Abstract: Adaptive inverse control can eliminate the disturbance of the system and control the performance of dynamic response independently,and the performance of the adaptive inverse control system depends on the accuracy of the system object,the inverse object and the controller identification model.In this paper,the dynamic functional link neural network was proposed to realize the simultaneous on-line modeling of the adaptive inverse control system object and inverse object,and realize the off-line modeling of the controller,and the identification of model parameters was transformed into optimization of spatial parameters.Aiming at the destruction of convergent population structure by chaos initialization,this paper presented variable parameter chaotic particle swarm optimization algorithm to optimize the weights of the neural network.Through the simulation experiment,we can see that the modeling error based on the dynamic function link neural network is small and the identification accuracy based on the dynamic function link neural network is high.Compared with the current reference model adaptive control methods,the method in this paper can achieve better disturbance cancellation effect and improve the tracking response performance of the system,thus verifying the effectiveness and feasibility of the method.

Key words: Adaptive inverse control,Disturbance elimination,System identification,Dynamic function link neural network,Variable parameter,Chaos particle swarm optimization algorithm