PID And Predictive Control Of Electrical Drives...
This book presents methods for design and implementation of PID and predictive control of electrical drives and grid connected three phase power converters with emphasis on meeting operational constraints while optimizing performance. The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking readers from the fundamentals through to the sophisticated design and analysis.
PID and Predictive Control of Electrical Drives...
PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice. The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis. The book contains sections on closed-loop performance analysis in both frequency domain and time domain, presented to help the designer in selection of controller parameters and validation of the control system. Continuous-time model predictive control systems are designed for the drives and power supplies, and operational constraints are imposed in the design. Discrete-time model predictive control systems are designed based on the discretization of the physical models, which will appeal to readers who are more familiar with sampled-data control system. Soft sensors and observers will be discussed for low cost implementation. Resonant control of the electric drives and power supply will be discussed to deal with the problems of bias in sensors and unbalanced three phase AC currents.
Shan Chai, Dae Yoo, Lu Gan and Ki Ng are PhD students working under the supervision of Professor Wang and are part of the research team that has produced, and is producing, new approaches and new understanding of the electrical motor control and the control of regenerative power supplies.
PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice. The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis. The book contains sections on closed-loop performance analysis in both frequency domain and time domain, presented to help the designer in selection of controller parameters and validation of the control system. Continuous-time model predictive control systems are designed for the drives and power supplies, and operational constraints are imposed in the design. Discrete-time model predictive control systems are designed based on the discretization of the physical models, which will appeal to readers who are more familiar with sampled-data control system. Soft sensors and observers will be discussed for low cost implementation. Resonant control of the electric drives and power supply will be discussed to deal with the problems of bias in sensors and unbalanced three phase AC currents.
The paper deals with a Model Predictive Control (MPC) algorithm applied to electrical drives. The main contribution is a comprehensive and detailed description of the controller design process that points out the most critical aspects and gives also some practical hints for implementation. As an example, the MPC is developed for a permanent magnet synchronous motor drive. Speed and current controllers are combined together, including all of the state variables of the system, instead of keeping the conventional cascade structure. This way the controller enforces both the current and the voltage limits. Both simulation and experimental results point out the validity of the design procedure and the potentials of the MPC in the electrical drives field.
Abstract:Model predictive control (MPC) technology for multi-phase electric drives has received increasing attention in modern industries, especially in electric vehicles, marine electrical propulsion, and wind power generation. However, MPC has several challenges in controlling multi-phase electric drives, including the design of weighting factors, high computational complexity, large harmonic currents, heavy reliance on the system model, fault-tolerant control operation, common-mode voltage, and zero-sequence current hazards. Therefore, this paper gives a comprehensive review of the latest and most effective solutions to the existing major technical challenges and prospects for the future trends of MPC for multi-phase electric drives.Keywords: fault-tolerant control; harmonic currents reduction; model predictive control; multi-phase; machine weighting factors
Model Predictive Control (MPC) has a long history in the field of control engineering. It is one of the fewareas that has received on-going interest from researchers in both the industrial and academiccommunities. Three major aspects of model predictive control make the design methodology attractive toboth engineers and academics. The first aspect is the design formulation, which uses a completelymultivariable system framework where the performance parameters of the multivariable control systemare related to the engineering aspects of the system; hence, they can be understood and 'tuned' byengineers. The second aspect is the ability of method to handle both 'soft' constraints and hard constraintsin a multivariable control framework. This is particularly attractive to industry where tight profit marginsand limits on the process operation are inevitably present. The third aspect is the ability to performprocess on-line optimization.
In the majority of applications, MPC is designed to follow constant reference signals and rejectdisturbances that have predominantly low frequency contents. However, these types of model predictivecontrollers are not adequate to provide the closed-loop performance required when the reference signalsand the disturbances are complex and have periodic components. This has motivated the development ofa new class of model predictive controllers that use the design framework of MPC while addressing therequirements of controlling systems with periodic components, which is called predictive repetitivecontroller.
Using experimentally validated applications, this one-day workshop will show the four steps involved inthe design of a predictive-repetitive controller: (i) frequency response analysis to extract dominantperiodic components of the reference or disturbance signals and re-construct the signals using frequencysampling filters; (ii) the design of predictive repetitive controllers by embedding the dominant periodiccomponents; (iii) the design of constrained predictive repetitive controllers using quadratic programmingalgorithms; (iv) simulation and experimental validation of the predictive repetitive control system withconstraints using MATLAB and Simulink as a platform. The one-day workshop is enhanced byparticipation of the engineers from MathWorks. They will demonstrate how to use the Robotics SystemToolbox to develop robotics applications. The system toolbox provides algorithms and hardwareconnectivity for developing autonomous mobile robotics applications. Toolbox algorithms include: maprepresentation, path planning, path following for differential drive robots. The system toolbox alsoprovides an interface between MATLAB and Simulink, and the Robot Operating System (ROS). ThisROS interface enables you to test and verify applications on ROS-enabled robots and robot simulatorssuch as Gazebo.
Eric Rogers is Professor of Control Systems Theory and Design in the School of Electronics andComputer Science, University of Southampton. His current research interests are in iterative learning andrepetitive control theory and applications, flow control, algeraic/behavioral approaches tomultidimensional systems and repetitive predictive control. In terms of applications, the Southamptongroup of which he is the senior academic have transferred iterative learning control algorithms to roboticassistedupper limb stroke rehabilitation with supporting clinical trials and smart rotor based control ofwind turbines. He is the editor-in-chief of the International Journal of Control and has served on theorganising/international programme committees of many international conferences. He has also served asa consultant to many government and industrial organisations in the United Kingdom and elsewhere. 041b061a72