Switched Controller Design for Robotic Manipulator via Neural Network-Based Sliding Mode Approach

In this brief, a novel adaptive switched controller of uncertain industry robotic manipulator (IRM) systems with switching loads is designed for trajectory tracking by utilizing switching neural network-based sliding mode control (SNNSMC) scheme. However, the error accumulation and system instability of the IRM may be caused by switching loads, while the existing single adaptive controller may not effectively deal with this issue. To do so, the IRM with switching loads is modeled as a switched system with multi-modal. Besides, the individual sub-controller with adaptive gain algorithm is devised to replace the single adaptive controller with fixed-gain for multi-modality system. Then, the radial basis function NN (RBFNN) is utilized for approaching to the plant, which avoids the limitation of the accurate model for the IRM system with switching loads. The SMC gain is designed as an adaptive adjustment value, which can be adjusted in real time to enhance the robustness of the system in spite of unknown disturbances and uncertainties. Subsequently, based on average dwell time (ADT) principle, the trajectory tracking error being close to zero is verified by the multi-Lyapunov function method. Finally, simulation results show that the provided method can both track the preset trajectory accurately, and attenuate chattering and high-speed switching of the plant effectively.