Output‐feedback stochastic model predictive control of chance‐constrained nonlinear systems

Robust Model Predictive Control for a Class of Discrete Nonlinear systems

This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the ...

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...

Stochastic Model Predictive Control

Model Predictive Control (MPC) is a control strategy that has been used successfully in numerous and diverse application areas. The aim of the present article is to discuss how the basic ideas of MPC can be extended to problems involving random model uncertainty with known probability distribution. We discuss cost indices, constraints, closed loop properties and implementation issues.

Particle Model Predictive Control: Tractable Stochastic Nonlinear Output-Feedback MPC

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

Nonlinear Model Predictive Control