Chattering-Free Neuro-Sliding Mode Control of 2-DOF Planar Parallel Manipulators

Abstract

This paper proposes a novel chattering free neuro‐sliding mode controller for the trajectory tracking control of two degrees of freedom (DOF) parallel manipulators which have a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. A feedforward neural network (NN) is combined with an error estimator to completely compensate the large nonlinear uncertainties and external disturbances of the parallel manipulators. The online weight tuning algorithms of the NN and the structure of the error estimator are derived with the strict theoretical stability proof of the Lyapunov theorem. The upper bound of uncertainties and the upper bound of the approximation errors are not required to be known in advance in order to guarantee the stability of the closed‐loop system. The example simulation results show the effectiveness of the proposed control strategy for the tracking control of a 2‐DOF parallel manipulator. It results in its being chattering‐free, very small tracking errors and its robustness against uncertainties and external disturbances

Synchronization Full-Order Terminal Sliding Mode Control for an Uncertain 3-DOF Planar Parallel Robotic Manipulator

“…The SMC was suggested in References [61,62], which was stated as follows. We select the sliding manifold and take its time derivative as follows:…”

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

“…Various approaches have been proposed to deal with this problem. As stated in [15,36,38], most of these can be classified into two main categories: the boundary layer saturation method and the estimated uncertainty method. In the boundary layer saturation method, the discontinuous part is replaced by a saturation function with a small neighborhood switching surface.…”

“…In our study, an adaptive chattering free neural network based sliding mode control (ACFN-SMC) method is proposed for tracking trajectories of redundant parallel manipulators. There are some similar approaches in the literature [32][33][34][35][36][37][38]. In [36], a fuzzy adaptive supervisory controller is employed in a fuzzy sliding mode control scheme to reduce the chattering of a 4-degree-of-freedom (DOF) parallel manipulator.…”

“…However, it neglects possible external disturbances or unmodeled dynamics. A novel chattering free neuro-based sliding mode controller for 5-bars parallel manipulator is proposed in [38]. In the study, uncertainties are considered but the controller design is model-dependent.…”

A neural network–based synchronized computed torque controller for three degree-of-freedom planar parallel manipulators with uncertainties compensation

“…The first one is model-free control approach, such as proportional-derivative (PD) controller, 1,2 nonlinear PD controller, 3,4 and adaptive switching learning PD control method. 5 The other one is model-based control approach, such as computed torque controllers, [6][7][8][9][10] the model-based iterative learning controller, 11 sliding mode controllers, [12][13][14][15][16] and the adaptive controllers. [17][18][19][20][21] The common characteristic of these approaches is that only local feedback information of the controlled joint is fed to the control loop of each individual actuator.…”