This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
Model Predictive Control Implementation with LabVIEWyurongwang1
This presentation was presented at National Instruments NIWeek 2007 to demonstrate how to use LabVIEW to implement model predictive control (MPC) strategies to control complicated coax manufacturing processes. Both MatLAB MPC and LabVIEW MPC were implemented in these applications.
This document discusses using System Functions (S-functions) in Simulink as an effective tool for implementing customized algorithms. S-functions allow incorporating custom MATLAB, C, or Fortran code into Simulink models. The document presents results of implementing a voltage disturbance detection algorithm both with an S-function and a MATLAB code function. The S-function implementation was found to provide more accurate results and faster evaluation compared to the MATLAB code function approach. Graphs of the root mean square voltage values showed less variation and faster updating using the S-function versus the MATLAB code function implementation.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
Systematic Model based Testing with Coverage AnalysisIDES Editor
Aviation safety has come a long way in over one
hundred years of implementation. In aeronautics, commonly,
requirements are Simulink Models. Considering this, many
conventional low level testing methods are adapted by various
test engineers. This paper is to propose a method to undertake
Low Level testing/ debugging in comparatively easier and
faster way. As a first step, an attempt is made to simulate
developed safety critical control blocks within a specified
simulation time. For this, the blocks developed will be utilized
to test in Simulink environment. What we propose here is
Processor in loop test method using RTDX. The idea is to
simulate model (requirement) in parallel with handwritten
code (not a generated one) running on a specified target,
subjected to same inputs (test cases). Comparing the results
of model and target, fidelity can be assured. This paper suggests
a development workflow starting with a model created in
Simulink and proceeding through generating verified and
profiled code for the processor.
This document describes the modeling and control of a helicopter (CE 150) system connected to a computer. It includes:
1) An overview of the helicopter hardware, software environments, and its two degrees of freedom (elevation and azimuth).
2) The development of nonlinear and linear mathematical models from balancing forces and moments. System parameters are identified.
3) Details on the hardware (I/O cards) and software used to control the helicopter from a computer in real-time, including MATLAB and Simulink.
4) The design of PID and state feedback controllers using pole placement to control the helicopter dynamics.
Sumit Dhalia has over 1.5 years of experience as a quality assurance engineer for Reliance Jio Infocomm Ltd. He has expertise in testing LTE networks and protocols including call setup, end-to-end system testing, and over-the-air testing. His skills include Linux, Windows, testing tools like IXIA and Wireshark, and wireless technologies like LTE. He is currently the deputy engineer at Reliance Jio and has led projects testing Internet of Things and LTE networks, including protocol testing, throughput testing, and self-optimization techniques.
This document discusses the modeling and realization of a miniaturized meta-material patch antenna. The student aims to design an antenna inspired by meta-materials to improve on previous designs. Objectives include understanding antenna and meta-material properties, mastering simulation software, and studying how specific dimensions impact performance. The student's design is simulated and tested, showing improved efficiency over 9% compared to typical antennas. Further study analyzes how changing the location of serpentines and gap between slots impacts the surface current and radiation.
This document describes the features and capabilities of HYPERSIM, a real-time simulator from OPAL-RT, for protective relay testing. HYPERSIM allows testing relays in a closed-loop with detailed EMT models, automation of complex test sequences, analysis of relay performance under various faults and interactions, and generation of detailed reports. It supports modeling of power systems, communication protocols like IEC 61850, and integration with other equipment. TestView is used to automate testing, analyze results, and manage test data in databases for archiving. ScopeView and additional analysis tools allow detailed evaluation of relay behavior under different conditions.
Model Predictive Control Implementation with LabVIEWyurongwang1
This presentation was presented at National Instruments NIWeek 2007 to demonstrate how to use LabVIEW to implement model predictive control (MPC) strategies to control complicated coax manufacturing processes. Both MatLAB MPC and LabVIEW MPC were implemented in these applications.
This document discusses using System Functions (S-functions) in Simulink as an effective tool for implementing customized algorithms. S-functions allow incorporating custom MATLAB, C, or Fortran code into Simulink models. The document presents results of implementing a voltage disturbance detection algorithm both with an S-function and a MATLAB code function. The S-function implementation was found to provide more accurate results and faster evaluation compared to the MATLAB code function approach. Graphs of the root mean square voltage values showed less variation and faster updating using the S-function versus the MATLAB code function implementation.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
Systematic Model based Testing with Coverage AnalysisIDES Editor
Aviation safety has come a long way in over one
hundred years of implementation. In aeronautics, commonly,
requirements are Simulink Models. Considering this, many
conventional low level testing methods are adapted by various
test engineers. This paper is to propose a method to undertake
Low Level testing/ debugging in comparatively easier and
faster way. As a first step, an attempt is made to simulate
developed safety critical control blocks within a specified
simulation time. For this, the blocks developed will be utilized
to test in Simulink environment. What we propose here is
Processor in loop test method using RTDX. The idea is to
simulate model (requirement) in parallel with handwritten
code (not a generated one) running on a specified target,
subjected to same inputs (test cases). Comparing the results
of model and target, fidelity can be assured. This paper suggests
a development workflow starting with a model created in
Simulink and proceeding through generating verified and
profiled code for the processor.
This document describes the modeling and control of a helicopter (CE 150) system connected to a computer. It includes:
1) An overview of the helicopter hardware, software environments, and its two degrees of freedom (elevation and azimuth).
2) The development of nonlinear and linear mathematical models from balancing forces and moments. System parameters are identified.
3) Details on the hardware (I/O cards) and software used to control the helicopter from a computer in real-time, including MATLAB and Simulink.
4) The design of PID and state feedback controllers using pole placement to control the helicopter dynamics.
Sumit Dhalia has over 1.5 years of experience as a quality assurance engineer for Reliance Jio Infocomm Ltd. He has expertise in testing LTE networks and protocols including call setup, end-to-end system testing, and over-the-air testing. His skills include Linux, Windows, testing tools like IXIA and Wireshark, and wireless technologies like LTE. He is currently the deputy engineer at Reliance Jio and has led projects testing Internet of Things and LTE networks, including protocol testing, throughput testing, and self-optimization techniques.
This document discusses the modeling and realization of a miniaturized meta-material patch antenna. The student aims to design an antenna inspired by meta-materials to improve on previous designs. Objectives include understanding antenna and meta-material properties, mastering simulation software, and studying how specific dimensions impact performance. The student's design is simulated and tested, showing improved efficiency over 9% compared to typical antennas. Further study analyzes how changing the location of serpentines and gap between slots impacts the surface current and radiation.
This document describes the features and capabilities of HYPERSIM, a real-time simulator from OPAL-RT, for protective relay testing. HYPERSIM allows testing relays in a closed-loop with detailed EMT models, automation of complex test sequences, analysis of relay performance under various faults and interactions, and generation of detailed reports. It supports modeling of power systems, communication protocols like IEC 61850, and integration with other equipment. TestView is used to automate testing, analyze results, and manage test data in databases for archiving. ScopeView and additional analysis tools allow detailed evaluation of relay behavior under different conditions.
Ecet 380 Enthusiastic Study / snaptutorial.comStephenson60
Antenna Design
Key Conclusions (technical):
Key Conclusions (critical thinking):
I.OBJECTIVES
Introduction to the most commonly used antenna types and significant design parameters
Design an antenna suitable for a 2G, 3G or 4G wireless application deployment.
In considering the techniques that may be used for digital circuit testing, two distinct philosophies may be found, First is Functional Testing, which undertake a series of functional tests and check for the correct (fault free) 0 or 1 output response. It does not consider how the circuit is designed, but only that it gives the correct output during test and second one is Fault Modelling in whichto consider the possible Faults that may occur within the circuit, and then to apply a series of tests which are specifically formulated to check whether each of these faults is present or not.The faults which are likely to occur on the wafer during the manufacture of the ICs, and compute the result on the circuit output(s) with or without each fault present. Each of the final series of tests is then designed to show that a particular fault is present or not.
Building impedance matching network based on s parameter from manufacturerJournal Papers
The document describes a method for building an impedance matching network for a transistor using S-parameter data and optimization tools in ADS simulation software. The transistor of interest operates from 220-270 MHz, where reference designs are unavailable. Input and output impedances are extracted from S-parameter files. An existing matching network design at a nearby frequency is used as a starting point. Optimization replaces ideal components with real ones, achieving over 30 dB return loss while degrading slightly from the ideal simulation. The method demonstrates synthesizing a matching network when reference designs are unavailable.
Enhanced Built-In Self Test for MSP 430IJTET Journal
The enhanced Built in self test (BIST) is a combined form of both hardware and software together to resolve the memory problem in self testing. So it automatically comprises own test using self test pattern generation. The implementation can be done using the microcontroller MSP 430 series. Here we are using the Xilinx software for compilation in the design implementation. Also its functional can be done using dynamic RAM and reduces the external testing methods also includes diagnosis of test.
Karner resource estimation for objectory projectsOcho08
The document proposes a model for estimating resources needed to develop software using the Objectory process. The model is based on function points, which counts types of inputs, outputs, inquiries etc to determine the size of the system. It then adjusts for technical complexity factors and new environmental factors. It presents this Use Case Points model and shows how to calculate unadjusted use case points, technical complexity factor, and environmental factor. Finally, it validates the model using data from three projects. In summary, the model provides an early way to estimate resources for Objectory projects based on use case analysis and adjustment factors.
Overview of signal integrity simulation for sfp+ interface serial links with ...Conference Papers
The document discusses signal integrity simulation for high-speed SFP+ serial links. It begins with an overview of the challenges of high-speed transmission and the need for simulation. It then covers:
1) The IBIS-AMI model structure and configuration options for simulating transmitters and receivers.
2) The simulation setup created in ADS to optimize equalization settings for a specific SFP+ interface design.
3) The results of simulations at 10.3125Gbps and 16Gbps, showing larger eye openings at the lower data rate.
4) Additional transmitter simulation results, indicating better eye openings when optimizing de-emphasis levels and voltage amplitudes.
Relay Testing with HYPERSIM allows for more advanced testing of relays using a simulator rather than traditional open-loop testing. A simulator provides closed-loop testing where the relay response feeds back into the simulation, allowing testing under critical conditions. It offers benefits like evaluating new protection algorithms, studying complex protection schemes, and analyzing interactions between real and virtual relays. Key features include detailed EMT analysis, automation of test sequences, and the ability to simulate faults, disturbances, and communications protocols like IEC 618450.
This document discusses the use of neural networks in robotics. It outlines pros and cons, common neural network types used, and applications. The pros include the ability to model nonlinear systems, learn functions from data, perform parallel processing, and handle multiple inputs/outputs. Cons include getting stuck in local minimums and potential lack of accuracy. Common neural network types in robotics are backpropagation, Kohonen, and Hopfield networks. Applications include solving kinematics/dynamics problems, trajectory planning, computer vision/sensing, and control systems.
FPGA Optimized Fuzzy Controller Design for Magnetic Ball Levitation using Gen...IDES Editor
This paper presents an optimum approach for
designing of fuzzy controller for nonlinear system using
FPGA technology with Genetic Algorithms (GA) optimization
tool. A magnetic levitation system is considered as a case study
and the fuzzy controller is designed to keep a magnetic object
suspended in the air counteracting the weight of the object.
Fuzzy controller will be implemented using FPGA chip.
Genetic Algorithm (GA) is used in this paper as optimization
method that optimizes the membership, output gain and inputs
gains of the fuzzy controllers. The design will use a highlevel
programming language HDL for implementing the fuzzy
logic controller using the Xfuzzy tools to implement the fuzzy
logic controller into HDL code. This paper, advocates a novel
approach to implement the fuzzy logic controller for magnetic
ball levitation system by using FPGA with GA.
Towards Functional Safety compliance of Matrix-Matrix MultiplicationJavier Fernández Muñoz
The document discusses ensuring functional safety for machine learning-based autonomous systems. It proposes using checksum algorithms to detect errors in matrix-matrix multiplication, a key computation. The solution was evaluated on sequential and AVX-based multiplication, finding that checksums can achieve 100% diagnostic coverage with minimal performance impact depending on matrix size. Future work includes evaluating multiple bit errors and accelerators.
The document discusses using process control monitor (PCM) data from wafer fabrication to predict device performance and wafer yield. PCM data from various sites on the wafer are collected during fabrication and correlated with performance data from devices near those sites. A predictive model is created using the PCM data as inputs to predict device parameters and yield as outputs. The model allows early prediction of wafer and device quality before full testing. Neural networks and linear models were tested, with neural networks showing slightly better prediction accuracy. The model was deployed using a database and scripting to efficiently predict performance for new wafers based on their PCM data.
Presentation for Research Presentation Day 2009amaitos
This document discusses a research project investigating integrating dynamic thermal simulation with building energy management systems (BEMS) for improved energy efficiency. The researchers will set up experiments linking sensors and actuators to a building simulation model to test control strategies in real-time, like optimizing start/stop times. Initial results show using simulation to determine the optimal start time to reach a desired indoor temperature. Future work includes optimizing control algorithms and testing on actual buildings to use simulation as the "brains" of BEMS throughout a building's lifespan.
Network simulation software allows users to model complex systems in detail and analyze proposed network systems before implementing them. Some popular open source simulators include NS2, NS3, OMNeT++, and J-Sim, while commercial options include OPNET, QualNet, and 20-Sim. These simulators vary in features such as supported programming languages, licensing, ease of use, and supported platforms. Simulation provides advantages like studying existing systems without disruption and gaining insights without implementing systems. However, it also requires time and training to properly use and interpret results.
James Ransley is a physicist with experience in microelectromechanical systems and acoustic inkjet technology. He has a track record of innovation through analytical and numerical modeling of complex systems. He holds a PhD in materials science from Cambridge University and has worked in both academia and industry. He is looking for a new role where he can continue applying physics principles.
This document is a laboratory manual for digital signal processing experiments using MATLAB and a DSP chip. It contains an introduction to MATLAB and lists 16 experiments using MATLAB and 11 experiments using a DSP chip. The experiments cover topics like DFT, convolution, filtering, sampling, and impulse responses. The manual was prepared by faculty of the electronics and communication engineering department to help students learn and apply DSP concepts.
A Survey on Wireless Network SimulatorsjournalBEEI
The Network simulator helps the developer to create and simulate new models on an arbitrary network by specifying both the behavior of the network nodes and the communication channels. It provides a virtual environment for an assortment of desirable features such as modeling a network based on a specific criteria and analyzing its performance under different scenarios. This saves cost and time required for testing the functionality and the execution of network. This paper has surveyed various Wireless Network Simulators and compared them.
Model Predictive Control For Integrating ProcessesEmerson Exchange
The document discusses controlling integrating processes like liquid levels using model predictive control (MPC). It explains that integrating processes require control since they have no natural equilibrium. The key points covered are:
- MPC can effectively control integrating processes by considering feedback, model correction, rotation factors, and tuning parameters.
- Tuning time to steady state, penalty on move, and model correction factor impact control performance for setpoint changes and load disturbances.
- With proper tuning, MPC provides improved control of integrating processes compared to conventional PI control.
Robust model predictive control for discrete-time fractional-order systemsPantelis Sopasakis
In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete-
time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties
4 modeling and control of distillation column in a petroleum processnazir1988
This document describes the modeling and simulation of a condensate distillation column in a petroleum process. It presents a calculation procedure to model the column based on an energy balance structure using reflux rate and boilup rate as inputs to control distillate purity and bottom product impurity. A nonlinear dynamic model of the column is developed and simulated in MATLAB. The simulation shows the column can maintain product quality under normal operations but quality decreases with disturbances like changes in feed rate. A reduced-order linear model is then developed for use in model-reference adaptive control to improve disturbance rejection.
The document provides an overview of model predictive control (MPC), including its advantages, concept, terminology, applications, prediction models, state space models, optimization windows, closed-loop control systems, constraints, and numerical solutions. MPC has advantages like intuitive concepts, easy tuning, handling multivariable processes, and treating constraints simply. It requires a process model and derivation of the control law is more complex than PID. MPC uses prediction models within an optimization window to minimize a cost function while satisfying constraints. Numerical solutions involve techniques like quadratic programming.
Ecet 380 Enthusiastic Study / snaptutorial.comStephenson60
Antenna Design
Key Conclusions (technical):
Key Conclusions (critical thinking):
I.OBJECTIVES
Introduction to the most commonly used antenna types and significant design parameters
Design an antenna suitable for a 2G, 3G or 4G wireless application deployment.
In considering the techniques that may be used for digital circuit testing, two distinct philosophies may be found, First is Functional Testing, which undertake a series of functional tests and check for the correct (fault free) 0 or 1 output response. It does not consider how the circuit is designed, but only that it gives the correct output during test and second one is Fault Modelling in whichto consider the possible Faults that may occur within the circuit, and then to apply a series of tests which are specifically formulated to check whether each of these faults is present or not.The faults which are likely to occur on the wafer during the manufacture of the ICs, and compute the result on the circuit output(s) with or without each fault present. Each of the final series of tests is then designed to show that a particular fault is present or not.
Building impedance matching network based on s parameter from manufacturerJournal Papers
The document describes a method for building an impedance matching network for a transistor using S-parameter data and optimization tools in ADS simulation software. The transistor of interest operates from 220-270 MHz, where reference designs are unavailable. Input and output impedances are extracted from S-parameter files. An existing matching network design at a nearby frequency is used as a starting point. Optimization replaces ideal components with real ones, achieving over 30 dB return loss while degrading slightly from the ideal simulation. The method demonstrates synthesizing a matching network when reference designs are unavailable.
Enhanced Built-In Self Test for MSP 430IJTET Journal
The enhanced Built in self test (BIST) is a combined form of both hardware and software together to resolve the memory problem in self testing. So it automatically comprises own test using self test pattern generation. The implementation can be done using the microcontroller MSP 430 series. Here we are using the Xilinx software for compilation in the design implementation. Also its functional can be done using dynamic RAM and reduces the external testing methods also includes diagnosis of test.
Karner resource estimation for objectory projectsOcho08
The document proposes a model for estimating resources needed to develop software using the Objectory process. The model is based on function points, which counts types of inputs, outputs, inquiries etc to determine the size of the system. It then adjusts for technical complexity factors and new environmental factors. It presents this Use Case Points model and shows how to calculate unadjusted use case points, technical complexity factor, and environmental factor. Finally, it validates the model using data from three projects. In summary, the model provides an early way to estimate resources for Objectory projects based on use case analysis and adjustment factors.
Overview of signal integrity simulation for sfp+ interface serial links with ...Conference Papers
The document discusses signal integrity simulation for high-speed SFP+ serial links. It begins with an overview of the challenges of high-speed transmission and the need for simulation. It then covers:
1) The IBIS-AMI model structure and configuration options for simulating transmitters and receivers.
2) The simulation setup created in ADS to optimize equalization settings for a specific SFP+ interface design.
3) The results of simulations at 10.3125Gbps and 16Gbps, showing larger eye openings at the lower data rate.
4) Additional transmitter simulation results, indicating better eye openings when optimizing de-emphasis levels and voltage amplitudes.
Relay Testing with HYPERSIM allows for more advanced testing of relays using a simulator rather than traditional open-loop testing. A simulator provides closed-loop testing where the relay response feeds back into the simulation, allowing testing under critical conditions. It offers benefits like evaluating new protection algorithms, studying complex protection schemes, and analyzing interactions between real and virtual relays. Key features include detailed EMT analysis, automation of test sequences, and the ability to simulate faults, disturbances, and communications protocols like IEC 618450.
This document discusses the use of neural networks in robotics. It outlines pros and cons, common neural network types used, and applications. The pros include the ability to model nonlinear systems, learn functions from data, perform parallel processing, and handle multiple inputs/outputs. Cons include getting stuck in local minimums and potential lack of accuracy. Common neural network types in robotics are backpropagation, Kohonen, and Hopfield networks. Applications include solving kinematics/dynamics problems, trajectory planning, computer vision/sensing, and control systems.
FPGA Optimized Fuzzy Controller Design for Magnetic Ball Levitation using Gen...IDES Editor
This paper presents an optimum approach for
designing of fuzzy controller for nonlinear system using
FPGA technology with Genetic Algorithms (GA) optimization
tool. A magnetic levitation system is considered as a case study
and the fuzzy controller is designed to keep a magnetic object
suspended in the air counteracting the weight of the object.
Fuzzy controller will be implemented using FPGA chip.
Genetic Algorithm (GA) is used in this paper as optimization
method that optimizes the membership, output gain and inputs
gains of the fuzzy controllers. The design will use a highlevel
programming language HDL for implementing the fuzzy
logic controller using the Xfuzzy tools to implement the fuzzy
logic controller into HDL code. This paper, advocates a novel
approach to implement the fuzzy logic controller for magnetic
ball levitation system by using FPGA with GA.
Towards Functional Safety compliance of Matrix-Matrix MultiplicationJavier Fernández Muñoz
The document discusses ensuring functional safety for machine learning-based autonomous systems. It proposes using checksum algorithms to detect errors in matrix-matrix multiplication, a key computation. The solution was evaluated on sequential and AVX-based multiplication, finding that checksums can achieve 100% diagnostic coverage with minimal performance impact depending on matrix size. Future work includes evaluating multiple bit errors and accelerators.
The document discusses using process control monitor (PCM) data from wafer fabrication to predict device performance and wafer yield. PCM data from various sites on the wafer are collected during fabrication and correlated with performance data from devices near those sites. A predictive model is created using the PCM data as inputs to predict device parameters and yield as outputs. The model allows early prediction of wafer and device quality before full testing. Neural networks and linear models were tested, with neural networks showing slightly better prediction accuracy. The model was deployed using a database and scripting to efficiently predict performance for new wafers based on their PCM data.
Presentation for Research Presentation Day 2009amaitos
This document discusses a research project investigating integrating dynamic thermal simulation with building energy management systems (BEMS) for improved energy efficiency. The researchers will set up experiments linking sensors and actuators to a building simulation model to test control strategies in real-time, like optimizing start/stop times. Initial results show using simulation to determine the optimal start time to reach a desired indoor temperature. Future work includes optimizing control algorithms and testing on actual buildings to use simulation as the "brains" of BEMS throughout a building's lifespan.
Network simulation software allows users to model complex systems in detail and analyze proposed network systems before implementing them. Some popular open source simulators include NS2, NS3, OMNeT++, and J-Sim, while commercial options include OPNET, QualNet, and 20-Sim. These simulators vary in features such as supported programming languages, licensing, ease of use, and supported platforms. Simulation provides advantages like studying existing systems without disruption and gaining insights without implementing systems. However, it also requires time and training to properly use and interpret results.
James Ransley is a physicist with experience in microelectromechanical systems and acoustic inkjet technology. He has a track record of innovation through analytical and numerical modeling of complex systems. He holds a PhD in materials science from Cambridge University and has worked in both academia and industry. He is looking for a new role where he can continue applying physics principles.
This document is a laboratory manual for digital signal processing experiments using MATLAB and a DSP chip. It contains an introduction to MATLAB and lists 16 experiments using MATLAB and 11 experiments using a DSP chip. The experiments cover topics like DFT, convolution, filtering, sampling, and impulse responses. The manual was prepared by faculty of the electronics and communication engineering department to help students learn and apply DSP concepts.
A Survey on Wireless Network SimulatorsjournalBEEI
The Network simulator helps the developer to create and simulate new models on an arbitrary network by specifying both the behavior of the network nodes and the communication channels. It provides a virtual environment for an assortment of desirable features such as modeling a network based on a specific criteria and analyzing its performance under different scenarios. This saves cost and time required for testing the functionality and the execution of network. This paper has surveyed various Wireless Network Simulators and compared them.
Model Predictive Control For Integrating ProcessesEmerson Exchange
The document discusses controlling integrating processes like liquid levels using model predictive control (MPC). It explains that integrating processes require control since they have no natural equilibrium. The key points covered are:
- MPC can effectively control integrating processes by considering feedback, model correction, rotation factors, and tuning parameters.
- Tuning time to steady state, penalty on move, and model correction factor impact control performance for setpoint changes and load disturbances.
- With proper tuning, MPC provides improved control of integrating processes compared to conventional PI control.
Robust model predictive control for discrete-time fractional-order systemsPantelis Sopasakis
In this paper we propose a tube-based robust model predictive control scheme for fractional-order discrete-
time systems of the Grunwald-Letnikov type with state and input constraints. We first approximate the infinite-dimensional fractional-order system by a finite-dimensional linear system and we show that the actual dynamics can be approximated arbitrarily tight. We use the approximate dynamics to design a tube-based model predictive controller which endows to the controlled closed-loop system robust stability properties
4 modeling and control of distillation column in a petroleum processnazir1988
This document describes the modeling and simulation of a condensate distillation column in a petroleum process. It presents a calculation procedure to model the column based on an energy balance structure using reflux rate and boilup rate as inputs to control distillate purity and bottom product impurity. A nonlinear dynamic model of the column is developed and simulated in MATLAB. The simulation shows the column can maintain product quality under normal operations but quality decreases with disturbances like changes in feed rate. A reduced-order linear model is then developed for use in model-reference adaptive control to improve disturbance rejection.
The document provides an overview of model predictive control (MPC), including its advantages, concept, terminology, applications, prediction models, state space models, optimization windows, closed-loop control systems, constraints, and numerical solutions. MPC has advantages like intuitive concepts, easy tuning, handling multivariable processes, and treating constraints simply. It requires a process model and derivation of the control law is more complex than PID. MPC uses prediction models within an optimization window to minimize a cost function while satisfying constraints. Numerical solutions involve techniques like quadratic programming.
M. Tech. Thesis: Control System Design for an Energy Efficient Operation of P...Pallavi Kumari
This document describes the design and optimization of a ternary Petlyuk column for separating a benzene-toluene-xylene mixture. The column is divided into six tray sections with specified numbers of trays in each section. Base case operating conditions are determined that minimize reboiler duty. These include specifications for product purities and a vapor side draw ratio. Temperature and composition profiles at the optimized conditions are presented, achieving a minimum reboiler duty of 1619 kW.
Smart Process Distillation Application Improves Recovery And Saves EnergyJim Cahill
The document summarizes a case study where a SmartProcess Distillation Optimizer was implemented on a large purification distillation column. The optimizer improved recovery and reduced energy usage. It immediately started reducing distillate rate and product losses by 22% while maintaining purity specifications. Steam usage was reduced by an average of 7%, resulting in estimated annual savings of over $700k. The project was completed in two weeks following a functional design study, demonstrating excellent payback.
Introductory course on concepts used in predictive control. For more files and MATLAB suporting information go to:
http://controleducation.group.shef.ac.uk/OER_index.htm
simulation and control in chemical enginneringThành Lý Phạm
This document describes the Group of Chemical Process Modeling, Control and Simulation at the University "Babeş-Bolyai" in Cluj-Napoca, Romania. It outlines the research areas and projects, laboratory equipment, mathematical models developed, and international meetings attended by the research group from 1999-2003. The group's work involves modeling, simulation, optimization, and advanced control of various chemical processes.
GATE 2013 CHEMICAL ENGINEERING SolutionsSundar Kannan
Solutions for gate 2013 chemical engineering paper.
The same in a powerpoint format can be downloaded from:
https://drive.google.com/file/d/0B6g7hNFF87j3ZkQydGFEQ3QxaVE/edit?usp=sharing
mskannan20@gmail.com
Design the implementation of CDEx Robust DC Motor.Ankita Tiwari
This document describes an experiment using LabVIEW to design and implement a robust DC motor controller. It discusses:
1. The apparatus used, including the LabVIEW software and computer specifications.
2. An overview of LabVIEW, describing it as a visual programming language where programs are created by connecting functional nodes with wires to control data flow.
3. The procedure for the experiment, which involves modeling the DC motor and controller, discretizing the controller, and performing time and frequency response simulations to analyze robustness.
To Perform SIL And PIL Testing on Fast Dynamic System using Economical AVR Co...ijsrd.com
This document describes using an economical AVR controller to perform software-in-the-loop (SIL) and processor-in-the-loop (PIL) testing on a fast dynamic system. It discusses using an Arduino board with an Atmega328 microcontroller to implement rapid control prototyping (RCP) methodology. The RCP process involves modeling a DC motor system in Simulink, designing a PI controller, and then performing SIL and PIL tests to verify the controller code functions as intended on the AVR hardware before implementation on the real system. The results show the PIL output is within acceptable limits of the SIL and model-in-the-loop simulations, demonstrating the feasibility of using low
This document describes a Microsoft Excel-based analytical tool developed by Mafraha Jahan to assess the energy performance of educational buildings. The tool has two sections: an initial assessment screen for administrative personnel and detailed analysis tabs for facility/engineering personnel. It has 17 tabs for billing information, temperature profiles, HVAC details, and analyzing 12 potential energy performance measures. The tool helps prioritize energy usage sectors, propose measures, and calculate annual energy/cost savings and paybacks to identify the most economical measures.
Model predictive control techniques for cstr using matlabIAEME Publication
1) The document compares Model Predictive Control (MPC) and Dynamic Matrix Control (DMC) techniques for controlling a Continuous Stirred Tank Reactor (CSTR) system using MATLAB.
2) MPC explicitly uses a dynamic model to predict process behavior over a horizon and optimize future controls. DMC similarly predicts outputs and optimizes future controls but uses a dynamic matrix of step responses.
3) Internal Model Control (IMC) is also examined, which encapsulates a representation of the process in the controller. IMC can specify closed-loop responses and is appropriate for linear, stable systems.
Implementation of a new Size Estimation Modelijsrd.com
In this paper, we present a comparison between the COCOMO size estimation and a proposed size estimation model. Our experimental results show that the proposed model is providing more accurate size. It will help in accurate effort and cost estimation. Ultimately it will result in increase in overall productivity. Size estimation is a very popular task. We also explain the fundamentals of size estimation.
A resonable approach for manufacturing system based on supervisory control 2IAEME Publication
This document summarizes a research paper that proposes a novel approach for manufacturing system control using supervisory control and discrete event systems. It describes a testbed that was developed using this approach with three main hardware components: a personal computer, interface, and programmable logic controller. The paper discusses developing a model for the large, complex testbed manufacturing system by breaking it down into smaller, fundamental and interaction sub-models. It explains how the testbed model was implemented using clocked Moore synchronous state machines in programmable logic controller ladder logic programs.
LabVIEW - Teaching tool for control design subjectIOSR Journals
This document discusses using LabVIEW as a teaching tool for control design subjects. It allows students to simulate experimental results, analyze comparisons between different systems, and validate modeling and controller design. Students can visualize the effects of changing system parameters. This makes the learning process more active compared to traditional lectures. LabVIEW provides tools like root locus analysis, Bode plots, and step responses to analyze system performance. Examples are given of modeling a DC motor and analyzing its frequency response and root locus. LabVIEW is concluded to improve engineering education by allowing students to validate theory with working prototypes.
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
The document presents the modeling and real-time simulation of an induction motor using RT-LAB software. RT-LAB allows converting Simulink models to real-time simulations that can run across multiple processors. The paper discusses the mathematical model of an induction motor and its implementation in RT-LAB. Two models are presented - one where data is transferred during simulation using OpComm blocks, and another where data is recorded using OpWriteFile blocks and transferred after simulation. Simulation results are presented for an induction motor with described parameters running on an RT-LAB configuration.
This document discusses using a perceptron neural network model to implement the COCOMO II software cost estimation model. It begins with an introduction to software cost estimation and the COCOMO II model. It then provides details on neural networks and the perceptron learning rule. The proposed model uses the COCOMO II effort multipliers and scale factors as input to a three-layer perceptron network with one hidden layer. The weights are initially set based on the COCOMO II model and are updated using the perceptron learning rule. The model aims to provide more accurate effort estimates than COCOMO II alone by incorporating machine learning. Prior related work combining COCOMO II and neural networks is also discussed.
Performance analysis of a liquid column in a chemical plant by using mpceSAT Publishing House
This document discusses the use of model predictive control (MPC) to control the composition of a liquid column in a chemical plant. It provides background on MPC and how it can be used for multivariable processes like liquid columns. The document describes modeling a liquid column process in MATLAB Simulink and comparing the performance of MPC and PID control of the column. The results show that MPC provides better control of the column composition and liquid level than PID control.
Performance analysis of a liquid column in a chemical plant by using mpceSAT Publishing House
This document discusses the use of model predictive control (MPC) to control the composition of a liquid column in a chemical plant. It provides background on MPC and how it can be used for multivariable processes like liquid columns. The document describes modeling a liquid column process in MATLAB Simulink and comparing the performance of MPC and PID control of the column. The results show that MPC provides better control of the column composition and liquid level than PID control.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijics
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the
performance of an aircraft. This is required both for the success of the mission and the survivability of the
aircraft in the harsh enemy conditions.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijcisjournal
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the performance of an aircraft. This is required both for the success of the mission and the survivability of the aircraft in the harsh enemy conditions.
A system is envisioned comprising of the accurate modeling of the physical world and the accurate model of control system. An interoperable system which can work seamlessly together will provide mission planners, System integrators, aeronautical/aerospace engineers a milieu wherein the Control System designer who is found wanted as far as the physical world is concerned is given a system which can simulate the real world in lab conditions. To achieve this, we combine the two most promising environments prevalent in the industry today namely Systems tool kit for modeling the operational environment MATLAB and LabVIEW for modeling the control system environment. Using a Math script window of LabVIEW, we have designed the aircraft model and controlling the variables of an aircraft using a simulation loop of a LabVIEW. The different flight conditions were arrived using Orthogonal Array (OA) based on different Aircraft weight, Altitude, Mach number configurations. This attempts to span the aircrafts across the regimes in aircrafts flight envelope. A system comprising of both, with seamless UDP based connection between the two is developed to expedite the process of development of feasible control system design and verification which allows the aircrafts to undertake complex mission. This system we believe would answer questions of limits of the aircrafts maneuverability and survivability in terms of its limitation concerning control system design and development of commercial fighter aircrafts, UAV's and Quad copters.
solve 6 , quiz 2link of book httpwww.irccyn.ec-nantes.fr~mart.pdfarihantcomputersddn
solve 6 , quiz 2
link of book :http://www.irccyn.ec-
nantes.fr/~martinet/Mobrob/FundamentalsofVehicleDynamics.pdf 122% Document Elements
Tables charts Smart Art Review A Home Layout Paragraph Style Cambria (Body) 12 Aa A
AaBbc AaBbCcDdE Normal No spacing Heading 1 Heading 2 Tit Problem 6 Propose a currently
non-commercially available car mechatronics system. Describe the sensors, ac uators and control
system you ntend to use. Explain the purpose of this innovation and specify what the added value
is for the user [i.e why would anyone want to buy it?]. Print Layout View Sec 1 Pages: rds: 32 of
45 Search in Document Aa Text Box Shape Picture Themes 22% O
Solution
COMPUTER AIDED DESIGN OF MECHATRONIC SYSTEMS I Use of advanced computer
tools in the product development process is necessary to meet the market needs for fast product
development. I These tools allows for the verification of the design in all phases of the
development process. I Virtual prototypes, I The main drawback of using the computer tools is
that they are mostly limited to specific domains. The main requirement to the computer tools for
mechatronic system design I to apply to the systems from various domains. I to simultaneously
use different system representations (block diagram, physical model, bond graph)
Matuško&Koloni 2 of 43 MECHATRONICS SYSTEMS 2014/2015 CLASSICAL (NON-
MECHATRONIC) OPTIMIZATION I Classical approach to the optimization during the design
process assumes that the parameters of the process, sensors, actuators are known and constant
and only controller parameters are optimized. CONTROLLER PROCESS INTERFACE
SENSOR OPTIMIZATION + - yR(t) y(t) Matuško&Koloni 3 of 43 MECHATRONICS
SYSTEMS 2014/2015 OPTIMIZATION IN MECHATRONIC SYSTEM I Since the main
characteristic of the mechatronic design approach is simultaneous design of all system
components, the optimization process is performed, not only on the controller parameters but
also on the parameters of the other parts of the system. CONTROLLER PROCESS
INTERFACE SENSOR OPTIMIZATION + - yR(t) y(t) Matuško&Koloni 4 of 43
MECHATRONICS SYSTEMS 2014/2015 MODELS OF MECHATRONIC SYSTEMS I
Physical models, I Bond graphs I Control oriented models I Block diagrams, I Bode diagrams, I
Nyquist diagram, I Sate space models. I Time domain models I Animation I C-code
Matuško&Koloni 5 of 43 MECHATRONICS SYSTEMS 2014/2015 PHYSICAL MODELS I
Physical models describe the system behavior both on functional and structural level, I Model
parameters are real physical parameters, I Generally, physical models are not suitable for the
control system design, I Building such models requires a deep insight into a physical behavior of
the system. Matuško&Koloni 6 of 43 MECHATRONICS SYSTEMS 2014/2015 BOND
GRAPHS I Universal representation of the system behavior, independent of the system nature. I
Based on power flow analysis of the system. I Two quantities that define the system behavior
and power flow within the system (and with its environment).
To design and implementation of variable and constant with no load for induction motor (IM) that is the goal in this work. This paper was including three parts, first the simulation model with no load for IM, Second the simulation model with constant load for IM, Third the simulation model with variable load for IM. In addition, this work includes comparative between two different controllers (PI and fuzzy logic control (FLC). The simulation results clearly the implementation of variable and constant with no load for IM. The simulation response of the system achieves better results when choosing to use type fuzzy-PI controller technique comparison with conventional PI controller and improve the performance of the system at different operation conditions.
IRJET- Modelling and Control of Variable Frequency Drive using PLC and Ma...IRJET Journal
This document discusses modeling and controlling a variable frequency drive (VFD) system using a programmable logic controller (PLC) and MATLAB. A generic VFD-motor system model is proposed. The plant model is developed in MATLAB/Simulink. The aim is to simulate and analyze appropriate control techniques for real-time PLC implementation of variable speed drive control. A PLC-based controller controls motor speed in the presence of variable load through image acquisition. The PLC program is translated to MATLAB/Simulink for testing. Results show the induction motor's temperature, actual speed and torque values, and energy balance at different operating conditions when controlled by the PLC and drive.
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.
A SYSTEMC/SIMULINK CO-SIMULATION ENVIRONMENT OF THE JPEG ALGORITHMVLSICS Design
In the past decades, many factors have been continuously increasing like the functionality of embedded systems as well as the time-to-market pressure has been continuously increasing. Simulation of an entire system including both hardware and software from early design stages is one of the effective approaches to improve the design productivity. A large number of research efforts on hardware/software (HW/SW) co-simulation have been made so far. Real-time operating systems have become one of the important components in the embedded systems. However, in order to validate function of the entire system, this system has to be simulated together with application software and hardware. Indeed, traditional methods of verification have proven to be insufficient for complex digital systems. Register transfer level test-benches have become too complex to manage and too slow to execute. New methods and verification techniques began to emerge over the past few years. Highlevel test-benches, assertion-based verification, formal methods, hardware verification languages are just a few examples of the intense research activities driving the verification domain.
This document discusses simulating a photovoltaic (PV) energy conversion system using MATLAB/Simulink and PLECS. It first describes the graphical environments of MATLAB/Simulink and PLECS. It then presents the simulation of a PV inverter system, developing models of the control system in MATLAB/Simulink and models of the power electronics plant using both MATLAB/Simulink transfer functions and PLECS circuit components. Simulation results are provided to compare the two approaches.
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Tech transfer is a common methodology for transferring new products or an existing
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