Dual-layer fuzzy control architecture for the CAS rover arm

Abstract

Since the conventional impedance control method for a rover arm is not suitable for unconstructed environment with uncertainties, a fuzzy inference method which improves the impedance model dynamically is introduced to realize high-precision control. The fuzzy PD control algorithm which applies to the joint control of a rover arm is analyzed in this paper. With the two level control algorithms, a novel dual-layer fuzzy control framework is proposed, which can enhance the control performance significantly. In order to verify the validity and reliability of the designed algorithms, the robotic arm of the CAS rover is considered as an experimental platform. Kinematics and dynamics models of robotic arm are derived at first. Moreover, the fuzzy inference mechanism and implementation process of impedance model parameters are illustrated. Extensive simulations and experimental results show that the control accuracy and the force control of the system have been significantly improved with the proposed dual-layer fuzzy control architecture.

Attitude control for astronaut assisted robot in the space station

“…Zou et al used terminal sliding mode and Chebyshev neural network to solve the finite-time attitude tracking control for spacecraft [20]. Gao et al used fuzzy algorithm to control the CAS rover arm [21]. Liu and Li used the adaptive neural fuzzy control for mobile manipulators [22].…”