—Recently, many technological improvement apply in the discovery of various designs of haptic devices. Several mechanism structures including serial, parallel, and hybrid-kinematic manipulators can be considered for making a haptics device. The most successful haptic mechanisms are parallel-type, because of low moving inertia, large force reflection, and high stiffness. This research shows the 6-DOF parallel haptics device based on the parallel mechanism using a translation driver motor mounted on each leg. Firstly, we introduce a 6-DOF parallel mechanism using a translation driver motor haptics device model. Due to the unsure parameters, we focus on solving the mathematics model with the nonlinear parameters of the 6-DOF parallel mechanism. Secondly, to fix the kinematics and dynamics nonlinear uncertainties parameters, the SMCNN controller for 6-DOF parallel mechanism application using a translation driver motor is designed. The Sliding model control base on artificial intelligence neural network is used to calculate the unsure factors. In this technique, to prove the stability of the system the Lyapunop theory is used. Finally, the authors the simulation results of two control algorithms with different uncertain components are presented and comparing them to demonstrate the effectiveness of the new control method. The control method is demonstrated by way of implementing the set of rules in artificial surroundings with realistic parameters, in which the received consequences are fairly promising. The obtained from SMCNN algorithm results are highly promising and accurate.

This research the authors design the intelligent control for 3-DOFs lower limb rehabilitation robot base on the complex dynamics equation. The Force Feed-Forward Method (FFM) is promote to control the of 3-DOFs lower limb rehabilitation robot including dynamics characteristics. The robot can sense the force of the therapist which exerted on the robot and patient's leg, then produces necessary forces through joints at the hip, knee, and ankle. The force feedforward controller is used to compensate the force generated by the therapist to perform patient-active exercises. In this paper, firstly authors briefly introduce 3-DOFs lower limb rehabilitation robot, next the kinematics and dynamics equation of 3-DOFs lower limb rehabilitation robot established base on Lagrange-Euler method are presented, and then the control method is introduced. Last, the performance of the proposed control methods has been confirmed by numerical simulations of the robot in all three joints: hip, knee, and ankle.

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4-DOF car motion simulator helps to simulate real-life experiences that drivers do not have the opportunity to access the real environment. The dynamics equation of 4-DOF car motion simulator is a very complex problem with many uncertain parameters, so it requires intelligent control algorithms. Sliding mode controller (SMC) can achieve good tracking performance and robustness to the disturbances, but SMC has worse stability and reliability than PID controller. As a most widely used controller, PID controller has many obvious advantages, but it has poorer tracking performance than SMC. In this paper, a control method of sliding mode type PID controllers is proposed to fully combine the advantages of the two controllers. In this study, based on the dynamics equation of 4-DOF car motion simulators the author develop two algorithms to control sliding mode control type PID (SMC-type-PID) and sliding mode controller type PID with GA optimization for 4-DOF car motion simulator. Firstly, the authors used Lyapunop theory to prove the stability of the system, next presenting the simulation results of two control algorithms with different uncertain components and comparing them to find and demonstrate the effectiveness of the new control method applied to the 4-DOF car motion simulator.