Swarm Optimization Applied to Engine RPM Control

Optimization for control system design or testing is commonly used. Most of the optimization approaches are based on simplex or gradient descent. If the system is complex these approaches are susceptible to being caught in local minimums. Particle swarm optimization (PSO) is a subset of evolutionary computation, which includes genetic algorithms. Evolutionary search techniques have been introduced as a means of detecting global minimums within a parameter range. PSO has been presented by a number of researchers, with applications in function optimization and neural network training. In this study PSO theory and equations will be detailed. The procedure will be applied to an engine rpm control system and results will be presented. The optimization procedure is used to minimize cumulative error and select parameters for a lead-lag plus integral control system. The simulation was coded in simulink and is shown in the figures.