Research on improvement of model-free learning adaptive controller based on multi-innovation theory

To improve the convergence rate of model-free learning adaptive controller (MFLAC). A new design method of MFLAC is presented in this paper. We extend the model-free control law from signal innovation form to multi-innovation form based on the multi-innovation theory and the parameters are optimized by artificial fish swarm algorithm (AFSA).The performance analysis and simulation results show that the proposed model-free controller based on multi-innovation has faster convergence rate and better tracking performance.