Adaptive Robust Quadratic Stabilization Tracking Control for Robotic System with Uncertainties and External Disturbances

Abstract

An adaptive robust quadratic stabilization tracking controller with hybrid scheme is proposed for robotic system with uncertainties and external disturbances. The hybrid scheme combines computed torque controller (CTC) with an adaptive robust compensator, in which variable structure control (VSC) andH∞optimal control approaches are adopted. The uncertain robot manipulator is mainly controlled by CTC, the VSC is used to eliminate the effect of the uncertainties and ensure global stability, andH∞approach is designed to achieve a certain tracking performance of closed-loop system. A quadratic stability approach, which allows separate treatment of parametric uncertainties, is used to reduce the conservatism of the conventional robust control approach. It can be also guaranteed that all signals in closed-loop system are bounded. The validity of the proposed control scheme is shown by computer simulation of a two-link robotic manipulator.

Robust Tracking Control for Robotic Manipulator via Fuzzy Logic System andH∞Approaches

“…In some publications like [9], it is clearly unrealistic that only Coriolis terms, gravity terms, and friction terms may be uncertain, whereas inertia matrix was assumed to be known exactly. Literatures [5,6,17] assumed that the nominal system is completely known, which is controlled using computed torque method. Aiming at the uncertain system, a fuzzy or neural controller acts as compensator for computed torque method.…”

“…To verify the theoretical results, simulations are carried out in two degrees of freedom robot manipulator as shown in Figure 3 described by [6,17] ( ) = [ ] ,…”

“…Tracking control of robotic manipulator is always a challenging problem in control fields due to its uncertainties, disturbances, and nonlinear dynamics [1,2]. Over the past decades, various control approaches have been applied for controlling the challenging robotic manipulator systems, such as PID control [3], computed torque control (CTC) method [4][5][6], variable structure control [7][8][9], fuzzy control [5,[10][11][12], and neural networks control [13][14][15][16][17][18].…”

“…For example, in [13,15], neural networks were utilized to approximate the equivalent control of variable structure control entirely, and then the ∞ techniques were applied to achieve certain tracking performance. As a feedback linearization control technique, CTC was usually included in controller designing for complex robotic system due to its good performances [5,6,16,17]. In order to eliminate the effect of the uncertainties, Song et al [5] proposed an approach of CTC plus fuzzy compensator for robotic system; the nominal system was controlled by using CTC method and for uncertain system a fuzzy controller acts as compensator.…”

“…Chen et al [20] proposed two types of adaptive control scheme combining conventional CTC and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Recently, Peng and Liu [6] proposed an adaptive robust quadratic stabilization tracking control scheme that combines CTC, nonlinear ∞ control, and variable structure control for robotic manipulator. Peng et al [21] proposed a fuzzy adaptive output feedback control scheme based on fuzzy adaptive observer for robotic systems with parameter uncertainties and external disturbances.…”