Nonlinear Model Predictive Control of a Stewart Platform Based on Improved Dynamic Model

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ABSTRACT

This paper presents a nonlinear model predictive control (NMPC) for six-degree-of-freedom (DOF) Stewart Platform based on parallel mechanisms. Nowadays, NMPC has been used in many applications in industry. First, nonlinear equations related to Stewart platform dynamic are extracted using Lagrange method. The advantages of this dynamic model are improved and are highly accurate because we take into account rational velocity and acceleration of pods around their longitudinal axis and accurately model the friction of joint. In this controller, outputs are anticipated at any time. The main advantage of the proposed controller is that constraints can be applied to inputs and outputs. Another benefit is its high precision. If the weights are added to inputs and outputs, the errors of tracking will reduce in outputs. In current work, three different trajectories were used in simulation to verify the performance of designed and proposed NMPC. Also, we extracted PID controller results to compare and validate the NMPC.

KEYWORDS

Nonlinear Model Predictive Control, Stewart Platform, parallel mechanisms, Lagrange method

Cite this paper

Soheil Sheikh Ahmadi, Arash Rahmanii. (2020) Nonlinear Model Predictive Control of a Stewart Platform Based on Improved Dynamic Model. International Journal of Theoretical and Applied Mechanics, 5, 18-26