By Granino A. Korn
A specified, hands-on consultant to interactive modeling and simulation of engineering systems
This publication describes complex, state of the art recommendations for dynamic method simulation utilizing the need modeling/simulation software program package deal. It deals unique suggestions on easy methods to enforce the software program, offering scientists and engineers with robust instruments for growing simulation situations and experiments for such dynamic structures as aerospace cars, keep an eye on platforms, or organic systems.
Along with new chapters on neural networks, Advanced Dynamic-System Simulation, moment Edition revamps and updates the entire fabric, clarifying causes and including many new examples. A bundled CD comprises an industrial-strength model of OPEN hope in addition to hundreds and hundreds of application examples that readers can use of their personal experiments. the one ebook out there to illustrate version replication and Monte Carlo simulation of real-world engineering platforms, this volume:
- Presents a newly revised systematic strategy for difference-equation modeling
- Covers runtime vector compilation for speedy version replication on a private computer
- Discusses parameter-influence experiences, introducing very speedy vectorized records computation
- Highlights Monte Carlo stories of the results of noise and production tolerances for control-system modeling
- Demonstrates quickly, compact vector types of neural networks for keep an eye on engineering
- Features vectorized courses for fuzzy-set controllers, partial differential equations, and agro-ecological modeling
Advanced Dynamic-System Simulation, moment Edition is a very resource for researchers and layout engineers up to the mark and aerospace engineering, ecology, and agricultural making plans. it's also a superb advisor for college students utilizing wish.
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Extra info for Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies
T 5 10 (b) (c) FIGURE 2-2. Data exchanges between an “analog” differential-equation system and a primitive sampled-data (“digital”) system. The graphic display (a) and the output listing (b) were produced by the small DYNAMIC program segment in (c) for different values of NN and m. The program has set t0 = 0 and x(0) = 0 by default and explicitly assigned q(0) = 0. Note that the “analog” input y from the digital-to-analog converter reads the digital q-value from the preceding sampling step and is therefore always one step behind the current value of q.
Complete simulation program and stripchart display for an electrical servo with motor-ﬁeld delay, ﬁeld saturation, and sinusoidal input u = A * cos(w * t). You can also set w = 0 to obtain the servomechanism step response. The resulting motor torque is limited by motor-ﬁeld saturation represented by the soft-limiting hyperbolic-tangent function torque = maxtrq * tanh(g2 * I/maxtrq) (1-8) The response of motor, gears, and load to the torque satisﬁes the differential equations of motion (d/dt)x = xdot (d/dt)xdot = (torque − R * xdot)/M (1-9) INRODUCTION TO CONTROL-SYSTEM SIMULATION 23 where M represents the inertia of motor, gears, and load, and R > 0 is a motor damping parameter.
2-9). This simple solution of the switching problem again implies a compromise between switching-time resolution and computing speed, for no integration step can be larger than the sampling interval COMINT = TMAX/(NN − 1) (Sec. 1-9). This may be wasteful when one needs only a few switch and/or limiter operations. 2-11. Using the step Operator and Heuristic Integration-Step Control A better way to obtain correct integration of switch and limiter functions is to program all such operations following a Desire step statement placed at the end of the differential-equation- program section.
Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies by Granino A. Korn