Modal Space Neural Network Compensation Control for Gough-Stewart Robot with Uncertain Load

Abstract This paper investigates the motion control of a Stewart parallel robot with uncertain load. The external disturbances caused by load can trigger dynamic coupling among six degree-of-freedoms and have a significant impact on control precision. In practice, the dynamic parameters of load is difficult to be precedent identified and the robot with load is a multi-input multi-output(MIMO) system in physical space, which makes the dynamic coupling problem difficult to solve effectively and the high-performance control is hard to achieve. In this paper, the dynamic effect of the load is first analyzed. Then a novel motion control method, modal space neural network compensation control, is designed with the aim of reducing the effect of the load disturbances. This controller implements the neural network to compensate for the load disturbances. In addition, the modal space control theory is introduced to realize the independent control of each control channel. The feasibility of the proposed controller is evaluated in numerical simulations. Results reveal that the proposed control method gives exceptional tracking performance and dynamic coupling suppression capability.