In this paper, an overall structure with the asymmetric constrained controller is constructed for human–robot interaction in uncertain environments. The control structure consists of two decoupling loops. In the outer loop, a discrete output feedback adaptive dynamics programing (OPFB ADP) algorithm is proposed to deal with the problems of unknown environment dynamic and unobservable environment position. Besides, a discount factor is added to the discrete OPFB ADP algorithm to improve the convergence speed. In the inner loop, a constrained controller is developed on the basis of asymmetric barrier Lyapunov function, and a neural network method is applied to approximate the dynamic characteristics of the uncertain system model. By utilizing this controller, the robot can track the prescribed trajectory precisely within a security boundary. Simulation and experimental results demonstrate the effectiveness of the proposed controller.
