Description of a modeling, simulation, animation, and real-time control (MoSART) environment for a class of electromechanical systems

This paper describes an Interactive Modeling, Simulation, Animation, and Real-Time Control (MoSART) Environment that is useful for controls education and research. The described MoSART environment is shown to be useful for analyzing, designing, visualizing, and evaluating control systems for a class of "cart-pendulum" electromechanical systems. The environment-referred to as Cart-Pendulum Control3D-Lab-is based on Microsoft Windows, Visual C++, Direct-3D, and MATLAB/Simulink. The environment can be used as a stand-alone application or together with MATLAB, Simulink, and toolboxes. When used as a stand-alone application, a friendly graphical user interface permits easy interaction. Users may select (via pull-down menus) systems, dynamical models, control laws, exogenous signals (including joystick inputs) and associated parameters, initial conditions, integration routines, and associated parameters. When used with MATLAB, Simulink, and toolboxes, the previously mentioned nominal features are significantly enhanced. In either case, the interface permits users to access the following (via pull-down menus): animation models, mesh properties, texture and lighting models, system-specific visual indicators, graphics to be displayed, animation/data display/storage rates, simulation control buttons, and extensive documentation. When Simulink is present, users can exploit extensive visualization and three-dimensional (3-D) animation features through provided and/or user-generated Simulink diagrams. This capability makes the developed environment very extensible with respect to mathematical models and control laws. In addition, users may readily export simulation data to MATLAB/toolboxes for postprocessing and further analysis. The environment also contains a suite of well-documented (easy-to-modify) models and control laws that are implemented within the provided Simulink block diagrams. Provided (special) blocks enable animation, joystick inputs, and (near) real-time simulation and animation (when possible). (Near real-time-or faster-than-real-time-simulation and animation are possible whenever the mathematical and animation models are sufficiently simple and data manipulation requirements, e.g. storage and display, are sufficiently mild. For the systems considered, (near) real-time simulation and animation is readily achievable.) Associated with each block diagram are system-specific, menu-accessed m-files that permit detailed analysis and design. A hardware module permits real-time control of actual hardware experiments. The developed environment is shown to be a valuable tool for enhancing both controls education in a variety of classes as well as research. Examples are presented to illustrate the utility of the environment.

[1]  Bernard Etkin,et al.  Dynamics of flight , 1959 .

[2]  T. Teichmann,et al.  Dynamics of Flight: Stability and Control , 1959 .

[3]  John H. Blakelock,et al.  Automatic control of aircraft and missiles , 1965 .

[4]  E. Rynaski,et al.  Optimal helicopter station keeping , 1966 .

[5]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[6]  L. Shampine,et al.  Numerical Solution of Ordinary Differential Equations. , 1995 .

[7]  Michael A. Malcolm,et al.  Computer methods for mathematical computations , 1977 .

[8]  F. Grund.  Forsythe, G. E. / Malcolm, M. A. / Moler, C. B., Computer Methods for Mathematical Computations. Englewood Cliffs, New Jersey 07632. Prentice Hall, Inc., 1977. XI, 259 S , 1979 .

[9]  James Allen Mette.  Multivariable control of a submarine using the LQG/LTR method. , 1985 .

[10]  Michael Athans,et al.  Multivariable Control of a Twin Lift Helicopter System using the LQG/LTR Design Methodology , 1986, 1986 American Control Conference.

[11]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[12]  S. Nash,et al.  Numerical methods and software , 1990 .

[13]  A. G. Sreenatha,et al.  Robust low order dynamic controller for flexible spacecraft , 1991 .

[14]  Vladimir A. Chobotov.  Spacecraft Attitude Dynamics and Control , 1991 .

[15]  John T. Bosworth,et al.  Linearized aerodynamic and control law models of the X-29A airplane and comparison with flight data , 1992 .

[16]  R. T. Reichert.  Dynamic scheduling of modern-robust-control autopilot designs for missiles , 1992 .

[17]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[18]  L. Geppert.  Platforms: the new contenders-not your father's CPU , 1993, IEEE Spectrum.

[19]  J. A. Adam,et al.  Virtual reality is for real , 1993 .

[20]  K. Kornbluh.  Engineering software-seeing data in action , 1993 .

[21]  Gary J. Balas,et al.  LPV control design for pitch-axis missile autopilots , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[22]  Armando A. Rodriguez,et al.  Graphical visualization of missile–target air‐to‐air engagements: An educational tool for designing and evaluating missile guidance and control systems , 1995 .

[23]  J. Tagg,et al.  From Teaching to Learning — A New Paradigm For Undergraduate Education , 1995 .

[24]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[25]  A. A. Rodriguez,et al.  An interactive modeling, simulation, animation and real-time control (MoSART) helicopter environment , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[26]  Karl Johan Åström,et al.  Interactive tools for education in automatic control , 1998 .

[27]  A. A. Rodriguez,et al.  Multivariable analysis and control of a cart-pendulum-seesaw system using an animation tool , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[28]  Chen-I Lim,et al.  DEVELOPMENT OF INTERACTIVE MODELING, SIMULATION, ANIMATION, AND REAL-TIME CONTROL (MoSART) TOOLS FOR RESEARCH AND EDUCATION , 1999 .

[29]  A. A. Rodriguez,et al.  A tool for analyzing, designing, and visualizing multivariable aircraft control systems , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[30]  A. A. Rodriguez,et al.  Multivariable control of helicopter systems using an interactive modeling, simulation, and visualization tool , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[31]  Ian D. Walker,et al.  A Simulink-based robotic toolkit for simulation and control of the PUMA 560 robot manipulator , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[32]  W. P. Dayawansa,et al.  Virtual control systems laboratory , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[33]  Warren E. Dixon,et al.  A MATLAB-based control systems laboratory experience for undergraduate students: toward standardization and shared resources , 2002, IEEE Trans. Educ..

[34]  Andy Olsen,et al.  Microsoft Visual C# .NET Step by Step--Version 2003 , 2003 .

[35]  Domenico Prattichizzo,et al.  The automatic control telelab: a user-friendly interface for distance learning , 2003, IEEE Trans. Educ..

[36]  Bruno Welfert,et al.  Modelling, simulation, animation, and real-time control (MoSART) for a class of electromechanical systems: a system-theoretic approach , 2004 .

[37]  Richard M. Felder,et al.  MATTERS OF STYLE , 2004 .

[38]  O. Cifdaloz,et al.  Description of a Modeling, Simulation, Animation, and Real-Time Control (MoSART) Environment for a Broad Class of Dynamical Systems , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.