APU Control Parameter Optimization by Multiple-Strategies Particle Swarm Optimization Algorithm
In order to improve the dynamic performance of the auxiliary power unit(APU)in the switching process of the work point,a multiple-strategies particle swarm optimization algorithm was proposed to op-timize the control parameters of the fuzzy PID.This algorithm introduced probability adjustable interfer-ence strategy,chaos search strategy,inertia weight adaptive and learning factor linear adjustment strategy to the conventional iterative process of particle swarm optimization algorithm,making it more suitable for optimizing the fuzzy PID control parameters with 187 dimensions.The APU simulation model was built based on the research on the value of two key parameters of the algorithm,namely,enhancing the global search capability p and enhancing the local search capability pp.The simulation results showed that the dynamic performance of the fuzzy PID control parameters optimized by multiple-strategies particle swarm optimization algorithm was greatly improved compared with the classical PID control parameters in terms of speed change,torque change and throttle opening change.
Auxiliary power unitFuzzy PIDParameter optimizationParticle swarm optimizationAPU simulation
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