Zhu, NingyuNingyuZhuXie, Wen-FangWen-FangXieDr. SHEN Henghua2026-09-102026-09-102022Zhu, N., Xie, W., & Shen, H. (2022). Adaptive sliding mode control with RBF neural network-based tuning method for parallel robot. In IEEE (Ed.). The proceedings of IECON 2022 – 48th annual conference of the IEEE industrial electronics society. IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium (pp. 1-6). IEEE.978166548025397816654802602577-16471553-572Xhttp://hdl.handle.net/20.500.11861/29384In this paper, a novel adaptive sliding mode control scheme with RBF (radial basis function) neural network-based tuning method is proposed for the trajectory tracking of a 6-RSS (Revolute-Spherical-Spherical) parallel robot in Cartesian space. Parallel robot is a highly nonlinear system with closed-chain mechanisms, which poses the major challenges to the controller design. The robust sliding mode controller is developed to deal with system uncertainties such as modeling errors, frictions, and disturbances. With strong adaptation and learning ability, RBF neural network is adopted to identify the parallel robot dynamics, and then the adaptive self-tuning of the control gains in the controller is realized, which is more flexible than manual tuning method and can guarantee the desired results of the changing system. The stability of the controller has been validated using Lyapunov theorem. Simulation results demonstrate that the proposed controller can achieve better tracking performance than the sliding mode controller with fixed control gains.enRadial Basis FunctionSliding Mode ControlNetwork-Based MethodsSliding ModeTuning MethodNeural Network-Based MethodsAdaptive Sliding Mode ControlNeural TuningParallel RobotNeural NetworkSimulation ResultsControl DesignError ModelLearning AbilityStability ControlTracking PerformanceSystematic UncertaintiesCartesian SpaceRobot DynamicsStrong Learning AbilityPositive Definite Diagonal MatrixNeural Network ControlSteepest Descent MethodTracking ErrorRoot Mean Square ErrorTracking AccuracyRotational MotionTranslational MotionController TuningEnd-Effector PoseConference Paper10.1109/IECON49645.2022.9968368