Abstract
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.
Highlights
IntroductionMany researchers have focused on developing high-performance machines with capabilities comparable to humans, especially with respect to motion, safety, as well as energy efficiency
Nowadays, many researchers have focused on developing high-performance machines with capabilities comparable to humans, especially with respect to motion, safety, as well as energy efficiency.From the analysis of human and animal behaviors, it was found that the adaptable compliance and variable stiffness play important roles
It is a kind of variable stiffness system, which consists of two actuators where one regulates the equilibrium position of the variable stiffness series elastic system (VSSES), and the other adjusts its stiffness
Summary
Many researchers have focused on developing high-performance machines with capabilities comparable to humans, especially with respect to motion, safety, as well as energy efficiency. Sliding mode controls [20,21,22,23] with radial basis function neural networks (RBFNNs) were used to approximate the unknown nonlinearities and the upper bound of the estimated disturbances of a three-phase shunt active power filter by simulations. These approximations did not require any knowledge of the uncertainties, their adaptive laws are sensitive to external disturbances and measurement noise [24]. The appendixes present the definitions of the matrices, vectors, and functions
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