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We address the problem of environmental contour line tracking for a class of autonomous vehicles. A reference velocity is designed for the autonomous vehicles to do contour line tracking. Based on Lashall invariance principle, an ideal controller is designed for the vehicle with ideal model and ideal information about the environmental concentration function to track the desired contour line. For the vehicle with possibly modeling uncertainty, we combine a neural controller containing a wavelet neural network (WNN) identifier with a robust control to construct a robust adaptive WNN control for the vehicle to track the desired environmental contour line. Then we give theoretical proof of the efficiency of the designed robust adaptive WNN control. Simulation results and conclusion are presented and discussed.

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.