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Approximation-Based Control of an Uncertain Robot with Output Constraints

Abstract In this paper, we consider the problem of tracking a desired trajectory for an uncertain robot in the presence of constraints and uncertainties. The dynamics of the uncertain robot are represented by an n-link rigid robotic manipulator. To deal with the system uncertainties and disturbances, the adaptive neural networks are used to approximate the unknown model of the robot and handle the unknown disturbance. Uniform ultimate boundedness of the closed loop system is guaranteed by using a Barrier Lyapunov Function (BLF). Extensive simulations for a robot with constraints are carried out to illustrate the effectiveness of the proposed control.