Parallel stochastic robustness synthesis for control system design

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.