Parallel stochastic robustness synthesis for control system design

Author

Schubert, Wolfgang M. ; Stengel, Robert F.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Princeton Univ., NJ, USA

Volume

6

fYear

1995

fDate

21-23 Jun 1995

Firstpage

4429

Abstract

Stochastic robustness synthesis is used to evaluate compensator robustness numerically and to automate the design of stochastic optimal controllers. Monte Carlo simulation (MCS) is applied to quantify robustness, and a genetic algorithm (GA) searches for the optimal controller. The overall algorithm is computationally expensive, and parallel computing is utilized to reduce execution times. Parallel stochastic robustness analysis and design (PSRAD) is introduced as a viable solution for real-time controller design. A dynamic scheduler is proposed to alleviate stochastic load imbalances. Results are presented for a shared-virtual-memory computer

Keywords

Monte Carlo methods; control system CAD; control system analysis computing; genetic algorithms; optimal control; parallel algorithms; robust control; stochastic systems; Monte Carlo simulation; compensator robustness; control system design; dynamic scheduler; genetic algorithm; parallel stochastic robustness synthesis; real-time controller design; shared-virtual-memory computer; stochastic load imbalance; stochastic optimal controllers; Automatic control; Concurrent computing; Control system synthesis; Genetic algorithms; Optimal control; Parallel processing; Processor scheduling; Robust control; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, Proceedings of the 1995

Conference_Location

Seattle, WA

Print_ISBN

0-7803-2445-5

Type

conf

DOI

10.1109/ACC.1995.532774

Filename

532774