Download Advanced Dynamic-system Simulation: Model-replication by Granino A. Korn PDF

By Granino A. Korn

Research the most recent strategies in programming refined simulation platforms. This state of the art textual content provides the most recent strategies in complex simulation programming for interactive modeling and simulation of dynamic platforms, corresponding to aerospace autos, keep an eye on platforms, and organic structures. the writer, a number one authority within the box, demonstrates software program that may deal with huge simulation stories on commonplace own pcs.

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Additional resources for Advanced Dynamic-system Simulation: Model-replication Techniques and Monte Carlo Simulation

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B) The Complete Simulation Program Figure 1-9c lists the complete guided-torpedo program used to produce the displays in Figures 1-9a and b. The experiment protocol first selects an integration routine, display colors, and display scale, and then sets the initial value of the integration step DT, the simulation runtime TMAX, and the number NN of display sampling points. The experiment-protocol script next sets torpedo parameters, initial target coordinates, and target-velocity components. Finally, we specify initial values for the state variables x, y, and phi.

An implied digital-to-analog converter converts y to an analog voltage that controls the motor torque torque. In the DYNAMIC program segment of Figure 2-4, the simulated digital controller (lines following the SAMPLE m statement) updates the state difference equations at every mth communication point, exactly as a real digital controller would. The sample rate is (NN – 1)/(m * TMAX) = 1/TS. 6 Reference 1 shows that the digital PID-controller has the z-transfer function [1] KD(z–1) 1 z+1 ᎏ ᎏ G(z) ≡ KP + ᎏ 2 (KI + TS) z – 1 + TSz ≡ Az2 + Bz + C ᎏᎏ z(z–1) where KP, KI, and KD are the proportional, derivative, and integral gain parameters.

Hindmarsh, LSODE and LSODI, ACM/SIGNUM Newsletter, 15, No. 4, 1980. info References 31 15. J. D. Lambert, Numerical Methods for Ordinary Differential Equations: The Initial-Value Problem, Wiley, New York, 1991. 16. W. E. Schiesser, A Comparative Study of Merson-type Runge-Kutta Algorithms, Report, Chemical Engineering Department, Lehigh University, Bethlehem, PA, 1980. 17. L. F. Shampine and H. A. Watts, Software for Ordinary Differential Equations, in Mathematical Software (L. R. ), Prentice-Hall, Englewood Cliffs, NJ, 1984.

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