Composite direct adaptive Taylor series – fuzzy controller for the robust asymptotic tracking control of flexible-joint robots

Abstract

Recent developments in the use of Taylor series-based controllers have increased the need for utilizing Taylor series system for complex nonlinear systems. This research aims at developing a direct adaptive controller for the tracking control of electrically-driven flexible-joint robotic manipulators. In our direct adaptive method, the decentralized controller benefits by two control terms, namely a Taylor series estimator to approximate an ideal controller for the diminution of joint tracking error in the presence of lumped uncertainty and also a fuzzy estimator to compensate an approximation error. The joint tracking error and its time derivatives up to the second order are guaranteed to converge asymptotically to zero by means of the stability analysis. To obtain better control performance, the particle swarm optimization is used for selecting the control parameters. Simple and accurate design, small joint tracking error and no need of bounding functions are the main advantages of proposed control method. Finally, numerous simulation studies are included to verify the efficiency of the controller.