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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

The control of a parallel robotic manipulator with uncertain dynamics is a noteworthy challenge due to the complicated dynamic model; multi-closed-loop chains; and singularities. This study develops a Synchronization Full-Order Terminal Sliding Mode Control (S-FOTSMC) for a 3-DOF planar parallel robotic manipulator with uncertain dynamics. First, to achieve faster convergence of position error and synchronization error variables with minimum values at the same time, a Synchronization Full-Order Terminal Sliding Mode Surface (S-FOTSMS) is constructed in the cross-coupling error’s state space. Next; an integral of the switching control term is applied; that means, a continuous control term is extended for rejecting the effects of chattering. Finally, an SFOTSMC is designed to guarantee that sliding mode motion will occur. Consequently, the stability and the robustness of the proposed method are secured with high-performance irrespective of the influences of uncertain terms in the robot system. The simulation performances show the effectiveness of our proposed system for position tracking control of a 3-DOF planar parallel robotic manipulator.

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This paper comes up with a novel Fast Terminal Sliding Mode Control (FTSMC) for robot manipulators. First, to enhance the response, fast convergence time, against uncertainties, and accuracy of the tracking position, the novel Fast Terminal Sliding Mode Manifold (FTSMM) is developed. Then, a Supper-Twisting Control Law (STCL) is applied to combat the unknown nonlinear functions in the control system. By using this technique, the exterior disturbances and uncertain dynamics are compensated more rapidly and more correctly with the smooth control torque. Finally, the proposed controller is launched from the proposed sliding mode manifold and the STCL to provide the desired performance. Consequently, the stabilization and robustness criteria are guaranteed in the designed system with high-performance and limited chattering. The proposed controller runs without a precise dynamic model, even in the presence of uncertain components. The numerical examples are simulated to evaluate the effectiveness of the proposed control method for trajectory tracking control of a 3-Degrees of Freedom (DOF) robotic manipulator.

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This study presents a new adaptive synchronized computed torque control algorithm based on neural networks for three degree-of-freedom planar parallel manipulators. The basic idea of the proposed control algorithm is to use the incorporation of cross-coupling errors of active joints with the adaptive computed torque control algorithm, online self-tuned neural networks, and error compensators. The key to the success of the proposed approach is to improve the trajectory tracking accuracy of the parallel manipulator's end-effector while driving the synchronization errors among active joints to zero. The uncertainties of the control system such as modeling errors, frictional terms, and external disturbances are adaptively compensated online during the trajectory tracking of the parallel manipulator. Using the Lyapunov theory, it is proved that the tracking errors and error rates of the overall system asymptotically converge to zero. To demonstrate the effectiveness of the proposed control algorithm, compared simulations are conducted using MATLAB/Simulink [version 2013a] combined with Solidworks 2014.

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