Abstract
This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.
Highlights
In the past decades, there have been enormous research efforts in the development of efficient control schemes for robot manipulators [1,2,3]
This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer
In [60], a hybrid fuzzy adaptive output feedback control design was proposed for uncertain multi-input and multioutput (MIMO) nonlinear systems with time-varying delays and input saturation
Summary
There have been enormous research efforts in the development of efficient control schemes for robot manipulators [1,2,3]. The robust tracking control schemes should be further developed for the robot manipulator to manage uncertainties, disturbances, nonlinearities, input constraints, and their coupling effects. Fuzzy saturated output feedback tracking control was proposed for robot manipulators using a singular perturbation theory based approach in [56]. In [60], a hybrid fuzzy adaptive output feedback control design was proposed for uncertain MIMO nonlinear systems with time-varying delays and input saturation. The adaptive neural output feedback control scheme will be developed by using the RBFNN and disturbance observer for uncertain robot manipulators with input saturation based on backstepping technique. This work is motivated by the disturbance observer-based adaptive neural output feedback control to follow desired time-varying trajectories of robot manipulators.
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