MATLAB control system toolbox

This tutorial uses the MATLAB Control System Toolbox for linear quadratie regulator, linear quadratie estimator (Kalman filter) and linear quadratie Gaussian eontrol system design. The tutorial also employs the Robust Control Toolbox for multivariable robust eontrol system design. Problems in Chapter 9 are used as design examples. 

The computer-generated transfer function for the voltage across the capacitor crosses two different energy domains without separation since the model is all together. The transfer function is obtained in one step in symbolic form. CAMPG generated the code for the A, B, C, D matrices which are displayed in MATLAB. Any other transfer function for the efforts and flow output variables can be obtained. More details are presented in [11]. At this point, the computer-generated model becomes so versatile that all the linear control theory operations implemented in the MATLAB Control Systems Toolbox can be used on the entire mechatronics model. 

This tutorial introduees the reader to time domain analysis using MATLAB. It uses eommands from the Control System Toolbox. A list of the eommands ean be found using... 

For each session, we put the most important functions in a table for easy reference or review. The first one is on the basic commands and plotting. Try the commands as you read. You do not have to enter any text after the "%" sign. Any text behind a "%" is considered a comment and is ignored. We will save some paper and omit the results generated by MATLAB. If you need to see that for help, they are provided on our Web Support. There is also where we post any new MATLAB changes and upgrades. Features in our tutorial sessions are based on MATLAB Version 6.1, and Control System Toolbox 5.1. 

For carrying out Step 3, any parametrical optimization numerical method can be used. The optimization procedure (Agranovich, 2004) is realized using MATLAB functions dlqry , Kalman (Control System Toolbox) and finincon (Optimization Toolbox). 

The MATLAB Control Toolbox makes it quite easy to generate frequency response plots for both openloop and closedloop systems. Table 11.3 gives a MATLAB program that generates Bode plots and Nichols plots. The process example is the three-heated-tank system with the openloop transfer function... 

For advanced techniques in modeling, identification, and control, MATLAB has a variety of additional toolboxes that are licensed individually. Relevant toolboxes for process control include control system, fuzzy logic, system identification, model predictive control, neural networks, optimization, partial differential equations, robust control, and statistics. 

The AR model and the variance of e k) can be estimated from an incontrol data set using software such as Matlab System Identification Toolbox [191]. A standard x-chart is designed using control limits at 3 standard deviations (3(t limits) to monitor the residuals j k) and consequently Jhistit). 

In order to reduce the displacement field of the cantilever beam system discussed in this paper, a non-linear fuzzy controller was constructed by using the Fuzzy Toolbox of Matlab. 

Among the software for control design and system simulation MATLAB together with SIMULINK is a widespread tool. In particular, MATLAB includes a large variety of toolboxes for control design (standard, non-linear. 

The compact format of ARX and ARMAX models given by Eqs 5.6 and 5.7 can be easily converted into more intuitive, expanded format exemplified by Eq. 5.9. With input (u and e) and output (y) data, the matrices A, B and C can be readily identified employing the System Identification Toolbox for Use with MATLAB (31). An ARX model for a pan, granulation process was developed by Adetayo et al. (3) with a successful application for effective control of the plant. 

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