MFAC Parameter Optimization Based on Adaptive Multi-Strategy Behavioral Particle Swarm Optimization

Aiming at the current difficulties in parameter tuning and optimisation of model-free adaptive control algorithms, an adaptive multi-strategy behavioural particle swarm optimisation algorithm is proposed to optimise the controller parameters. The algorithm makes the particle evolution autonomous through the multi-strategy behavioural selection probabilistic algorithm. The algorithm also adopts a linear reduction strategy to adjust the inertia weights and acceleration factors, and realises the automatic optimisation of the model-free adaptive control parameters 11, 1.1,. P and A. The simulation is carried out with a typical nonlinear system as the control object. The results show that the improved particle swarm optimization algorithm has better optimization speed and accuracy; after the control with optimized parameters, the amount of overshooting is greatly reduced, and the oscillation phenomenon that occurs when the expected output changes in steps or the system model is changed is effectively overcome, so that the system has stronger anti-interference and adaptivity.