Feedback-Assisted Iterative Learning Model Predictive Control with Nonlinear Fuzzy Model

Learning Model Predictive Control for Iterative Tasks

A Learning Model Predictive Controller (LMPC) for iterative tasks is presented. The controller is referencefree and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nonincreasing performance at each iteration. The paper presents the control design approach, and shows how to r...

Iterative Learning Economic Model Predictive Control

An iterative learning based economic model predictive controller (ILEMPC) is proposed for repetitive tasks in this paper. Compared with existing works, the initial feasible trajectory of the proposed ILEMPC is not restricted to be convergent to an equilibrium so it can handle various types of control objectives: stabilization, tracking a periodic trajectory and even pure economic optimization. ...

Fuzzy model predictive control

A highly nonlinear system controlled by a linear model predictive controller (MPC) may not exhibit a satisfactory dynamic performance. This has led to the development of a number of nonlinear MPC (NMPC) approaches that permit the use of first principles-based nonlinear models. Such models can be accurate over a wide range of operating conditions, but may be difficult to develop for many industr...

Nonlinear Model Predictive Control

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...