Output Feedback Neural Network Adaptive Robust Control of a Class of SISO Nonlinear Systems *

Through the use of high-gain observer to estimate the unmeasurable system states, neural networks (NNs) and adaptive robust control (ARC) method are integrated to design performance oriented output feedback control laws for a class of single-input-single-output (SISO) n-th order nonlinear systems in normal form. Multi-layer neural networks (MLNNs) with the estimated states as inputs are used to approximate all unknown but repeat-able nonlinear functions in the system. A controlled learning is achieved through the use of discontinuous projections with fictitious bounds in the tuning laws for NN weights. Certain robust control terms are con-structed to effectively attenuate various model uncer-tainties and estimation errors for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, asymptotic output tracking is achieved in the ideal case. Experi-mental results on the output feedback control of a linear motor drive system are obtained to illustrate the pro-posed algorithm.