RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator

An electrohydraulic elastic manipulator (EEM) is a kind of variable stiffness system (VSS). The equilibrium position and stiffness controller are the two main problems which must be considered in the VSS. When the system stiffness is changed for a specific application, the system dynamics are significantly altered, which is a challenge in controlling equilibrium position. This paper presents adaptive robust control for controlling the equilibrium position of the EEM under the presence of the variable stiffness. The proposed control includes sliding mode controls (SMCs), radial basis function neural network (RBFNN), and backstepping technique. The RBFNN is employed to compensate for the uncertainties and the variant stiffness in mechanical dynamics and hydraulic dynamics. The Lyapunov approach and projection algorithm are used to derive the adaptive laws of the RBFNN and to prove the stability and robustness of the entire EEM. Finally, some experiments are implemented and compared with other controllers to prove the effectiveness of the proposed method with the variant stiffness.