Abstract
Parallel manipulators possess the advantages of being compact structure, high stiffness, stability and high accuracy, so such parallel manipulators have been widely employed in application fields as diverse as parallel kinematic machine, motion simulator platform, medical rehabilitation device, and so on. Due to the complexity of the closed-loop structural system, an accurate dynamic model is very difficult to be derived in the absence of some uncertainties parameters and external disturbances. In order to improve the trajectory tracking accuracy with time-varying and nonlinear parameters, this paper addresses the design and implement of adaptive fuzzy sliding mode control (AFSMC) for a three-degree-of-freedom (DOF) parallel manipulator, where internal force term can be linearly separated into a regression matrix and a parameter vector that contains the estimated errors. Furthermore, fuzzy inference unit is utilized to modify the gain parameters in real-time by using the state feedback from the task space and the adaptive law is performed to update uncertainties in dynamic parameters. The proposed controller is deduced in the sense of Lyapunov theory to guarantee the stability while improving the trajectory tracking performance. Finally, simulation experiment results demonstrate that the proposed control method is insensitive to uncertainties and disturbances and permits to decrease the requirement for the bound of these uncertainties, which validate the effectiveness of the developed control method and exhibit good trajectory tracking performance compared with sliding mode control (SMC) and fuzzy sliding model control (FSMC).
Highlights
Parallel manipulators are widely believed to be an interdisciplinary research area which becomes an outstanding field attracting more and more attention from both industrial and academic domains
A large number of new architectures dealing with the kinematics and dynamics of parallel manipulators have been proposed in academic [1, 2], while some of them have been implemented to practical applications, e.g., parallel kinematic machine [3], motion simulator platform [4], surgical medical equipment [5], dexterous hands [6], etc
E main contribution of this paper is to propose an alternative adaptive fuzzy sliding mode control algorithm for a 3-DOF redundantly actuated parallel manipulator. e control algorithm combines the advantages of conventional sliding mode controller and fuzzy logic controller. e stability of the control system is theoretically proven in terms of Lyapunov method, and the tracking errors can converge to zero asymptotically. e parameters uncertainties of the control system are compensated by the adaptive control law and the unknown disturbances are rejected by the fuzzy self-tuning robust control law. e simulation experiment results demonstrate that the developed controller can exhibit excellent tracking performance compared with the two other controllers
Summary
Parallel manipulators are widely believed to be an interdisciplinary research area which becomes an outstanding field attracting more and more attention from both industrial and academic domains. To overcome the aforementioned problems in the design of SMC, fuzzy sliding mode control is proposed in [20] as a model-free controller scheme, which is an alternative effective approach to control such system for its advantage of independent on a precise mathematical model, as well as good robustness and nonlinear characteristics. Qi et al [26] proposed a fuzzy adaptive supervisory controller (FASC) for the 4-DOF parallel manipulator to reduce the chattering, and the comparative analysis with the sliding surface control (SMC) indicated that the improved control can reduce greatly the chattering phenomenon and possess good robustness against the parameters uncertainties and external disturbances. Filabi et al [27] proposed a fuzzy adaptive sliding mode controller for trajectory tracking with considering dynamics of electromechanical actuators, and several simulation cases demonstrated that the proposed control strategy can achieve favorable control performance with regard to uncertainties, nonlinearities, and external disturbances.
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