Data-Driven Optimization Framework for 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...

Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

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 nondecreasing performance at each iteration. The paper presents the control design approach, and shows how to r...

Particle Swarm Optimization for Nonlinear Model Predictive Control

The paper proposes two Nonlinear Model Predictive Control schemes that uncover a synergistic relationship between on-line receding horizon style computation and Particle Swarm Optimization, thus benefiting from both the performance advantages of on-line computation and the desirable properties of Particle Swarm Optimization. After developing these techniques for the unconstrained nonlinear opti...

Nonlinear Model Predictive Control

Model Predictive Control for Nonlinear Sampled-data Systems

The topic of this paper is a new model predictive control (MPC) approach for the sampled–data implementation of continuous–time stabilizing feedback laws. The given continuous–time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution ...