tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - type:Model product:"Control System Toolbox"User-contributed code library2014-12-24T21:47:56-05:00761100tag:www.mathworks.com,2005:FileInfo/488712014-12-24T17:49:40Z2014-12-24T17:49:40ZFeedback Control of Dynamic Systems, 7th Edition, 2015Matlab files for Feedback Control of Dynamic Systems, 7th Edition, Pearson, 2015<p>Matlab files to create the figures in Feedback Control of Dynamic Systems, 7th Edition, Pearson, 2015, by G. F. Franklin, J. D. Powell, A. Emami-Naeini</p>Abbas Emami-Naeinihttp://www.mathworks.com/matlabcentral/fileexchange/authors/8710MATLAB 8.4 (R2014b)Control System ToolboxSimulinkMATLABSymbolic Math ToolboxSystem Identification ToolboxSymbolic Math ToolboxSystem Identification Toolboxfalsetag:www.mathworks.com,2005:FileInfo/475382014-08-21T15:14:23Z2014-12-02T21:23:24ZRLC circuit demo bundleAnalyze the RLC series circuit using different modeling approaches and with hardware<p>This demo bundle demonstrates how to model and analyze a dynamical system – the RLC circuit – in MATLAB, Simulink, and Simscape and how to seamlessly move between these modeling environments. The corresponding hardware experiment is analyzed with a low-cost data acquisition hardware platform.
<br />Example files:
<br />RLC_symbolic.m: model and analyze circuit using symbolic math
<br />RLC_nonlinear.m: model and analyze circuit using numeric math
<br />RLC_nonlinear.pdf: documentation generated with MATLAB’s publish feature
<br />RLC_simulink.slx: model and analyze circuit using graphical modeling
<br />RLC_simscape.slx: model and analyze circuit using physical modeling
<br />RLC_AnalogDiscovery.m: analyze hardware circuit using the DAQ toolbox
<br />RLC_AnalogDiscovery.pdf: documentation generated with MATLAB’s publish feature</p>
<p>Support material:
<br />data_bode.mat: hardware measurement data file
<br />data_step.mat: hardware measurement data file
<br />Comp_Bode.fig: plot comparing theory and hardware experiment
<br />Comp_Step.fig: plot comparing theory and hardware experiment
<br />RLC_model.PNG: Simscape model screenshot
<br />RLC_schematic.PNG: schematic for hardware experiment
<br />license.txt</p>
<p>Note: these are the example files that are used in a recorded Webinar on experimenting in teaching (available at <a href="http://www.mathworks.com/videos/learn-effective-efficient-experimenting-with-matlab-98172.html">http://www.mathworks.com/videos/learn-effective-efficient-experimenting-with-matlab-98172.html</a>). This Webinar discusses some of the teaching challenges and presents approaches and techniques to efficiently create, perform and evaluate experiments.</p>
<p>Webinar resources:
<br />* Interactive tutorials: <a href="http://www.mathworks.com/academia/student_center/tutorials/?s_tid=acport_tut_sp_til">http://www.mathworks.com/academia/student_center/tutorials/?s_tid=acport_tut_sp_til</a>
<br />* Online courseware: <a href="http://www.mathworks.com/academia/classroom-resources/search.html?resource=course%20materials">http://www.mathworks.com/academia/classroom-resources/search.html?resource=course%20materials</a>
<br />* Hardware catalogue: <a href="http://www.mathworks.com/hardware-support/index.html?suggestion=">http://www.mathworks.com/hardware-support/index.html?suggestion=</a>
<br />* Cody Coursework: <a href="https://coursework.mathworks.com/">https://coursework.mathworks.com/</a></p>Mischa Kimhttp://www.mathworks.com/matlabcentral/fileexchange/authors/414423MATLAB 8.3 (R2014a)Control System ToolboxData Acquisition ToolboxParallel Computing ToolboxSimscapeSimulinkSimulink Control DesignSymbolic Math ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/484712014-11-16T09:27:06Z2014-11-16T09:27:06ZGPC control of a two-tank systemTo illustrate how to control a nonlinear plant using GPC based on a locally linearized model<p>This Simulink model is to illustrate the GPC control of a nonlinear plant based on a locally linearized model around the equilibrium point.
<br />The nonlinear plant is a two-tank system described in the paper "Constrained Pole Assignment Control of a Two Tank System", 2014 15th International Carpathian Control Conference (ICCC), pp.52-57. The output is the level of tank 2. The level of tank 1 is not observed.</p>
<p>It consists of the following files:</p>
<p>TwoTank.mdl : the Simulink model
<br />T2Tank.m : S-function of the plant
<br />T2TankControl.m : S-function of the controller
<br />GPCcoef.m : a function to compute the GPC coefficients based on
<br /> my paper "A novel DMC-like implementation of GPC"
<br />radical.m : a simple function to compute sign(x)sqrt(|x|)</p>
<p>Users can try to modify the final value of the step function serving as the reference signal here (be sure not to deviate very much from the equilibrium, since we only use a local model here), start simulation, and see the output of the plant.</p>
<p>Copyright (c) 2014, Yiping Cheng, <a href="mailto:ypcheng@bjtu.edu.cn">ypcheng@bjtu.edu.cn</a></p>Yiping Chenghttp://www.mathworks.com/matlabcentral/fileexchange/authors/520204MATLAB 7 (R14)Control System Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484342014-11-12T14:45:42Z2014-11-12T15:39:03ZWebinar Ivan LIEBGOTT: Enseigner les sciences de l'ingénieur avec MATLAB SimulinkLes fichiers du Webinar: Enseigner les sciences de l'ingénieur avec MATLAB Simulink (Ivan LIEBGOTT)<p>L'ensemble des fichiers qui ont servi à la réalisation du webinar sont disponibles dans ce téléchargement.</p>Ivan Liebgotthttp://www.mathworks.com/matlabcentral/fileexchange/authors/302588MATLAB 8.3 (R2014a)Control System ToolboxSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/483352014-11-03T14:41:48Z2014-11-03T14:41:48ZSample simulation: Vehicle with PID controllerA simple car simulation<p>This Vehicle model is input a pedal opening, and is output a vehicle speed[km/h].
<br />I used this book as reference:
<br /><a href="http://www.amazon.co.jp/gp/switch-language/product/4906864015/ref=dp_change_lang?ie=UTF8&language=en_JP">http://www.amazon.co.jp/gp/switch-language/product/4906864015/ref=dp_change_lang?ie=UTF8&language=en_JP</a></p>Daisuke Fukasehttp://www.mathworks.com/matlabcentral/fileexchange/authors/143976MATLAB 8.3 (R2014a)Control System ToolboxSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/286182010-09-01T19:50:10Z2014-10-29T20:29:08ZData based modeling of nonlinear dynamic systems using System Identification ToolboxPerspectives on nonlinear identification using a throttle valve modeling example.<p>Using an engine throttle valve modeling example, this demo shares some perspectives on creation of nonlinear models of dynamic systems from the measurements of its input and outputs. It describes useful workflows for approaching the task of data-based modeling using System Identification Toolbox™ . Two modeling approaches are described:
<br />1. Black box modeling: case where you cannot derive the exact mathematical representation of the system from physical considerations; the form of the model as well as the values of its coefficients is extracted from data.</p>
<p>2. Grey-box modeling: the equations of motion relating the input and output variables are known, but the values of various physical constants in the equations are unknown; the data is then used to find the values of those unknowns only.</p>
<p>The emphasis is on the black box modeling approach. It is shown that even though no a priori knowledge of model structure is required, it is often helpful to have some intuition about the nature of the system and to use this knowledge to fine-tune the configuration of model structures. </p>
<p>Contents:
<br />* A document titled "DATA-BASED MODELING OF ENGINE THROTTLE VALVE DYNAMICS" </p>
<p>* MATLAB files for command line demo (see throttledemo.m)
</p>Rajiv Singhhttp://www.mathworks.com/matlabcentral/fileexchange/authors/8280MATLAB 7.10 (R2010a)Optimization ToolboxSimulinkSystem Identification ToolboxMATLABStatistics ToolboxControl System ToolboxWavelet ToolboxFinancial Toolboxfalsetag:www.mathworks.com,2005:FileInfo/480422014-10-06T18:45:41Z2014-10-06T18:45:41ZFoster and Cauer equivalent networksContinued fraction formula is used to calculate parameters of the equivalent Cauer-type RC ladder.<p>You will need only basic matrix operations to construct the Cauer-type RC ladder equivalent to the Foster-type RC chain. The algorithm is often being embedded into thermal modelling tools; therefore, usually you don't have to perform this transformation externally. My submission is then purely for educational purposes. You will also need Control System Toolbox xor Symbolic Math Toolbox to perform some initial transfer function manipulations. However, it is also possible to provide numerator and denominator coefficients explicitly. To run the illustrative non-parametrized model, you will need the free PLECS Viewer (<a href="http://www.plexim.com/download/blockset">http://www.plexim.com/download/blockset</a>). The full parametrized version of the PLECS-based model is also included.</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Control System Toolboxfalsetag:www.mathworks.com,2005:FileInfo/479882014-10-02T15:29:53Z2014-10-02T18:55:16ZProgrammable linear-quadratic regulatorA feedforward neural network is used to adjust LQR gains in the case of non-stationary state matrix.<p>This model demonstrates that the LQR design approach can be effectively used also for plants characterized by a non-stationary state matrix. A set of controllers is designed for different working points and an FFNN is employed to store this knowledge. The gain matrix K is then adjusted on the fly if the state matrix A changes. In the case of induction motor some entries of the state matrix A are functions of e.g. the stator flux space vector angular velocity and the slip angular velocity of the rotor. More details can be found in Neural-Network-based Programmable State Feedback Controller for Induction Motor Drive (<a href="http://dx.doi.org/10.1109/IJCNN.2006.246811">http://dx.doi.org/10.1109/IJCNN.2006.246811</a>). It is also advisable to get familiar with LQR basics prior to playing with this model. See e.g. <a href="http://www.mathworks.com/help/control/ref/lqr.html">http://www.mathworks.com/help/control/ref/lqr.html</a> .</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Control System ToolboxNeural Network ToolboxSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/478672014-09-17T16:20:53Z2014-09-17T22:46:01ZRepetitive Neurocontroller with Disturbance FeedforwardThis project demonstrates repetitve process control using gradient-based dynamic optimization tool.<p>Repetitive neurocontroller (RNC) with disturbance feedforward path active in the pass-to-pass direction (kDFF) represents a novel (2014 as far as kDFF is concerned) approach to repetitive process control. The resulting control scheme is of Disturbance Dual Feed-Forward (DDFF) type. The solution is of model-free type, i.e. no accompanying neural network is needed to model dynamics of the plant. Please see inside the m-files for more information. This submission enables you to play with different system configurations, i.e. several flags are provided to easily reconfigure the system. The solution was inspired by the concept of iterative learning control (ILC). This project might be of your interest if you deal with: repetitive process control, iterative learning control, dynamic optimization problems, and neurocontrollers in the form of online trained neural networks. Such control tasks are often encountered in robotics and power electronics. Refs to relevant papers are included. If something brought you here, it could be probably also interesting to have a look at my gradient-free swarm-based repetitive controller (PDPSRC or PDMSRC on Matlab Central).</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Control System ToolboxNeural Network ToolboxSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/478472014-09-14T19:55:17Z2014-09-15T21:23:45ZPlug-in Direct Particle Swarm Repetitive ControllerThis project demonstrates repetitve process control using evolutionary dynamic optimization tools.<p>Plug-in Direct Particle Swarm Repetitive Controller (PDPSRC) and Plug-in Direct Multi-Swarm Repetitive Controller (PDMSRC) represent a novel (2013) approach to repetitive process control. Please see inside the m-files for more information. This submission enables you to play with the single-swarm controller as well as the multi-swarm one. The solution was inspired by the concept of iterative learning control (ILC). This project might be of your interest if you deal with: repetitive process control, iterative learning control, dynamic optimization problems, particle swarm optimization. Such control tasks are often encountered in robotics and power electronics. Links to relevant open access papers are also included.</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Control System ToolboxSimulinkMATLABfalse