RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform

Abstract

The stewart platform is widely applied in the industry. However, Rotary Stewart Platform (RSP) has very little research for this type. Moreover, Inverse Kinematic (IK) solution in papers published previously is complex and unclear. Therefore, in this paper, first, we design and build the mathematical model and check it in Simmechnics. Second, the robust control of the RSP proposed in this paper precisely tracks a command under the platform uncertainties. The inverse kinematic solution of the platform, derived in this paper, supports for control design of the platform. Radial Basis Function (RBF) neural network adaptive sliding mode controller is used to achieve the satisfactory tracking performance and the system stability. Stability of the system is guaranteed through Lyapunov theory. The simulation is conducted to illustrate the effectiveness of the proposed control for the RSP.