This paper addresses the robust switching tracking neural control problem for a robotic manipulator in the presence of uncertainties and disturbances. The proposed controller, which is a combination of a robust adaptive control technique, radial basis function neural network approximation and average dwell-time technique, can guarantee position tracking performance of robotic manipulator system, in the sense that all variables of the resulting closed-loop system are bounded and the H∞ disturbance attenuation level is well obtained. Simulation results on a two-link robotic manipulator show the satisfactory tracking performance of the proposed control scheme even in the presence of large modelling uncertainties and external disturbances by comparing it with PD control strategy.
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