Approximating optimal finite horizon feedback by model predictive control

Finite horizon robust model predictive control with terminal cost constraints

In this paper, we develop a finite horizon model predictive control algorithm which is robust to modelling uncertainties. A moving average system matrix is constructed to capture modelling uncertainties and facilitate the future output prediction. The paper is mainly focused on the step tracking problem. Using linear matrix inequality techniques, the design is converted into a semi-definite opt...

Finite Horizon Robust Model Predictive Control Using Linear Matrix Inequalities

In this paper, we develop a finite horizon model predictive control algorithm which is robust to model uncertainties. A moving average system matrix is constructed to capture model uncertainties and facilitate future output predictions. The paper is focused on step tracking control. Using linear matrix inequality techniques, the design is converted into a semi-definite optimization problem. Clo...

Adaptive Horizon Model Predictive Control

Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic processes. Beside the standard requirements of MPC including a terminal cost that is a control Lyapunov function, AHMPC requi...

Computationally Efficient Long Horizon Model Predictive Direct Current ‎Control of DFIG Wind Turbines ‎

Model predictive control (MPC) based methods are gaining more and more attention in power converters and electrical drives. Nevertheless, high computational burden of MPC is an obstacle for its application, especially when the prediction horizon increases extends. At the same time, increasing the prediction horizon leads to a superior response. In this paper, a long horizon MPC is proposed to c...

Sliding Horizon Feedback Control