Designing an Output Feedback Tracking Controller for Mobile Manipulators by Using a Neural Adaptive Robust Technique

In this paper, the tracking control of a robotic arm mounted on a wheeled mobile platform is considered. A nonlinear neural adaptive robust control algorithm is proposed for the output feedback tracking control of a wheeled mobile manipulator without measuring system velocities to deal with the unmodeled system dynamics, parametric uncertainties and external disturbances. A Lyapunov-based stability analysis shows that tracking and observation errors are Uniformly Ultimately Bounded (UUB) and converge to a small ball containing the origin. A Radial Basis Function Neural Network (RBFNN) is employed to compensate for the uncertainties of mobile manipulator dynamics. Nonparametric uncertainties and NN approximation errors are also compensated by an adaptive robust controller. In addition, hyperbolic tangent function is employed in the design of the output feedback controller to reduce the risk of actuators saturation and to produce smoother control signals. Finally, simulation results demonstrate the effectiveness of the proposed controller well.