction industry. Represents standards for good practice in the form of recomantations and guidance. Is applicable for stand alone, BMS, DDC networked DDC and Integrated BMS systems.

Pid controller

The document discusses different types of controllers:
1) On-Off, P, PI, PD, and PID controllers. On-Off controllers have only two modes while P controllers use proportional gain. PI controllers add integral action to eliminate steady-state error. PD controllers use derivative action and PID controllers combine all three actions.
2) Block diagrams and transfer functions are presented to show how each controller type is modeled and its effect on the closed loop system. The proportional, integral, and derivative gains (Kp, Ki, Kd) determine each controller's effect.
3) PID controllers combine proportional, integral and derivative actions and are commonly used in industrial control systems due to their robust performance.

Unit 1 telemetry principles

Telemetry is the process of measuring a physical quantity at a remote location and transmitting the data to a central station. This document discusses different types of telemetry systems based on transmission medium (wire, radio, optical fiber), modulation method (DC, AC, pulse), input signal (analog, digital), and number of channels (single, multi). It also describes specific systems including pneumatic, electrical, hydraulic, and pulse telemetry. Common frequency ranges used for telemetry applications are identified.

Advanced Sensors

This document discusses several types of advanced sensors. It begins by distinguishing between analog and digital sensors, with analog sensors producing continuous outputs and digital sensors producing discrete outputs. It then examines several specific sensor types in more detail, including position sensors like potentiometers; temperature sensors like thermostats and thermocouples; light sensors such as photoresistors; and motion sensors including passive infrared and ultrasonic sensors. The document concludes by noting the wide variety of advanced sensors that can perform different functions.

Application of sensors : Thermistors and potentiometer

Application of sensors
Applications of potentiometer:
1. Audio Control The potentiometer is used in radio and television (TV) receiver for volume control, tone control and linearity control.
2. Continuous Balance DVM – The basic block diagram of a servo balancing potentiometer type DVM The input voltage is applied to one side of a mechanical chopper comparator, the other side being connected to the variable arm of a precision potentiometer.
3. Lighting
We can use a potentiometer to control the lighting level of a television, or the brightness of a computer screen.
RTD
1.Air and Gas Temperature Measurement with RTD Sensors
2.liquids Temperature Measurement with Flexible RTDs
The RTD temperature sensors are more accurate and precise then normally used temperature sensors and uses resistance concept to detect the temperature and convert to the digital value.
THERMISTOR
1.NTC Thermistors For Cooling Applications ((PCB)
2. Thermistors Temperature Detection in Fire Alarms.
The most cost effective fire alarm is one utilizing the thermistor method.

Electronic Measurement Flow Measurement

This document discusses various types of flow measurement. It begins by defining flowrate and explaining that flow occurs due to a pressure difference. The Hagen-Poiseuille equation relates flowrate to pressure difference, pipe diameter, fluid viscosity and length. Reynolds number determines if flow is laminar or turbulent. Differential pressure flow meters like venturi tubes and orifices use a restriction to create a pressure difference proportional to flowrate. Other meter types discussed include magnetic, ultrasonic, turbine and positive displacement meters. Effects like Coanda and Coriolis are also summarized.

PID controller, P, I and D control Comparison PI, PD and PID Controller P, I,...

P, I and D control Comparison
PI, PD and PID Controller
P, I, D, PI, PD, PID using OP-AMP
The Characteristics of P, I, and D controllers

Basics of Sensors & Transducers

This article provides an introduction to the fundamental of Sensors and Transducers. It illustrates the different classifications of sensors and transducers. Explains capacitive, resistive and inductive transducers in brief. Also shows the examples under these types of transducers.

Dcs vs scada

The document discusses distributed control systems (DCS) and supervisory control and data acquisition (SCADA) systems. It provides an introduction and overview of key concepts for both DCS and SCADA. For DCS, it describes the components, functions, applications and how a DCS works. For SCADA, it outlines where SCADA is used, hardware and software architectures, and how SCADA systems function through data acquisition, communication, presentation and control.

PID Control

This document describes an experiment to build an analog PID controller circuit using op-amps. It includes:
1. An introduction to the basic concepts of proportional, integral and derivative control and how they are implemented using op-amps.
2. Analysis of the circuit diagrams for differential input, derivative, and integrator op-amps used to build the PID controller.
3. Procedures to assemble the circuit, test it using a function generator and oscilloscope, and calculate gains.
4. Results showing the input-output signal relationships for each component, along with calculated gains and pole locations. Square and sine wave responses are shown for the complete PID controller.
5. A conclusion that the

Automation PLC & SCADA

This document provides an overview of programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems. It discusses the history and evolution of automation and PLCs, describes common PLC components and programming, and reviews the MicroLogix 1000 PLC and RSLogix5000 programming software. Key features of SCADA systems are also summarized, including dynamic graphics, alarms, recipe management, security, connectivity, databases, and scripting. The document is submitted by Nitish Kumar Singh for review by KL Pursnani and covers automation, PLCs, ladder logic, MicroLogix1000, and SCADA systems at a high level.

Types of sensors

Types of sensors- mechatronics
Download link:
http://skillcruise.com/academics/robotics-mechatronics/sensors/

Class 30 controller tuning

This document describes the open-loop transient response method for tuning controllers. It involves disconnecting the controller, making a small manual disturbance to the process, and recording the response of the controlled variable over time. The lag time and process reaction time are determined from the response curve. These values along with the disturbance size are used with Ziegler-Nichols tuning formulas to calculate controller settings for proportional, PI, and PID control modes. The document provides details on applying this tuning technique and references for further information.

Measurements lecture 1

1. Measurement involves comparing an unknown value to a known standard using an instrument. Common instruments include indicators, recorders, and integrators.
2. Calibration ensures accurate measurements by comparing instrument readings to a primary or secondary standard over the measurement range.
3. Damping minimizes oscillations to provide steady, accurate readings by introducing opposing forces through methods like air friction, eddy currents, or fluid friction.

Discrete time control systems

Discrete time control systems

Pid controller

Pid controller

Sensors and its types

Sensors and its types

Pid controller

Pid controller

PID controller

PID controller

Working Principals of Various Sensors

Working Principals of Various Sensors

BMS COMMISSIONING CODE C_CIBSE

BMS COMMISSIONING CODE C_CIBSE

Pid controller

Pid controller

Unit 1 telemetry principles

Unit 1 telemetry principles

Advanced Sensors

Advanced Sensors

Application of sensors : Thermistors and potentiometer

Application of sensors : Thermistors and potentiometer

Electronic Measurement Flow Measurement

Electronic Measurement Flow Measurement

PID controller, P, I and D control Comparison PI, PD and PID Controller P, I,...

PID controller, P, I and D control Comparison PI, PD and PID Controller P, I,...

Basics of Sensors & Transducers

Basics of Sensors & Transducers

Dcs vs scada

Dcs vs scada

PID Control

PID Control

Automation PLC & SCADA

Automation PLC & SCADA

Types of sensors

Types of sensors

Class 30 controller tuning

Class 30 controller tuning

Measurements lecture 1

Measurements lecture 1

Paper id 21201482

This document provides an overview of different approaches for tuning PID controllers. It first introduces PID controllers and their proportional, integral and derivative terms. It then describes several common methods for tuning PID controllers, including manual tuning on-site, Ziegler-Nichols reaction curve method, Ziegler-Nichols oscillation method, and Cohen-Coon method. These tuning methods are compared based on their performance and applicability to different process control systems.

pid controller

This document provides an overview of PID controllers, including:
- The three components of a PID controller are proportional, integral, and derivative terms.
- PID controllers are widely used in industrial control systems due to their general applicability even without a mathematical model of the system.
- Ziegler-Nichols tuning rules can be used to experimentally determine initial PID parameters to provide a stable initial response for the system. Fine-tuning is then used to optimize the response.

Screenshot 2021-02-23 at 2.46.02 PM.pdf

The document discusses PID controllers, including:
1) PID controllers use proportional, integral and derivative modes to control systems. The proportional mode determines how much correction is made, the integral mode determines how long a correction is applied, and the derivative mode determines how fast a correction is made.
2) Ziegler-Nichols tuning rules provide methods to experimentally determine PID parameters (Kp, Ti, Td) when mathematical models are unknown, including open-loop and closed-loop methods using a plant's step response.
3) An electronic PID controller can be implemented as a circuit using resistors and capacitors to realize the proportional, integral and derivative terms.

4470838.ppt

The document discusses proportional (P) control and its limitations. A P-only controller can reduce fluctuations but cannot eliminate steady-state error or offset. Adding an integral (I) term can eliminate offset by incorporating past errors, but higher I gain can cause instability. The document examines examples of P-only control response and how adding I improves response while reducing overshoot and oscillations. However, carefully tuning the gains is necessary for stability.

At4201308314

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Lec 5 pid

The document discusses PID controllers and their tuning methods. It provides information on:
- The components and design of PID controllers, including proportional, integral and derivative modes.
- Common tuning methods for PID controllers like Ziegler-Nichols tuning, which involves finding the critical gain and oscillation period to determine PID parameters.
- The characteristics of proportional, integral and derivative controllers and how they affect rise time, overshoot, settling time and steady state error.
- An example problem demonstrating the use of Ziegler-Nichols tuning to design a PID controller for a given system.

Bj4301341344

This document compares the performance of PID, PI, and MPC controllers for controlling water level in a tank process. It describes modeling the first-order plus dead time process in MATLAB and tuning the PID controller using Ziegler-Nichols method. Simulation results show that the MPC controller achieved better performance than the PID and PI controllers in terms of rise time, settling time, and overshoot. Specifically, the MPC controller had the shortest rise time and settling time, as well as the lowest overshoot of the three controllers evaluated.

V13I1006.pdf

This document summarizes a study that evaluated the performance of tuned PID controllers for speed control of a DC motor. The researchers developed linear and nonlinear mathematical models of a DC motor and represented the system using state space equations. They then simulated four different PID controllers using MATLAB/Simulink to control the motor speed in response to a step input signal. The system responses under each controller were analyzed and discussed in terms of their performance.

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

This document discusses controlling the position of a DC motor using fuzzy proportional-derivative controllers with different defuzzification methods. It first introduces Shravan Kumar Yadav and his background. It then models a DC motor in Simulink and designs a crisp PD controller as a benchmark. Different fuzzy PD controllers using various defuzzification methods are implemented and their responses compared. The fuzzy controllers are able to reject disturbances without retuning, unlike the crisp PD controller. The purpose is to control DC motor position using fuzzy logic control in MATLAB and compare its performance to PID control.

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

This document discusses using fuzzy proportional-derivative (FPD) controllers to control the position of a DC motor. It first describes modeling the DC motor in Simulink and designing a crisp proportional-derivative (PD) controller as a benchmark. Then it discusses designing an FPD controller and comparing the system responses using different defuzzification methods. It finds that the FPD controller is able to reject disturbances without further tuning, unlike the crisp PD controller.

F010133747

This document discusses using fuzzy proportional-derivative (FPD) controllers to control the position of a DC motor. It first describes modeling the DC motor in Simulink and designing a crisp proportional-derivative (PD) controller as a benchmark. Then it discusses designing an FPD controller and comparing the system responses using different defuzzification methods. It finds that the FPD controller is able to reject disturbances without further tuning, unlike the crisp PD controller.

Optimal tuning of pid power system stabilizer in simulink environment

This document summarizes a research paper that proposes using an optimal tuning method based on the Ziegler-Nichols tuning rules to determine the parameters of a PID power system stabilizer (PSS) controller in a MATLAB/Simulink model. The paper models a single machine connected to an infinite bus system, adds a PID controller with PSS, and simulates the system under normal load conditions. Simulation results show that the Ziegler-Nichols method yields PID controller parameters that provide better dynamic performance compared to a conventional trial and error approach, with lower overshoot and shorter settling time. The proposed optimally tuned PID-PSS controller thus improves the stability and performance of the synchronous generator in the simulated power system.

Ge2310721081

This document describes a PID controller with self-tuning capabilities using fuzzy logic. It replaces the conventional PID controller in a chopper-fed DC motor drive system to improve performance. The PID gains (KP, KI, KD) are automatically tuned online by a fuzzy logic controller based on the error and change in error. This allows the PID gains to adapt as needed for different operating conditions. Simulation results showed the proposed self-tuning PID controller performed better than a conventional PID controller.

IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...

This document describes research into using different controller types, including fuzzy logic controllers and genetic algorithm optimized PID controllers, to control a STATCOM device for improved reactive power compensation performance. A STATCOM is a shunt Flexible AC Transmission System device that can help solve power quality issues. Conventionally, PID controllers are used but require trial and error to tune parameters. The document models a STATCOM system and explores using fuzzy logic control or genetic algorithms to automatically determine optimal PID parameters to achieve faster response compared to conventional PID control. Simulation results in MATLAB show that both fuzzy logic control and genetic algorithm optimized PID control improve the STATCOM current control response compared to manually tuned PID controllers.

IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...

This document discusses control methods for STATCOMs using fuzzy logic controllers and genetic algorithm-tuned PID controllers. STATCOMs are shunt FACTS devices that help solve power quality issues through fast reactive power control. Conventionally, PID controllers are used but require trial and error to tune parameters. The document proposes using fuzzy logic controllers and genetic algorithms to optimize PID parameters to improve STATCOM current control response. It describes STATCOM modeling, fuzzy logic controller design including fuzzification, inference, and defuzzification. Genetic algorithms are used to find optimal PID parameters. Simulation results in MATLAB show the proposed methods improve current control response over conventional PID control.

5_2018_12_17!10_45_47_AM.ppt

1) Fuzzy logic control can be used to improve PID controllers by adapting their P, I, and D parameters using fuzzy rules based on system measurements and errors.
2) A fuzzy PID controller works similarly to a traditional PID controller but uses fuzzy logic to determine the control signal rather than linear combinations of error terms. Membership functions and rules can be designed based on an initially tuned PID controller.
3) Supervisory fuzzy control uses fuzzy logic at a higher level to monitor process control systems and tune or override lower level PID controllers as needed to optimize performance under different conditions. It provides adaptive, experience-based control like human operators.

1011ijaia03

This document presents a method for automatically tuning PID controllers using particle swarm optimization (PSO) algorithm. It describes PID controllers and common tuning methods like Ziegler-Nichols. It then provides an overview of PSO algorithm and how it can be applied to optimize PID parameters to minimize a performance index for a DC motor model. Simulation results show the PSO-tuned PID controller provides improved rise time, settling time and overshoot compared to Ziegler-Nichols tuning.

A fuzzy model based adaptive pid controller design for nonlinear and uncertai...

We develop a novel adaptive tuning method for classical proportional–integral–derivative (PID)
controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to
overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in
industry, to the control of nonlinear processes, we introduce a method which can readily be used by the
industry. In this method, controller design does not require a first principal model of the process which is
usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from
the measured input–output data of the process. A soft limiter is used to impose industrial limits on the
control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear
process involving instabilities. Several tests showed the method's success in tracking, robustness to noise,
and adaptation properties. We as well compared our system's performance to those of a plant with
altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude,
we present a novel adaptive control method that is built upon the well-known PID architecture that
successfully controls highly nonlinear industrial processes, even under conditions such as strong
parameter variations, noise, and instabilities

Tuning for PID Controllers.pdf

PID controllers are commonly used to control industrial processes due to their simplicity. Tuning PID controllers on-site is often necessary due to process variations. The Ziegler-Nichols tuning method provides initial estimated PID parameters based on the process response to a step input or by increasing controller gain until sustained oscillations occur. The method yields parameters that provide acceptable overshoot and settling time for initial tuning, which can then be fine-tuned based on the effects of each parameter.

Classical Techniques for PID Tunning: Review

This document provides a review of classical techniques for PID tuning, including the Ziegler-Nichols and Cohen-Coon methods. It discusses the advantages and disadvantages of these traditional tuning approaches and gives examples of PID controller applications in temperature control, manufacturing processes, renewable energy systems, and other industrial uses.

Paper id 21201482

Paper id 21201482

pid controller

pid controller

Screenshot 2021-02-23 at 2.46.02 PM.pdf

Screenshot 2021-02-23 at 2.46.02 PM.pdf

4470838.ppt

4470838.ppt

At4201308314

At4201308314

Lec 5 pid

Lec 5 pid

Bj4301341344

Bj4301341344

V13I1006.pdf

V13I1006.pdf

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers Wit...

F010133747

F010133747

Optimal tuning of pid power system stabilizer in simulink environment

Optimal tuning of pid power system stabilizer in simulink environment

Ge2310721081

Ge2310721081

IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...

IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...

IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...

IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...

5_2018_12_17!10_45_47_AM.ppt

5_2018_12_17!10_45_47_AM.ppt

1011ijaia03

1011ijaia03

A fuzzy model based adaptive pid controller design for nonlinear and uncertai...

A fuzzy model based adaptive pid controller design for nonlinear and uncertai...

Tuning for PID Controllers.pdf

Tuning for PID Controllers.pdf

Classical Techniques for PID Tunning: Review

Classical Techniques for PID Tunning: Review

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

CEC 352 - SATELLITE COMMUNICATION UNIT 1

SATELLITE COMMUNICATION

SCALING OF MOS CIRCUITS m .pptx

this ppt explains about scaling parameters of the mosfet it is basically vlsi subject

Engineering Standards Wiring methods.pdf

Engineering Standards Wiring methods.pdf

An Introduction to the Compiler Designss

compiler material

132/33KV substation case study Presentation

132/33Kv substation case study ppt

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

Conceptos basicos de fisica

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

Rainfall intensity duration frequency curve statistical analysis and modeling...

Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

Software Engineering and Project Management - Software Testing + Agile Method...

Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.

Advanced control scheme of doubly fed induction generator for wind turbine us...

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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Generative AI Use cases applications solutions and implementation.pdf

Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/

ITSM Integration with MuleSoft.pptx

ITSM Integration with mulesoft

22CYT12-Unit-V-E Waste and its Management.ppt

Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.

Software Engineering and Project Management - Introduction, Modeling Concepts...

Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.

Properties Railway Sleepers and Test.pptx

Properties Railway Sleepers and Test.pptx

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

CEC 352 - SATELLITE COMMUNICATION UNIT 1

CEC 352 - SATELLITE COMMUNICATION UNIT 1

SCALING OF MOS CIRCUITS m .pptx

SCALING OF MOS CIRCUITS m .pptx

Engineering Standards Wiring methods.pdf

Engineering Standards Wiring methods.pdf

An Introduction to the Compiler Designss

An Introduction to the Compiler Designss

132/33KV substation case study Presentation

132/33KV substation case study Presentation

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

TIME TABLE MANAGEMENT SYSTEM testing.pptx

TIME TABLE MANAGEMENT SYSTEM testing.pptx

Rainfall intensity duration frequency curve statistical analysis and modeling...

Rainfall intensity duration frequency curve statistical analysis and modeling...

Software Engineering and Project Management - Software Testing + Agile Method...

Software Engineering and Project Management - Software Testing + Agile Method...

Advanced control scheme of doubly fed induction generator for wind turbine us...

Advanced control scheme of doubly fed induction generator for wind turbine us...

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf

1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf

Generative AI Use cases applications solutions and implementation.pdf

Generative AI Use cases applications solutions and implementation.pdf

ITSM Integration with MuleSoft.pptx

ITSM Integration with MuleSoft.pptx

22CYT12-Unit-V-E Waste and its Management.ppt

22CYT12-Unit-V-E Waste and its Management.ppt

Software Engineering and Project Management - Introduction, Modeling Concepts...

Software Engineering and Project Management - Introduction, Modeling Concepts...

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

- 1. Chapter:4 Controller tuning Suvendu Mondal AP,EED, SurTech 20-04-2023 SurTech, JIS, DumDum 1
- 2. PI controller • A PI controller, also known as a proportional-integral controller, is a type of feedback control system commonly used in industrial control applications. It is a type of controller that uses two terms to adjust the control signal: the proportional term and the integral term. • The proportional term in a PI controller produces an output that is proportional to the error between the desired set point and the actual process variable. The integral term in a PI controller produces an output that is proportional to the integral of the error over time. The sum of these two terms forms the output of the controller, which is used to adjust the control signal to the process. • The proportional term in the PI controller helps to reduce the steady-state error, while the integral term helps to reduce the transient error. The PI controller is widely used in various industrial applications such as temperature control, pressure control, and speed control. • The PI controller can be tuned to achieve optimal performance by adjusting the values of the proportional gain (Kp) and the integral gain (Ki). The values of Kp and Ki can be adjusted to achieve the desired response of the control system, such as minimizing overshoot, reducing settling time, and improving stability. • In summary, the PI controller is a widely used feedback control system that uses two terms, the proportional term and the integral term, to adjust the control signal. It is commonly used in industrial control applications and can be tuned to achieve optimal performance by adjusting the values of the proportional gain and integral gain. 20-04-2023 SurTech, JIS, DumDum 2
- 3. PD controller • A PD controller, also known as a proportional-derivative controller, is a type of feedback control system that uses two terms to adjust the control signal: the proportional term and the derivative term. • The proportional term in a PD controller produces an output that is proportional to the error between the desired set point and the actual process variable, similar to the PI controller. The derivative term in a PD controller produces an output that is proportional to the rate of change of the error over time. The sum of these two terms forms the output of the controller, which is used to adjust the control signal to the process. • The derivative term in a PD controller helps to predict and anticipate the future error, which can be used to adjust the control signal before the error occurs. This can help to reduce the overshoot and settling time in the control system, resulting in faster and more accurate control. • PD controllers are commonly used in industrial control applications, particularly in systems where fast response and precise control are required, such as in robotics, motors, and servo systems. • The PD controller can be tuned to achieve optimal performance by adjusting the values of the proportional gain (Kp) and the derivative gain (Kd). The values of Kp and Kd can be adjusted to achieve the desired response of the control system, such as minimizing overshoot, reducing settling time, and improving stability. • In summary, the PD controller is a feedback control system that uses two terms, the proportional term and the derivative term, to adjust the control signal. It is commonly used in industrial control applications where fast response and precise control are required, and can be tuned to achieve optimal performance by adjusting the values of the proportional gain and derivative gain 20-04-2023 SurTech, JIS, DumDum 3
- 4. PID controller • A PID controller, also known as a proportional-integral-derivative controller, is a type of feedback control system that uses three terms to adjust the control signal: the proportional term, the integral term, and the derivative term. • The proportional term in a PID controller produces an output that is proportional to the error between the desired setpoint and the actual process variable, similar to the PI controller. The integral term produces an output that is proportional to the integral of the error over time, and the derivative term produces an output that is proportional to the rate of change of the error over time. • The sum of these three terms forms the output of the controller, which is used to adjust the control signal to the process. The proportional term provides a rapid response to changes in the error, while the integral term helps to reduce the steady-state error, and the derivative term helps to reduce the overshoot and settling time in the control system. • PID controllers are commonly used in various industrial control applications, such as temperature control, pressure control, and speed control, as well as in robotics and automation systems. • The PID controller can be tuned to achieve optimal performance by adjusting the values of the proportional gain (Kp), the integral gain (Ki), and the derivative gain (Kd). The values of Kp, Ki, and Kd can be adjusted to achieve the desired response of the control system, such as minimizing overshoot, reducing settling time, and improving stability. • In summary, the PID controller is a feedback control system that uses three terms, the proportional term, the integral term, and the derivative term, to adjust the control signal. It is commonly used in industrial control applications and can be tuned to achieve optimal performance by adjusting the values of the proportional gain, integral gain, and derivative gain 20-04-2023 SurTech, JIS, DumDum 4
- 5. Ziegler-Nichols tuning method • The Ziegler-Nichols tuning method is a popular and widely used technique for tuning PID controllers. It was proposed by John G. Ziegler and Nathaniel B. Nichols in the 1940s and is also known as the ultimate gain method. • The Ziegler-Nichols tuning method involves a step response test in which a step change is made to the setpoint of the control system and the resulting response of the system is observed. Based on the response, the ultimate gain and ultimate period of the system are determined. • The ultimate gain (Ku) is the gain at which the system starts to oscillate continuously, and the ultimate period (Tu) is the period of oscillation at that gain. From these values, the controller gains (Kp, Ki, and Kd) can be calculated using the following rules: • Proportional gain (Kp) = 0.6*Ku • Integral gain (Ki) = 1.2*Ku/Tu • Derivative gain (Kd) = 0.075KuTu • Once the gains are calculated, the controller can be tested again to verify its performance and adjusted if necessary. • The Ziegler-Nichols tuning method is a simple and effective method for tuning PID controllers, but it has some limitations. It is primarily suited for systems with a single dominant time constant and can result in overshoot and instability in some systems. Therefore, it is important to verify the performance of the controller after tuning and make adjustments as necessary. 20-04-2023 SurTech, JIS, DumDum 5
- 6. Cohen coon tuning method • The Cohen-Coon tuning method is another popular technique for tuning PID controllers. It was proposed by G.C. Cohen and D.W. Coon in 1953 and is based on the process reaction curve of the system. • The Cohen-Coon tuning method involves a step response test in which a step change is made to the set point of the control system and the resulting response of the system is observed. Based on the response, the process parameters of the system are determined, including the process gain (Kp) and the process time constant (Tp). • The controller gains (Kp, Ki, and Kd) can then be calculated using the following equations: • Proportional gain (Kp) = (1.35*Tp)/Kp • Integral time (Ti) = 2.5*Tp • Derivative time (Td) = 0.37*Tp • Once the gains are calculated, the controller can be tested again to verify its performance and adjusted if necessary. • The Cohen-Coon tuning method is a relatively simple and effective method for tuning PID controllers, but it can result in overshoot and instability in some systems. Therefore, it is important to verify the performance of the controller after tuning and make adjustments as necessary. It is also important to note that this method assumes that the system has a first-order plus dead-time (FOPDT) response, and may not be suitable for more complex systems. 20-04-2023 SurTech, JIS, DumDum 6
- 7. Implementation of PID controllers (digital and analog) • PID controllers can be implemented using both digital and analog circuits, depending on the application and system requirements. • Analog PID Controller: Analog PID controllers use operational amplifiers and other analog components to implement the PID algorithm. Analog PID controllers are widely used in process control systems, where high-speed and high-precision control is required. The analog PID controller circuit consists of three amplifiers, each connected to the proportional, integral, and derivative signals. The input signal is first amplified and then split into three branches, each with its gain controlled by the proportional, integral, and derivative gain coefficients. The outputs from the three branches are then added together and fed into the plant. The output from the plant is then fed back to the controller as feedback, which is used to adjust the control signal. 20-04-2023 SurTech, JIS, DumDum 7
- 8. Implementation of PID controllers (digital and analog) • Digital PID Controller: Digital PID controllers use microcontrollers or digital signal processors (DSPs) to implement the PID algorithm. Digital PID controllers are widely used in control systems that require flexible and programmable control algorithms. • The digital PID controller circuit consists of a microcontroller or DSP, which runs the PID algorithm using the feedback signal and set point. The microcontroller or DSP uses digital computation to calculate the control signal based on the proportional, integral, and derivative terms. The control signal is then output to a digital-to-analog converter (DAC), which converts the digital signal to an analog signal that can be used to control the plant. • The advantage of a digital PID controller over an analog controller is the ability to easily change the PID algorithm by modifying the software. Additionally, digital PID controllers can handle complex control algorithms and advanced control strategies, which may not be possible with analog controllers. 20-04-2023 SurTech, JIS, DumDum 8
- 9. Implementation of PID controllers (digital and analog) • In summary, PID controllers can be implemented using both analog and digital circuits, depending on the application and system requirements. Analog PID controllers use operational amplifiers and other analog components, while digital PID controllers use microcontrollers or DSPs. The choice of implementation depends on factors such as accuracy, speed, complexity, and flexibility of the control system. 20-04-2023 SurTech, JIS, DumDum 9