Optimal trajectory tracking control for a class of nonlinear nonaffine systems via generalized N‐step value gradient learning

Summary In this paper, the tracking control problem of unknown nonlinear systems is solved by using the generalized N‐step value gradient learning algorithm with parameter [GNSVGL()]. The GNSVGL() algorithm can provide optimal tracking decisions faster than traditional ones. Initialized by different positive semi‐definite functions, the monotonicity and convergence properties of the proposed algorithm are proven. Under some conditions, the stability analysis of the value‐iteration‐based algorithm is provided. The one‐return and ‐return critic neural networks are constructed to approximate the gradient of the one‐return and ‐return cost functions. The action neural network is employed to approximate the control law of the error system. It is emphasized that one‐return and ‐return critic networks are combined to train the action neural network. Finally, via conducting simulation studies and comparisons, the excellent tracking performance of the proposed algorithm is confirmed.