Advanced Dynamic-System Simulation: Model Replication and by Granino A. Korn

By Granino A. Korn

A distinct, hands-on advisor to interactive modeling and simulation of engineering systems

This booklet describes complex, state-of-the-art options for dynamic approach simulation utilizing the will modeling/simulation software program package deal. It deals distinct counsel on how you can enforce the software program, supplying scientists and engineers with robust instruments for developing simulation situations and experiments for such dynamic structures as aerospace automobiles, keep an eye on platforms, or organic systems.

Along with new chapters on neural networks, Advanced Dynamic-System Simulation, moment Edition revamps and updates all of the 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 process for difference-equation modeling
  • Covers runtime vector compilation for quick version replication on a private computer
  • Discusses parameter-influence experiences, introducing very quick vectorized facts computation
  • Highlights Monte Carlo reports of the consequences of noise and production tolerances for control-system modeling
  • Demonstrates quickly, compact vector types of neural networks for regulate 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 great advisor for college kids utilizing wish.

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Extra resources for Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies

Example text

But reset and drunr do not reset difference-equation or sampled-data state variables. You must explicitly reset them in your experiment-protocol script, perhaps with a named procedure collecting all such reset operations. TWO MIXED CONTINUOUS/SAMPLED-DATA SYSTEMS 2-6. Guided Torpedo with Digital Control As a simple example, Fig. 2-3 shows how the guided-torpedo program of Sec. 1-16 can be modified to incorporate digital control. The controller operations error = (phi − psi) * swtch(dd − DD) gain = gain0 + 800 * t rudder = −rumax * sat(gain * error) now become sampled-data assignments6 preceded by a SAMPLE m statement at the end of the DYNAMIC program segment.

2-7 the Desire library function round(q) is used to quantize a sine wave with the assignment y = a * round(x/a) where a is the quantization interval. The error y − x caused by signal quantization is the quantization noise. round(q) returns floating-point numbers rounded to the nearest integer value, not integers. round(q) is not continuous; it is a switched step function that must follow a step, OUT, or SAMPLE m statement at the end of a DYNAMIC program segment. round(x) can also implement rounding in experimentprotocol scripts.

0 x,y,xt,yt Longitudiual axis v u γ2 Velocity phi FIGURE 1-9a. Guided torpedo tracking a constant-speed target. The target angle psi, not shown here, is the angle between the horizontal line and the line joining the torpedo and the target. 3 rudder×2,err×40,DD×10,phi×2 vs. t FIGURE 1-9b. Time histories of the torpedo rudder deflection, the error phi-psi, the angle phi, and the squared distance dd to the target (see the text). We terminate the run when the torpedo gets close to the target, where psi tends to change rapidly.

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