Prescribed Performance Control of Euler-Lagrange System with Unknown Dead-zone and Uncertain Disturbances

In this paper, the full state feedback tracking control problem for Euler-Lagrange system with unknown dead-zone and uncertain disturbances is considered and an adaptive neural-network-based prescribed performance control algorithm is proposed. The performance function is employed to constrain tracking errors and describe the expected region of transient behavior. Radial basis function neural network (RBFNN) is utilized to approximate unknown nonlinearities. The proposed controller is able to track the desired trajectory, guarantee transient performance and compensate for the estimated dead-zone effect. Lyapunov direct method is utilized to ensure asymptotical stability of the closed-loop system. Simulation example is presented to illustrate the effectiveness and feasibility of the proposed algorithm.

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