Improved Area Covering in Dynamic Environments by Nonlinear Model Predictive Path Following 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...

Analysis of explicit model predictive control for path-following control

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can redu...

Model Predictive Path-Following Control of an AR.Drone Quadrotor

This paper addresses the design and implementation of the Extended Prediction Self-Adaptive Control (EPSAC) approach to Model Predictive Control (MPC) for path-following. Special attention is paid to the closed-loop performance in order to achieve a fast response without overshoot, which are the necessary conditions to ensure the desired tracking performance in confined or indoor environments. ...

Improved Nonlinear Model Predictive Control Based on Genetic Algorithm

Model predictive control (MPC) has made a significant impact on control engineering. It has been applied in almost all of industrial fields such as petrochemical, biotechnical, electrical and mechanical processes. MPC is one of the most applicable control algorithms which refer to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance....

Nonlinear Model Predictive Control