Control Design Made Easy By Ryan Gordon

Transcription

1 Control Design Made Easy By Ryan Gordon 2014 The MathWorks, Inc. 1

2 Key Themes You can automatically tune PID controllers in MATLAB from acquired data You can automatically tune PID controllers from dynamic simulations Complex MIMO control systems can be tuned automatically 2

3 Agenda (3 demos) PID Control Tuning in MATLAB from Measured Input/Output data PID Control Tuning in Simulink using a Simscape dynamic model Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink 3

4 Modeling Dynamic Systems: two approaches Data-Driven Modeling Use system test data to derive a mathematical representation 1.4e9 G( s) e 1 2.8e7s 0.1s 4

5 Modeling Dynamic Systems: two approaches First-Principles Modeling Use an understanding of the system s physics to derive a mathematical representation V+ V- 5

6 Modeling Dynamic Systems: Simscape First-Principles Modeling Use an understanding of the system s physics to map topography of physical components V+ V- 6

7 Both have advantages & disadvantages Data-Driven Modeling Complete Modeling Environment First-Principles Modeling Advantages: Fast Accurate Disadvantages: Requires plant Requires data acquisition system Advantages: Insight in behavior Physical parameters Disadvantages: Time-consuming Requires expertise 7

8 Agenda (3 demos) PID Control Tuning in MATLAB from Measured Input/Output data PID Control Tuning in Simulink using a Simscape dynamic model Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink 8

9 Introduction to Control System Toolbox Use industry-standard tools and algorithms for analysis and design of control systems Create, manipulate, and analyze linear models Design SISO and MIMO controllers 9

10 Introduction to System Identification Toolbox What is it? Modeling tool that can use experimental data to estimate mathematical models (black box) or tune parameters of predefined models in MATLAB (grey box) Why was it developed? To estimate models from data Who can use it? Controls engineers Plant and noise models for control system design Applications requiring prediction MPC, noise cancellation, financial analysis Decision making Virtual sensing, fault diagnosis, modal analysis y(t) t Data to Model s 2 s 2 3s 4 10

11 Agenda (3 demos) PID Control Tuning in MATLAB from Measured Input/Output data PID Control Tuning in Simulink using a Simscape dynamic model Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink 11

12 Introduction to Simulink Block-diagram environment Model, simulate, and analyze multidomain systems Design, implement, and test: Control systems Signal processing systems Communications systems Other dynamic systems Platform for Model-Based Design 12

13 Introduction to Simulink Control Design Automatically tune gains of PID controllers Rapidly perform advanced linear analysis and control design for plants modeled in Simulink u + Controller Plant A x + B u = 0 y 13

14 Agenda (3 demos) PID Control Tuning in MATLAB from Measured Input/Output data PID Control Tuning in Simulink using a Simscape dynamic model Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink 14

15 Introduction to Robust Control Toolbox Analyze and automatically tune control systems for performance and robustness in the presence of uncertainty Design fixed-structure controllers modeled in Simulink 15

16 Key Themes You can automatically tune PID controllers in MATLAB from acquired data You can automatically tune PID controllers from dynamic simulations Complex MIMO control systems can be tuned automatically 16

17 Next Steps Check out the other booths at the MATLAB Virtual Conference Visit the website for even more videos, examples and webinars Check out the MATLAB community Answers Cody Blogs 17