The document provides an introduction to PID control, including:
- A course plan that covers what PID control is, using the UNICOS PID controller, manual and automatic tuning exercises, and a question/wrap-up session.
- An explanation of the components of a feedback control system including defining the setpoint, influencing and observing the process, and closing the control loop.
- How proportional, integral and derivative control actions work to solve the problems of steady state error and oscillations in proportional-only control.
- The full PID control equation incorporating proportional, integral and derivative terms with their corresponding parameters - proportional gain, integral time and derivative time.
This document provides an introduction and overview of PID control. It begins with explaining the basic components of a feedback control system and then describes how a PID controller works by incorporating proportional, integral and derivative actions to address issues with proportional control alone.
The course plan is outlined, covering what PID control is, using the UNICOS PID controller, manual and auto-tuning exercises.
The document dives into more detail on proportional, integral and derivative control terms. It explains how adding an integral term solves steady-state error and a derivative term solves oscillations. The full PID equation incorporating proportional, integral and derivative terms with parameters is presented.
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.
This document provides an overview of process control concepts including:
1. Process control refers to methods used to control process variables when manufacturing a product. Modeling the process is important for understanding how to control it.
2. The basic elements of a process control loop include a measurement, controller, actuator, and process. Common signals are the process variable and manipulated variable.
3. Common types of controllers are on-off, proportional, integral, derivative, and PID. On-off is simple but ineffective. Proportional reduces offset but has steady state error. Integral eliminates offset but can oscillate. Derivative reduces oscillations. PID combines all three for optimal control.
4. Cascade control uses
This document provides an overview of PID control including:
- A brief history of PID control and its widespread use in process control applications.
- Explanations of proportional, integral and derivative control and how each term works to minimize error in a control loop.
- Details on implementing a PID controller including choosing a structure and tuning parameters.
- An example PID control loop for controlling oil level in a tank.
- Pseudocode for a software-based PID control algorithm.
The document serves as an introduction to PID control theory and implementation for process control applications.
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.
This document discusses elements of process control systems including sensors, controllers, and control elements. It provides definitions of these elements and describes how they relate and interact in a process control loop based on a block diagram approach. The key elements are the process being controlled, sensors that measure process variables, a controller that determines necessary control actions, and control elements that implement adjustments to the process. The document also discusses criteria for evaluating how well a control system is performing including stability, steady-state regulation, and transient response.
Three control loops are described: open loop control which takes action without feedback, closed loop control which measures process variables, compares to setpoints, and adjusts to correct deviations, and proportional control which adjusts the correcting element proportionally to the error. Key aspects of proportional, integral, and derivative control modes are also summarized. Proportional control responds directly to error, integral control eliminates offset through repeated proportional action, and derivative control improves response in slow processes by anticipating needed output changes.
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.
This document provides an introduction and overview of PID control. It begins with explaining the basic components of a feedback control system and then describes how a PID controller works by incorporating proportional, integral and derivative actions to address issues with proportional control alone.
The course plan is outlined, covering what PID control is, using the UNICOS PID controller, manual and auto-tuning exercises.
The document dives into more detail on proportional, integral and derivative control terms. It explains how adding an integral term solves steady-state error and a derivative term solves oscillations. The full PID equation incorporating proportional, integral and derivative terms with parameters is presented.
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.
This document provides an overview of process control concepts including:
1. Process control refers to methods used to control process variables when manufacturing a product. Modeling the process is important for understanding how to control it.
2. The basic elements of a process control loop include a measurement, controller, actuator, and process. Common signals are the process variable and manipulated variable.
3. Common types of controllers are on-off, proportional, integral, derivative, and PID. On-off is simple but ineffective. Proportional reduces offset but has steady state error. Integral eliminates offset but can oscillate. Derivative reduces oscillations. PID combines all three for optimal control.
4. Cascade control uses
This document provides an overview of PID control including:
- A brief history of PID control and its widespread use in process control applications.
- Explanations of proportional, integral and derivative control and how each term works to minimize error in a control loop.
- Details on implementing a PID controller including choosing a structure and tuning parameters.
- An example PID control loop for controlling oil level in a tank.
- Pseudocode for a software-based PID control algorithm.
The document serves as an introduction to PID control theory and implementation for process control applications.
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.
This document discusses elements of process control systems including sensors, controllers, and control elements. It provides definitions of these elements and describes how they relate and interact in a process control loop based on a block diagram approach. The key elements are the process being controlled, sensors that measure process variables, a controller that determines necessary control actions, and control elements that implement adjustments to the process. The document also discusses criteria for evaluating how well a control system is performing including stability, steady-state regulation, and transient response.
Three control loops are described: open loop control which takes action without feedback, closed loop control which measures process variables, compares to setpoints, and adjusts to correct deviations, and proportional control which adjusts the correcting element proportionally to the error. Key aspects of proportional, integral, and derivative control modes are also summarized. Proportional control responds directly to error, integral control eliminates offset through repeated proportional action, and derivative control improves response in slow processes by anticipating needed output changes.
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.
This document discusses PID controllers, which are widely used in industrial control systems. It provides details on:
- The basic components and functions of a PID controller, which uses proportional, integral and derivative terms to continuously calculate and apply error corrections.
- The characteristics and effects of P, I, and D controllers individually and together in a PID controller. While P reduces steady state error, I eliminates it, and D increases stability and reduces overshoot.
- Methods for tuning PID controllers, including Ziegler-Nichols tuning rules which determine parameters based on process response characteristics.
- Implementations of PID controllers using analog electronics with operational amplifiers, and limitations when used without additional modeling or modifications.
Okay, let's solve this step-by-step:
* Set point (Io) = 12 rpm
* Range = 15 - 10 = 5 rpm
* Initial controller output = 22%
* KI = -0.15%/s/% error
* Error = Actual - Set point = ?
* Given: Initial output is 22%
* To find: What is the actual speed?
Using the integral control equation:
Iout = Io - KI * ∫edt
22% = 12rpm - 0.15%/s/% * ∫e dt
∫e dt = (22% - 12rpm)/0.15%/s/% = 40%*
This project aims to design a semi-automatic water meter to address issues with the existing system like improper water fetching and theft. The objectives are to study the existing mechanical water meter, simulate an electronic circuit, and build a prototype. The scope is for homeowners and water sellers. It will prevent water spilling, record daily liter sales, and send data to the owner. The methodology involves literature review, data collection, analysis, circuit design, simulation, and prototype testing.
The document describes a project to reduce inventory discrepancies between an E-book system and SAP system at a factory. It involved defining key metrics, measuring current performance, analyzing causes of discrepancies, designing database functions to compare transactions, and verifying improvements. The project reduced defects per unit by 12.58%, tariff loss by 89.36% (US$47,252), and process cycle time to 3 days, meeting all project goals.
This document discusses various types of motor control, including on-off control and PID control. It begins with an overview of closed-loop control using motor feedback via encoders for velocity and position control. The main focus is on introducing PID control in a step-wise manner, first explaining on-off control and then proportional, integral and derivative controllers. It provides the mathematical formulas for these controller types and discusses implementing them in software and tuning the PID parameters.
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic controller the focus is the human operator's behaviour, whereas in conventional PID controller what is modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this design two input parameters: sensor output voltage and rate of change voltage and one output parameters: opening of the control valve .
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable
thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous
human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic
controller the focus is the human operator's behaviour, whereas in conventional PID controller what is
modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments
applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this
design two input parameters: sensor output voltage and rate of change voltage and one output
parameters: opening of the control valve .
The document provides an overview of advanced process control (APC), including its definition, applications, advantages, and limitations. It discusses how APC builds on basic process control techniques by using process models and optimization to enhance plant operation and profitability. Examples are given of APC applications in petrochemical plants and semiconductor manufacturing. The benefits of APC include improved yield, quality, energy efficiency, and responsiveness. However, APC implementations are also complex, time-consuming, and require specialized expertise and resources.
This document discusses PID (proportional-integral-derivative) controllers. It explains that PID controllers use three terms: proportional to the error, integral of the error, and derivative of the error. The document provides equations for continuous and discrete PID controllers. It also describes Ziegler-Nichols tuning, which is a common method for adjusting the PID parameters (Kp, Ti, Td) based on open-loop step response testing of the plant. Ziegler-Nichols tuning values are given for proportional, PI, and PID controllers to minimize error. An example problem demonstrates identifying plant parameters from step response data and applying Ziegler-Nichols tuning to design proportional, PI, and PID controllers.
control technology of bachlor of engineering technologyengineerfazi245
This document is a lecture on PID controllers that discusses:
- PID controllers are widely used in industrial control systems to regulate variables like temperature, pressure, and level.
- A PID controller calculates the error between a setpoint and measured process variable, and determines the necessary adjustments to the control input based on proportional, integral and derivative terms.
- The lecture provides background on PID controllers and explains the individual proportional, integral and derivative terms and how they work together to provide accurate and stable control.
Ch-4: Measurement systems and basic concepts of measurement methodsSuraj Shukla
This document provides an introduction and overview of measurement systems and concepts. It discusses:
- The basic components of a generalized measurement system, including sensing, conversion, manipulation, processing, transmission and presentation stages.
- Key definitions and concepts in measurement like accuracy, error, calibration, threshold, sensitivity and hysteresis.
- Classification schemes for transducers based on factors like the physical phenomenon, power type, output type and electrical phenomenon.
- Types of transducers like active vs passive, primary vs secondary, analog vs digital, and examples within resistive, capacitive, inductive and other categories.
This document provides an overview of process control tuning and PID control. It discusses the goals of controller tuning which are fast response and good stability, but these cannot be achieved simultaneously. PID control is then introduced which uses proportional, integral and derivative modes to achieve an acceptable compromise between stability and response speed. The key performance aspects of each control mode are explained. For proportional control, steady-state error is its main limitation. Integral control is used to eliminate this error over time by summing all past errors. Examples are also provided to demonstrate offset calculations for different control modes applied to a three-tank mixing process.
The document discusses different types of process controllers and their time responses. It explains that proportional (P), integral (I), derivative (D) and combined PI, PD, and PID controllers each have different effects on how the manipulated variable is calculated from the system deviation over time. It also discusses cascade, feedforward and ratio control systems which complement basic feedback control loops.
Modeling control of automatic voltage regulator with proportional integral de...eSAT Journals
This document summarizes a study that models the control of an Automatic Voltage Regulator (AVR) system using a Proportional Integral Derivative (PID) controller. The study creates a simulation model of an AVR controller using MATLAB/Simulink software. The simulation shows that adding a PID controller to the AVR results in a faster response time when loads change compared to using just the AVR with a proportional controller. With optimized PID constants, the voltage is restored to a stable level much quicker while experiencing less oscillation when loads fluctuate. Therefore, the study concludes that a PID controller is better suited than a proportional controller alone for controlling generator voltage with changing loads.
The document discusses PID control for electronic systems. PID controllers use proportional, integral, and derivative terms to control systems. Proportional control responds to current error, integral control responds to accumulated past error to eliminate offsets, and derivative control responds to the rate of change of error to reduce overshoot from P and I terms. Together in a PID controller, the P, I, and D terms work to quickly reduce error, eliminate offsets, and minimize overshoot for effective system control.
IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...IRJET Journal
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.
IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...IRJET Journal
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.
This document describes a project to create a virtual PID controller using LabVIEW software to control the fluid level in a tank. A data acquisition card interfaces with hardware including a flow meter, control valve, and level transmitter. The LabVIEW-based PID will generate control signals sent to the control valve via the DAQ card to maintain the desired fluid level. Keywords include DAQ card, LabVIEW, and PID.
Research on a Kind of PLC Based Fuzzy-PID Controller with Adjustable FactorNooria Sukmaningtyas
A kind of fuzzy-PID controller with adjustable factor is designed in this paper. Scale factor’s selfadjust
will come true. Fuzzy control algorithm is finished in STEP7 software, and then downloaded in S7-
300 PLC. WinCC software will be used to control the change-trend in real time. Data communication
between S7-300 PLC and WinCC is achieved by MPI. The research shows that this fuzzy-PID controller
has better robust capability and stability. It’s an effective method in controlling complex long time-varying
delay systems.
This document discusses PID controllers, which are widely used in industrial control systems. It provides details on:
- The basic components and functions of a PID controller, which uses proportional, integral and derivative terms to continuously calculate and apply error corrections.
- The characteristics and effects of P, I, and D controllers individually and together in a PID controller. While P reduces steady state error, I eliminates it, and D increases stability and reduces overshoot.
- Methods for tuning PID controllers, including Ziegler-Nichols tuning rules which determine parameters based on process response characteristics.
- Implementations of PID controllers using analog electronics with operational amplifiers, and limitations when used without additional modeling or modifications.
Okay, let's solve this step-by-step:
* Set point (Io) = 12 rpm
* Range = 15 - 10 = 5 rpm
* Initial controller output = 22%
* KI = -0.15%/s/% error
* Error = Actual - Set point = ?
* Given: Initial output is 22%
* To find: What is the actual speed?
Using the integral control equation:
Iout = Io - KI * ∫edt
22% = 12rpm - 0.15%/s/% * ∫e dt
∫e dt = (22% - 12rpm)/0.15%/s/% = 40%*
This project aims to design a semi-automatic water meter to address issues with the existing system like improper water fetching and theft. The objectives are to study the existing mechanical water meter, simulate an electronic circuit, and build a prototype. The scope is for homeowners and water sellers. It will prevent water spilling, record daily liter sales, and send data to the owner. The methodology involves literature review, data collection, analysis, circuit design, simulation, and prototype testing.
The document describes a project to reduce inventory discrepancies between an E-book system and SAP system at a factory. It involved defining key metrics, measuring current performance, analyzing causes of discrepancies, designing database functions to compare transactions, and verifying improvements. The project reduced defects per unit by 12.58%, tariff loss by 89.36% (US$47,252), and process cycle time to 3 days, meeting all project goals.
This document discusses various types of motor control, including on-off control and PID control. It begins with an overview of closed-loop control using motor feedback via encoders for velocity and position control. The main focus is on introducing PID control in a step-wise manner, first explaining on-off control and then proportional, integral and derivative controllers. It provides the mathematical formulas for these controller types and discusses implementing them in software and tuning the PID parameters.
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic controller the focus is the human operator's behaviour, whereas in conventional PID controller what is modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this design two input parameters: sensor output voltage and rate of change voltage and one output parameters: opening of the control valve .
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable
thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous
human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic
controller the focus is the human operator's behaviour, whereas in conventional PID controller what is
modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments
applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this
design two input parameters: sensor output voltage and rate of change voltage and one output
parameters: opening of the control valve .
The document provides an overview of advanced process control (APC), including its definition, applications, advantages, and limitations. It discusses how APC builds on basic process control techniques by using process models and optimization to enhance plant operation and profitability. Examples are given of APC applications in petrochemical plants and semiconductor manufacturing. The benefits of APC include improved yield, quality, energy efficiency, and responsiveness. However, APC implementations are also complex, time-consuming, and require specialized expertise and resources.
This document discusses PID (proportional-integral-derivative) controllers. It explains that PID controllers use three terms: proportional to the error, integral of the error, and derivative of the error. The document provides equations for continuous and discrete PID controllers. It also describes Ziegler-Nichols tuning, which is a common method for adjusting the PID parameters (Kp, Ti, Td) based on open-loop step response testing of the plant. Ziegler-Nichols tuning values are given for proportional, PI, and PID controllers to minimize error. An example problem demonstrates identifying plant parameters from step response data and applying Ziegler-Nichols tuning to design proportional, PI, and PID controllers.
control technology of bachlor of engineering technologyengineerfazi245
This document is a lecture on PID controllers that discusses:
- PID controllers are widely used in industrial control systems to regulate variables like temperature, pressure, and level.
- A PID controller calculates the error between a setpoint and measured process variable, and determines the necessary adjustments to the control input based on proportional, integral and derivative terms.
- The lecture provides background on PID controllers and explains the individual proportional, integral and derivative terms and how they work together to provide accurate and stable control.
Ch-4: Measurement systems and basic concepts of measurement methodsSuraj Shukla
This document provides an introduction and overview of measurement systems and concepts. It discusses:
- The basic components of a generalized measurement system, including sensing, conversion, manipulation, processing, transmission and presentation stages.
- Key definitions and concepts in measurement like accuracy, error, calibration, threshold, sensitivity and hysteresis.
- Classification schemes for transducers based on factors like the physical phenomenon, power type, output type and electrical phenomenon.
- Types of transducers like active vs passive, primary vs secondary, analog vs digital, and examples within resistive, capacitive, inductive and other categories.
This document provides an overview of process control tuning and PID control. It discusses the goals of controller tuning which are fast response and good stability, but these cannot be achieved simultaneously. PID control is then introduced which uses proportional, integral and derivative modes to achieve an acceptable compromise between stability and response speed. The key performance aspects of each control mode are explained. For proportional control, steady-state error is its main limitation. Integral control is used to eliminate this error over time by summing all past errors. Examples are also provided to demonstrate offset calculations for different control modes applied to a three-tank mixing process.
The document discusses different types of process controllers and their time responses. It explains that proportional (P), integral (I), derivative (D) and combined PI, PD, and PID controllers each have different effects on how the manipulated variable is calculated from the system deviation over time. It also discusses cascade, feedforward and ratio control systems which complement basic feedback control loops.
Modeling control of automatic voltage regulator with proportional integral de...eSAT Journals
This document summarizes a study that models the control of an Automatic Voltage Regulator (AVR) system using a Proportional Integral Derivative (PID) controller. The study creates a simulation model of an AVR controller using MATLAB/Simulink software. The simulation shows that adding a PID controller to the AVR results in a faster response time when loads change compared to using just the AVR with a proportional controller. With optimized PID constants, the voltage is restored to a stable level much quicker while experiencing less oscillation when loads fluctuate. Therefore, the study concludes that a PID controller is better suited than a proportional controller alone for controlling generator voltage with changing loads.
The document discusses PID control for electronic systems. PID controllers use proportional, integral, and derivative terms to control systems. Proportional control responds to current error, integral control responds to accumulated past error to eliminate offsets, and derivative control responds to the rate of change of error to reduce overshoot from P and I terms. Together in a PID controller, the P, I, and D terms work to quickly reduce error, eliminate offsets, and minimize overshoot for effective system control.
IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...IRJET Journal
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.
IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...IRJET Journal
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.
This document describes a project to create a virtual PID controller using LabVIEW software to control the fluid level in a tank. A data acquisition card interfaces with hardware including a flow meter, control valve, and level transmitter. The LabVIEW-based PID will generate control signals sent to the control valve via the DAQ card to maintain the desired fluid level. Keywords include DAQ card, LabVIEW, and PID.
Research on a Kind of PLC Based Fuzzy-PID Controller with Adjustable FactorNooria Sukmaningtyas
A kind of fuzzy-PID controller with adjustable factor is designed in this paper. Scale factor’s selfadjust
will come true. Fuzzy control algorithm is finished in STEP7 software, and then downloaded in S7-
300 PLC. WinCC software will be used to control the change-trend in real time. Data communication
between S7-300 PLC and WinCC is achieved by MPI. The research shows that this fuzzy-PID controller
has better robust capability and stability. It’s an effective method in controlling complex long time-varying
delay systems.
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
4. What is PID Control?
• Let’s take a step back… What is control?
• Control is just making a dynamic process
behave in the way we want
• We need 3 things to do this:
• A way to influence the process
• A way to see how the process behaves
• A way to define how we want it to behave
8/8/2023 Document reference 4
5. Defining behaviour
• We usually specify a value we want some
output of the system to have
• Usually called the Setpoint (SP)
• Can be the temperature of a room, the level
in a tank, the flow rate in a pipe…
• The value can be fixed, or may change with
time…
8/8/2023 Document reference 5
6. Influencing the process
• We need some kind of control input which
can create changes in the behavior of the
process
• Can be a heater, a valve, a pump…
• Typically it is not the same physical quantity
as what we are controlling
8/8/2023 Document reference 6
7. Observing behavior
• If we knew exactly how the process worked,
we would know what the output would be for
a given control input…
• Most of the time we don’t know exactly, so
we need to measure what the process does
• Usually called Measured Variable (MV) or
Process Variable (PV)
8/8/2023 Document reference 7
8. Feedback Control
• Now we have a measurement (MV), some
value that we want it to be (SP), and some
way to make changes to the process
(control input)
• We can ‘close the loop’
8/8/2023 Document reference 8
9. The Controller as a System
• Now we can see that any controller can be
thought of as a system that takes a setpoint
and a measured value as inputs, and gives
a control signal as an output
8/8/2023 Document reference 9
Controller
SP
MV
Control
10. The Controller as a System
• The controller needs to convert two signals
of one physical quantity (such as
temperature) into one signal of another
(such as valve position)
8/8/2023 Document reference 10
Controller
SP
MV
Control
11. The Controller as a System
• We know that the process is a dynamic
system:
• Its outputs depend on current inputs as well as
its past state
• For the controller to deal with this, it makes
sense that it should be a dynamic system
too
8/8/2023 Document reference 11
12. The Error Signal
• Very often we can think of the controller
acting on the difference between SP and
MV:
8/8/2023 Document reference 12
Controller
SP
MV
Control
+
-
Error
Σ
13. The Closed Loop
This is the ‘classic’ closed loop block diagram
representation of a control system
8/8/2023 Document reference 13
Controller
SP
MV
+
-
e
Σ Process
u
14. A Dynamic Controller
• We said that since the process is dynamic
(dependent on inputs made at different times),
it makes sense that the controller should be too
• How do we usually think of time?
• ‘Present’
• ‘Past’
• ‘Future’
8/8/2023 Document reference 14
16. The ‘Present’
• This part of the controller is only concerned
with what the error is now
• Let’s take a simple law: let the control signal
be proportional to the error:
𝒖 = 𝒌𝒑 × 𝒆
8/8/2023 Document reference 16
17. Proportional Control
• This is what is referred to as proportional
control. The control action at any instant is
the same as a constant times the error at
the same instant
• The constant 𝑘𝑝 is the Proportional Gain,
and is the first of our controller’s parameters
8/8/2023 Document reference 17
19. Is Proportional Control enough?
• Intuitively it seems like it should be fine on
its own: when the error is big, the control
input is big to correct it. As the error reduced
so does the control input.
• But there are problems…
8/8/2023 Document reference 19
20. Problem 1
Think of a pendulum.
If the setpoint is
hanging straight down,
then gravity acts as a
proportional controller
for the position…
Pendulum will
oscillate!
Document reference 20
8/8/2023
21. Problem 2
• What happens when
the error is zero?
• Control input is zero.
• Causes problems if
we need to have a
nonzero control value
while at our setpoint
8/8/2023 Document reference 21
22. Problem 2: steady state error
This problem is
normally called steady
state error
It’s a confusing name.
The issue is just that
the controller can’t
produce any output
when the error is zero.
Easiest to see for tank
level control: If there is
a constant flow out of
the tank, the controller
must provide the same
flow in, while the level
is at the setpoint.
8/8/2023 Document reference 22
23. Problem 2: steady state error
With P control, once
the error has reached
a value where 𝑘𝑝 × 𝑒 is
equal to the flow out,
the level will stabilize.
But it will be different to
the setpoint
8/8/2023 Document reference 23
24. P control problem summary
• Problem 1:
oscillations
• P control will give us
oscillations in some
processes,
regardless of the
value of the gain
parameter.
• Problem 2: steady
state error
• P control cannot
give us a nonzero
value of the control
at zero error for
some types of
process
8/8/2023 Document reference 24
25. Solving P control’s problems
• How to get rid of steady state error?
• Let’s ignore the present for the moment and
concentrate on what has happened in the
past
Document reference 25
8/8/2023
26. Solving steady state error: ‘the Past’
• Let’s look at the error in the past
8/8/2023 Document reference 26
SP
MV
+
-
e
Σ Process
Future
Past
Proportional
u
27. Solving steady state error
We can examine how
the controller error has
evolved in the past
If we sum up the past
values of the error, we
can get a value that
increases when there
is a constant error
Document reference 27
8/8/2023
28. Integral Action
We can let the control be given by the sum of
past values of the error, scaled by some gain.
In continuous time the sum is an integral:
𝒖 = 𝒌𝒊
𝟎
𝒕
𝒆 𝝉 𝒅𝝉
8/8/2023 Document reference 28
30. Integral gain, Integral time
Here is where confusion can start…
We have an integral gain 𝒌𝒊 which converts an
integrated error to a control signal
We would actually like to parameterize this as a
time, as in how fast we can remove a steady
state error
8/8/2023 Document reference 30
31. Integral gain, Integral time
Let’s rewrite:
𝒖 = 𝒌𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉
As:
is our Proportional Gain, and 𝑻𝒊 is our Integral
Time
8/8/2023 Document reference 31
32. The Integral time
Why do we use both 𝒌𝒑 and 𝑻𝒊 here?
𝒖 =
𝒌𝒑
𝑻𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉
Let’s add the proportional and integral parts:
𝒖 = 𝒌𝒑𝒆 𝒕 +
𝒌𝒑
𝑻𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉
8/8/2023 Document reference 32
33. Proportional and Integral Controller
8/8/2023 Document reference 33
SP
MV
+
-
e
Σ Process
Future
Integral
Proportional
u
𝒖 = 𝒌𝒑𝒆 𝒕 +
𝒌𝒑
𝑻𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉
34. Proportional and Integral Controller = PI Controller
We can rewrite the control law:
𝒖 = 𝒌𝒑 𝒆 𝒕 +
𝟏
𝑻𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉
𝒌𝒑 is the gain parameter, 𝑻𝒊 is the time it takes
to fix a steady state error
8/8/2023 Document reference 34
35. Solving P control’s problems revisited
• We solved the steady state error by adding
integral action (summing the past)
• How can we solve the oscillation problem?
• Let’s look at the future!
Document reference 35
8/8/2023
36. Solving oscillations: ‘the Future’
• Let’s look at the error in the future
8/8/2023 Document reference 36
SP
MV
+
-
e
Σ Process
Future
Integral
Proportional
u
37. Solving oscillations: ‘the Future’
How do we predict the
future of the error?
Look at its gradient!
If the gradient (the time
derivative) of the error is
in a direction that makes
the error smaller, we can
reduce the control input
Document reference 37
8/8/2023
38. Damping
It can be easier to think
of this as damping,
something that resists
velocity
Think of the wheel on
your car…
8/8/2023 Document reference 38
39. Damping
The spring is a
proportional controller
for the wheel position.
The damper adds a
derivative action by
opposing the velocity
of the wheel
8/8/2023 Document reference 39
40. Derivative action
Let’s let the control be dependent on the
derivative of the error:
𝒖 = 𝒌𝒅
𝒅𝒆(𝒕)
𝒅𝒕
Here 𝑘𝑑 is the derivative gain. Let’s again split
this into 𝑘𝑝𝑇𝑑, where 𝑇𝑑 is the derivative time
Document reference 40
8/8/2023
42. PID Controller!
8/8/2023 Document reference 42
SP
MV
+
-
e
Σ Process
Derivative
Integral
Proportional
u
𝒖 = 𝒌𝒑𝒆 𝒕 +
𝒌𝒑
𝑻𝒊 𝟎
𝒕
𝒆 𝝉 𝒅𝝉 + 𝒌𝒑𝑻𝒅
𝒅𝒆(𝒕)
𝒅𝒕
43. Derivative Time
Why do we want 𝑇𝑑 as a parameter?
We can think of it as how far ahead we want to
predict!
Easier to relate to process
8/8/2023 Document reference 43
44. Problems with Derivative action
We know that a
derivative amplifies
quick changes.
We can get problems if
MV is noisy.
Solution is to add low
pass filter
Document reference 44
8/8/2023
45. Derivative with filter
We already have:
𝒖 = 𝒌𝒑𝑻𝒅
𝒅𝒆(𝒕)
𝒅𝒕
Equations will get messy if we add a filter in
time domain! Let’s use Laplace! Then we can
use algebra instead of calculus.
Document reference 45
8/8/2023
46. Derivative with filter
Laplace transform frequency variable s is also
an operator. Multiplication by s is derivation,
and division is integration. So the derivative
part is now
𝑈 𝑠 = 𝑘𝑝𝑇𝑑𝑠𝐸(𝑠)
8/8/2023 Document reference 46
47. Derivative with filter
We add a low pass filter:
𝑈 𝑠 =
𝑘𝑝𝑇𝑑𝑠
1 +
𝑇𝑑
𝑇𝑑𝑠
𝑠
𝐸(𝑠)
Now we have another parameter 𝑇𝑑𝑠which is
the filter bandwidth. So introducing derivative
action requires two more parameters!
8/8/2023 Document reference 47
49. Full PID equation
This simplifies to:
𝑈 𝑠 = 𝑘𝑝 1 +
1
𝑇𝑖𝑠
+
𝑇𝑑𝑠
1+
𝑇𝑑
𝑇𝑑𝑠
𝑠
𝐸(𝑠)
8/8/2023 Document reference 49
50. ISA PID Form
This is the ISA ‘standard form’ for a PID
𝑈 𝑠
𝐸(𝑠)
= 𝑘𝑝 1 +
1
𝑇𝑖𝑠
+
𝑇𝑑𝑠
1 +
𝑇𝑑
𝑇𝑑𝑠
𝑠
We have one gain, and three time constants
8/8/2023 Document reference 50
51. The UNICOS PID Controller
8/8/2023 Document reference 51
Features and quirks
52. UNICOS PID Features
• Uses the ISA standard equation we just derived
• Parameterized by 𝑘𝑝, 𝑇𝑖, 𝑇𝑑, 𝑇𝑑𝑠
• Includes limits and ramps on setpoint and
output
• Can use scaling on input, or both input and
output
8/8/2023 Document reference 52
53. UNICOS PID Modes
8/8/2023 Document reference 53
Point to note:
Manual Mode is not open loop!
Use Output positioning to set
open loop control output
55. UNICOS PID Scaling
• The scaling features are a little tricky, and
have important consequences when tuning
the controller!
• Need to be very careful when applying
tuning results! (More on this later!)
8/8/2023 Document reference 55
56. Scaling types
• There are three types of scaling
• No scaling
• Input scaling
• Input and output (Full) scaling
8/8/2023 Document reference 56
57. No Scaling
• This is the simplest. The inputs and outputs
are applied directly to the PID equation, so it
operates on engineering values. For
example the MV and SP may be
temperatures, and the output a power
command to a heater
8/8/2023 Document reference 57
58. Input Scaling
• Here the input is scaled from engineering
values to a normalized value in the range 0-
100%. Scaling parameters must be
provided. For example 0 bar and 10 bar
could be the low and high limits, which are
then mapped to 0% and 100% as seen by
the PID algorithm.
8/8/2023 Document reference 58
59. Full Scaling (Input and Output)
• The input is converted to a 0-100% signal as
before, and now the output of the controller
is converted from 0-100% to engineering
values before being applied to the process
8/8/2023 Document reference 59
60. Scaling Pitfalls
• Note that the values of SP, MV and
controller output you see in the faceplate are
NOT the same as the PID equation ‘sees’ if
you use scaling!
• If you change the scaling type you MUST
scale your controller parameters (𝑘𝑝)!
8/8/2023 Document reference 60
62. P, PI, PD, PID?
For the complete PID controller we have 4
parameters 𝑘𝑝, 𝑇𝑖, 𝑇𝑑, 𝑇𝑑𝑠
But we can also choose to use only parts of the
controller, for example just, PI, giving 2
parameters to choose.
How do we know when to use what?
8/8/2023 Document reference 62
63. The Process Model
We now know exactly what’s inside our
controller, but so far we haven’t said anything
about what’s inside our process!
To be able to tune the controller, we need to
know something about it.
8/8/2023 Document reference 63
64. Open Loop Step Response
• A good starting point is to make an open
loop step test on the process
• Note that this is a step on the controller
output, not on the setpoint!
8/8/2023 Document reference 64
65. Open Loop Step Response
If our step response
looks something like
this, we have a stable
system with a first
order response
Document reference 65
8/8/2023
66. First Order System
We can get 3 pieces of
information about the
process:
• Process Gain 𝐾
• Time Constant 𝑇
• Time Delay 𝜏
8/8/2023 Document reference 66
67. Process Gain
This is the ratio of the
change in MV to the
change in control input
𝐾 =
∆𝑌
∆𝑈
In this case 𝐾 is (20-
10)/(5-0) = 2
8/8/2023 Document reference 67
68. Time Constant
This is the ‘speed’ of
the process.
To read it from the
trend, look for the time
it takes for the MV to
rise to 63% of its final
value
8/8/2023 Document reference 68
69. Time Constant
Ok, why 63%??
Because 1 − 𝑒−1 is
about 0.63….
The step response is
1 − 𝑒−
𝑡
𝑇 so after time 𝑇
this is 1 − 𝑒−1
8/8/2023 Document reference 69
70. Time Delay
This is simpler, it is just
the time it takes from
the start of the step
until the MV starts to
move.
8/8/2023 Document reference 70
71. First order process model
We have 3 parameters that determine behavior
• 𝐾 determines how big the output change will be
• 𝑇 determines how long the process takes to get
there
• 𝜏 determines how long it takes before the
process starts doing anything at all
Document reference 71
8/8/2023
72. Implications for control
The key feature is the relation between 𝑇and 𝜏
• If 𝜏 is small and 𝑇 is big, control using PI is fine
• If 𝜏 is similar in size to 𝑇 , we may need a more
complex controller (PID)
• If 𝜏 is big relative to 𝑇 , PID will struggle; it will
integrate during the delay and then see a large
jump!
8/8/2023 Document reference 72
73. Mini Exercise: PIDSim
• Play with the
process model
• Try open loop steps
• What happens with
large delay times?
• What happens
when adding an
integrator?
Document reference 73
8/8/2023
74. Tuning for a first order process
We have a process that looks like
𝐺 𝑠 =
𝐾
𝑇𝑠 + 1
𝑒−𝜏𝑠
And we know what 𝐾, 𝑇 and 𝜏 are from our
step test. Let’s say that we want our controlled
system to behave like this, but with a time
constant 𝑻𝒄𝒍
Document reference 74
8/8/2023
75. Tuning for a first order process
Now we know what our closed loop should look
like, and we also know what the process looks
like, and what the controller looks like. We ca use
the closed loop relation:
𝐺𝑐𝑙 𝑠 =
𝐶 𝑠 𝐺(𝑠)
1 + 𝐶 𝑠 𝐺(𝑠)
Where 𝐶 𝑠 is our controller.
8/8/2023 Document reference 75
76. Tuning for a first order process
𝐺𝑐𝑙 𝑠 =
𝐶 𝑠 𝐺(𝑠)
1 + 𝐶 𝑠 𝐺(𝑠)
From here we can work out what controller
parameters we need!
This is called Internal Model Control (IMC)
8/8/2023 Document reference 76
77. Internal Model Control (IMC)
Once we do the math, we get these
parameters for PI:
𝑲𝒑 =
𝟏
𝑲
𝑻
(𝑻𝒄 + 𝝉)
𝑻𝒊 = 𝐦𝐢𝐧 𝑻, 𝟒 𝑻𝑪 + 𝝉
(this is Skogestad’s IMC, S-IMC)
8/8/2023 Document reference 77
78. Internal Model Control (IMC)
IMC rules are nice, because they are not just
heuristics: they are calculated from a desired
behavior.
8/8/2023 Document reference 78
79. Integrating processes
• What happens
when the step
response is a
ramp?
• Process contains an
integrator!
• 𝐾 =
𝑑𝑌
𝑑𝑡
∆𝑈
Document reference 79
8/8/2023
80. SIMC tuning rules
The rules can be
derived for a number of
different process
models
8/8/2023 Document reference 80
http://folk.ntnu.no/skoge/publications/2012/skogestad-improved-simc-pid/PIDbook-chapter5.pdf
81. SIMC for UNICOS PID
Point to note: SIMC rules apply for a ‘cascade’ PID
structure, not the ISA standard used by UNICOS.
For PI and PD they are the same, but for the full
PID we need to do a transformation of the
parameters:
𝐾𝑝 = 𝐾𝑝
𝑐
(1 +
𝜏𝑑
𝑐
𝜏𝑖
𝑐), 𝜏𝑖 = 𝜏𝑖
𝑐
(1 +
𝜏𝑑
𝑐
𝜏𝑖
𝑐), 𝜏𝑑 =
𝜏𝑑
𝑐
1+
𝜏𝑑
𝑐
𝜏𝑖
𝑐
Document reference 81
8/8/2023
82. Other tuning rules
• There are MANY other sets of tuning rules!
8/8/2023 Document reference 82
83. Ziegler-Nichols
• You may have come across Ziegler-Nichols
• Early work (1942)
• Actually developed for fast setpoint tracking, so
they are really too aggressive for process
control!
• We will revisit them later!
8/8/2023 Document reference 83
84. Tuning rule warning
Lots of tuning rules are based on heuristics,
which might only fit certain process types. In
addition, you need to be sure what controller
structure they are for. You also need to know
what the control aim is (setpoint following,
disturbance rejection)
8/8/2023 Document reference 84
85. Recommendation: SIMC
• SIMC is good because:
• It forces you to do a step test and learn about
the process
• You can adjust the rules with an intuitive
parameter
• They are easily derived and therefore
understood
8/8/2023 Document reference 85
86. Recommendation: SIMC
Recommendation is to start with SIMC.
If you’re still not happy, you can use it as a
baseline, and make manual adjustments from
there.
Let’s now make a tuning ‘recipe’!
8/8/2023 Document reference 86
87. Tuning ‘recipe’
• Step 1: Preparation:
• Make sure it’s ok to put the process into open
loop
• Make sure the process is settled (steady state)
• Make sure you are close to your normal
operating point
8/8/2023 Document reference 87
88. Tuning ‘recipe’
• Step 2: Open loop step test
• Put the controller into Output Positioning
• Make a small step in the output value
• Observe the MV, make sure it doesn’t hit the limits!
• Once the MV has settled, make a step back to
where you started.
• Put the controller back in regulation
8/8/2023 Document reference 88
89. Tuning ‘recipe’
• Step 3: Calculate process parameters
• Find 𝐾, 𝑇 and 𝜏
• 𝐾 =
∆𝑌
∆𝑈
(watch out for scaling!)
• 𝑇 is the time taken to rise to 0.63 ∆𝑌
• 𝜏 is the time between your step input and the first
change in MV
8/8/2023 Document reference 89
91. Tuning ‘recipe’
• Step 5: Set parameters and test
• Apply the new parameters in the controller
• Make a small set point change and see how the
controller reacts
• If too aggressive, increase 𝑇𝑐𝑙 and recalculate
parameters
• If too slow, decrease 𝑇𝑐𝑙 and recalculate parameters
8/8/2023 Document reference 91
95. Autotuning
• What is autotuning?
• It is NOT a system which continuously updates
the controller parameters (this is adaptive
control)
• It is just a set of tools to automate the procedure
of finding a set of parameters
8/8/2023 Document reference 95
96. Open vs Closed Loop
• In our manual tuning we did an open loop
experiment (step test) and found parameters
from there. This procedure can be
automated
• There are also methods that allow us to
keep the loop closed
8/8/2023 Document reference 96
97. Automated SIMC Tuning
• Let’s revisit the SIMC method
• Doing the calculations and keeping track of
the scaling was a pain
• The UNICOS autotuner automates most of
this for us
8/8/2023 Document reference 97
98. UNICOS SIMC Autotune
• Open the controller
faceplate and go to
Autotune
• Select SIMC (1)
• First, we need to
identify the process
parameters (2)
Document reference 98
8/8/2023
1
2
3
4
99. UNICOS SIMC Autotune
• We enter a step
size, and it performs
the step test,
stopping when MV
has settled.
8/8/2023 Document reference 99
100. UNICOS SIMC Autotune
• Now we select the
closed loop response
time 𝑇𝑐𝑙 (3)
• This gives us the
proposed parameters
(4)
• We can apply them to
the controller (unless
set by APAR; these
must be set manually)
Document reference 100
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1
2
3
4
101. Closed Loop Autotuning
• What if we want to remain in closed loop
when tuning?
• We can use the Relay Method
• Effectively exchanges the PID for an on/off
control
Document reference 101
8/8/2023
102. Relay Method
• Control only has 2
values, depending
on whether MV is
higher or lower than
SP
• Gives a square
wave oscillation
Document reference 102
8/8/2023
103. Relay Method: detail
• Control goes
between +𝑑 and
− 𝑑 from its original
value, with some
hysteresis
Document reference 103
8/8/2023
104. Relay Method: detail
• Output oscillates
with an amplitude 𝑎
• We get two pieces
of information from
this: Ultimate gain
𝐾𝑢 and Ultimate
Period 𝑃𝑢
Document reference 104
8/8/2023
105. Relay Method: calculation
• Ultimate gain 𝐾𝑢
comes from the
amplitude of the
fundamental
frequency of the
square wave
𝐾𝑢 =
4𝑑
𝜋𝑎
• Ultimate Period 𝑃𝑢
is just the period of
the resulting
oscillation
8/8/2023 Document reference 105
106. Relay Method: tuning
Again we have MANY rules to choose from.
Ziegler-Nichols are the most well known
Document reference 106
8/8/2023
Time delay and time constant similar
Integrator and time delay system
Time delay dominated system
107. UNICOS Relay Autotuner
The current version uses the Ziegler-Nichols
rules
• You may find the result too ‘aggressive’
• You can always ‘detune’ by reducing 𝐾𝑝 and
increasing 𝑻𝒊 by small amounts
8/8/2023 Document reference 107
108. UNICOS Relay Autotuner
• Select method
RELAY and controller
type (1)
• We need to give an
amplitude (2). Before
starting, we should be
in steady state. Then
we can input Max and
Min values (ideally
symmetric!)
Document reference 108
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2
1
3
4
109. UNICOS Relay Autotuner
• We also need to
give the number of
cycles (2). 3 to 5 is
usually enough.
• Start the relay
experiment (3)
Document reference 109
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1
3
4
110. UNICOS Relay Autotuner
• Popup opens.
Observe that we get
a nice square wave
with fairly constant
period.
• After the given
number of cycles, it
will stop automatically
8/8/2023 Document reference 110
111. UNICOS Relay Autotuner
• Ziegler-Nichols tuning
rules are applied
internally. There is no
user parameter
• Results are displayed
(4) and we can
choose to apply the
new parameters
Document reference 111
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2
1
3
4
112. UNICOS Relay Autotuner
Practical notes:
• The experiment begins by checking for
steady state. It may take some time before
the relay cycles start
• If the experiment starts far from steady
state, it can cause problems
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113. Iterative Feedback Tuning
• IFT tries to adjust
the control
parameters in order
to minimize a cost
function
• We won’t look at it
in detail here
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2
3
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114. Exercise: Autotuning in
UNICOS
• Let’s try out the SIMC
and Relay methods
• Compare the results
to your results from
manual tuning
• Can you improve the
autotuning results?
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