Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

Abstract

Robot manipulators are now used in various domains and environments, where they can be subjected to random vibrations. Random vibrations mainly affect the torque control signal, and a torque controller is therefore required to be designed for stabilization purposes. However, for security or intellectual property protection reasons, most commercialized robots are manufactured with unknown and inaccessible torque controller interface such that the user can only design a position/velocity controller. This paper proposes an adaptive task-space velocity controller free from the inner controller's structure and exhibiting stochastic and deterministic disturbances rejection to deal with these issues. To deal with the unknown inner controller, the paper exploits the fact that most torque controllers use a velocity feedback term, and it considers the other terms as an unknown functions vector. To cope with random disturbances, it is demonstrated that the random excitation matrix can be linearly parameterized, and therefore, a direct adaptive method is constructed. Using radial basis function neural network (RBF NN), an indirect adaptive method is developed to cope with deterministic uncertainties. Through Lyapunov theory, the paper proves that all the closed-loop signals are bounded in probability. The effectiveness of the proposed approach is further demonstrated through simulation comparisons.INDEX TERMS Robot manipulators, Adaptive task-space control, Closed inner controller, Random vibrations, Neural networks.

Model‐free prescribed performance fast nonsingular terminal sliding mode control with practical finite‐time stability of uncertain robot manipulators

“…m 1 , m 2 , p 1 and p 2 are the same as mentioned in Section 2. 0 < 𝛿 i < FIGURE 4 The proposed sliding surface on the phase plane.…”

“…Generally, motion control of robot manipulators is still a difficult task because of modeling uncertainties, nonlinearities and external disturbances in system. In order to meet the requirements of high performance tracking control, lots of advanced control methods have been proposed and applied to robot manipulators, such as adaptive control [4,5], sliding mode control [6][7][8][9], time delay control [10] and model predictive control [11], etc.…”