Nonlinear model predictive control of Stewart platform 6 dof

This paper presents a nonlinear predictive approach, for Stewart platform (6 degrees of freedom). The optimal control is computed directly from the minimization of receding horizon cost function with offline optimization. The main purpose of this research is designing the predictive controller for Stewart platform. In this study, the kinematics and dynamics of Stewart robot is introduced, considering the dynamics of actuators. Following the introduction of nonlinear model predictive control will be discussed and according to robot dynamics, controller will be design. In addition, given the various uncertainties, robot dynamic equation could be rewritten. The controller is designed according to these uncertainties and then stability control is confirmed using Lyapunov theory. Due to the limited engine power and the output torque electric drive in practice, the proposed controller manages Stewart platform in such a way that it could track the desired trajectory well. To review the proposed method at the end of the study, Stewart platform is simulated and the control method proposed in this paper was compared with computed torque control (CTC) method, sliding mode control and Proportional-Integrator-Differentiation (PID) controller.