Lectures:
Introduction into problematic of design and realization of the controllers. Comparison of classical methods of design and modern control theories.
Realization of the controllers. Overview of software design. Real-time control. Realization of algorithms on chosen platforms: PC, microcontrollers, PLC, embedded systems,, dSPACE. Modern design techniques: Hardware-in-the-loop simulations. Rapid prototyping. Model-based design.
Design of PID controllers. Industrial PID controllers. Empiric method of setting PID controllers. Self-tunning PID controllers.
Nonlinear PID controllers. Analogue PID controllers. Choosing appropriate structure of control scheme for typical applications.
Digital PID controllers (PSD). Determination of parameters of controllers. Modification of PSD controllers. Determination of appropriate sampling period. PID controllers for engineering practice. Smooth controller attachment. Wind-up effect. Industrial PID controllers.
Introduction into quadratic optimal control. Strategies of quadratic optimal control. Dynamic programming, optimal principal. Principle of control design according minimization of quadratic critera.
Continuous quadratic optimal control, features of LQ controllers. Stochastic approach. Features of control circuit with LQ controller. Condition of realization. Adaptive LQ control. Description of the system and design of the control algorithms.
Linear stochastic system. Formulation of the problem of state estimation for stochastic system based on measuring inputs/outputs. Statistical methods of identification.
Adaptive and learning systems. Adaptive identification and control.
Optimal filtering based on input/output description - Wienerův filtr. Optimal filtration based on state-space description of the system - Kalman filter: correlated/uncorrelated noise of the process and measurement, extended Kalman filter.
LQG controller. Feedback state control for stochastic system. Scheme with Kalman filter. LTR method. Discrete LQG controller.
Predictive control strategy, design of predictive controller. Prediction based on I/O and state-space description. MPC with/without actuating limitation.
Robust control. Basic terminology. Use of robust controllers. Norms of the signals and systems, sensitivity functions. Introduction into description of uncertainty, structured and unstructured uncertainty, small gain theorem, robust stability. Methodology of robust control design. H2 a H-infinity methods.
Nonlinear systems. Methods of linearization. Problematics of nonlinear control. Fuzzy controller.
Exercises:
Working rules and conditions in the laboratory . Assigning individual projects.
Introduction to real-time desgin in Matlab/Simulink: xPC Target, Real Time Windows Target.
Laboratories:
Design of PSD controller and verivication on a real system.
Verification of wind up effect on a real system.
Adaptive identification on PC connected to a real system.
Design of adaptive controller with the model for a real system and its verification.
Laboratory experiment with quadratic optimal controller.
Laboratory experiment with LQR controller.
Laboratory experiment with MPC controller.
Laboratory experiment with LQG controller with Kalman filter.
Calculation of H-2 and H-infinity norms. Sensitivity functions
Laboratory experiment with robust controller.
Evaluation of particular methods of the control design.
Final test. Presentation of individual projects.
Projects:
All the students will be assigned to individual projects, which will be solved on computer according the assignment.
Introduction into problematic of design and realization of the controllers. Comparison of classical methods of design and modern control theories.
Realization of the controllers. Overview of software design. Real-time control. Realization of algorithms on chosen platforms: PC, microcontrollers, PLC, embedded systems,, dSPACE. Modern design techniques: Hardware-in-the-loop simulations. Rapid prototyping. Model-based design.
Design of PID controllers. Industrial PID controllers. Empiric method of setting PID controllers. Self-tunning PID controllers.
Nonlinear PID controllers. Analogue PID controllers. Choosing appropriate structure of control scheme for typical applications.
Digital PID controllers (PSD). Determination of parameters of controllers. Modification of PSD controllers. Determination of appropriate sampling period. PID controllers for engineering practice. Smooth controller attachment. Wind-up effect. Industrial PID controllers.
Introduction into quadratic optimal control. Strategies of quadratic optimal control. Dynamic programming, optimal principal. Principle of control design according minimization of quadratic critera.
Continuous quadratic optimal control, features of LQ controllers. Stochastic approach. Features of control circuit with LQ controller. Condition of realization. Adaptive LQ control. Description of the system and design of the control algorithms.
Linear stochastic system. Formulation of the problem of state estimation for stochastic system based on measuring inputs/outputs. Statistical methods of identification.
Adaptive and learning systems. Adaptive identification and control.
Optimal filtering based on input/output description - Wienerův filtr. Optimal filtration based on state-space description of the system - Kalman filter: correlated/uncorrelated noise of the process and measurement, extended Kalman filter.
LQG controller. Feedback state control for stochastic system. Scheme with Kalman filter. LTR method. Discrete LQG controller.
Predictive control strategy, design of predictive controller. Prediction based on I/O and state-space description. MPC with/without actuating limitation.
Robust control. Basic terminology. Use of robust controllers. Norms of the signals and systems, sensitivity functions. Introduction into description of uncertainty, structured and unstructured uncertainty, small gain theorem, robust stability. Methodology of robust control design. H2 a H-infinity methods.
Nonlinear systems. Methods of linearization. Problematics of nonlinear control. Fuzzy controller.
Exercises:
Working rules and conditions in the laboratory . Assigning individual projects.
Introduction to real-time desgin in Matlab/Simulink: xPC Target, Real Time Windows Target.
Laboratories:
Design of PSD controller and verivication on a real system.
Verification of wind up effect on a real system.
Adaptive identification on PC connected to a real system.
Design of adaptive controller with the model for a real system and its verification.
Laboratory experiment with quadratic optimal controller.
Laboratory experiment with LQR controller.
Laboratory experiment with MPC controller.
Laboratory experiment with LQG controller with Kalman filter.
Calculation of H-2 and H-infinity norms. Sensitivity functions
Laboratory experiment with robust controller.
Evaluation of particular methods of the control design.
Final test. Presentation of individual projects.
Projects:
All the students will be assigned to individual projects, which will be solved on computer according the assignment.