Adaptive robust controller using intelligent uncertainty observer for mechanical systems under non-holonomic reference trajectories

Non-holonomic reference trajectories and uncertainties are typically encountered in a class of mechanical systems. For such systems, this paper investigates the development of a novel explicit adaptive robust controller. By employing the structure of the Udwadia controller, the designed controller can deal with holonomic and non-holonomic reference trajectories in a unified manner. To avoid degradation of performance due to uncertainties, an observer is proposed to identify the uncertainties; the observer is designed using a fuzzy cerebellar model articulation controller neural network. A robust term is designed to restrain the initial deviations and to enhance the robustness of systems. Moreover, a compensatory term is designed to compensate for the residual errors resulted from the uncertainty observer. Rigorous theoretical analysis of the proposed controller is verified via the Lyapunov stability method, and an illustrative example is presented to demonstrate the effectiveness of the designed controller.