Adaptive Neural Network Output Feedback Control of Nonlinear Systems with Actuator Saturation

An indirect adaptive neural network (NN) saturation compensator is presented for a class of nonlinear systems. Output feedback control is considered where only the system output is assumed to be measurable. The imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. A NN-based state observer estimates derivatives of the output and another NN-based feedback controller is inserted into a feedforward path to capture the nonlinearities of the observed system and to cancel the effects of the unknown disturbances and the unknown saturation nonlinearity. The unknown system states identified by the NN observer are inputs of the NN-based controller. Two NNs interact together to achieve the desired performance. Both adaptive, neural network control laws and on line neural net weights tuning rules are rigorously derived based on feedback linearization and Lyapunov approach. The overall robust adaptive scheme guarantees that the states estimation errors, NN weights estimation errors, and output tracking errors are uniformly ultimately bounded. The simulation conducted indicates the proposed scheme can effectively estimate the unknown nonlinear system states and accommodate the unknown actuator constraints.