Robust adaptive trajectory linearization control for a class of uncertain nonlinear systems

This paper presents a novel robust adaptive trajectory linearization control (RATLC) method for a class of uncertain nonlinear systems based on a single hidden layer neural networks disturbance observer (SDO). The term ldquodisturbancerdquo used in this paper refers to the combination of model uncertainties and external disturbances. By utilizing the universal approximation property of neural networks with useful information on the controlled plant, the SDO can monitor time-varying disturbance very well. A robust adaptive term is added to overcome reconstruction error. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined system. Excellent disturbance attenuation ability and strong robustness of the proposed RATLC method are demonstrated by an example of inverted pendulum control problem.