A novel model predictive control strategy for constrained and unconstrained systems in presence of disturbance. (3rd January 2019) - Zamani, Mohammad Reza; Rahmani, Zahra; Rezaie, Behrooz
Abstract: In this paper, a novel model predictive control (MPC) strategy is proposed for a constrained and unconstrained linearized system. Contrary to conventional MPC algorithm, which uses only predicted information, this strategy uses both predicted data and past knowledge of the process to obtain control input. In fact, at each sampling instant, the combination of all elements of control sequence with weighting factors along with previous information of the process are applied to the plant as control input. These weighting factors are accessible by solving a quadratic optimization problem in order to minimize the error signal and provide the optimum values of factors. The convergence to the reference trajectory for constrained and unconstrained linearized system is proven and zero tracking error is achieved via this strategy. The effectiveness of this algorithm is investigated by simulation and the results show the superior performance of the proposed method in presence of disturbance.