Accelerated neuroadaptive tracking control of strict-feedback nonlinear systems without precise knowledge of target trajectory

The problem of tracking control under uncertain desired trajectory is interesting but nontrivial. The problem is even more challenging if the system under consideration involves modeling uncertainties. This paper investigates such problem for strict-feedback nonlinear systems. By combining Fourier series with radial basis function neural networks (NNs), an analytical model is developed to reconstruct the unknown desired trajectory. Based on which, 2 neural adaptive control schemes are developed to maintain target tracking closely. The first control strategy is based on direct tuning of the NN weights, and the second strategy is built upon the concept of a virtual parameter related to NN weights, which substantially reduces the number of parameters to be online updated, rendering the strategy structurally simpler and computationally less expensive. The effectiveness of the proposed control strategy is confirmed by systematic stability analysis and numerical simulation.