EXPLICIT MIN-MAX MODEL PREDICTIVE CONTROL OF CONSTRAINED NONLINEAR SYSTEMS WITH MODEL UNCERTAINTY
نویسندگان
چکیده
منابع مشابه
Explicit Min-max Model Predictive Control of Constrained Nonlinear Systems with Model Uncertainty
This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty. The case of time-invariant parameter uncertainty is considered. The explicit MPC controller is based on an orthogonal search tree structure of the state space partition and is designed by so...
متن کاملRobustified Nonlinear Model Predictive Control via a Min-Max Formulation
Nonlinear model predictive control (NMPC) is an appealing control method as it allows to control multi-input multi-output processes while taking constraints into account. It is based on successively solving open-loop optimal control problems. Although NMPC schemes may naturally exhibit a certain degree of inherent robustness, an explicit consideration of process uncertainties is preferable, par...
متن کاملModel predictive control for max-min-plus-scaling systems
We further extend the model predictive control framework, which is very popular in the process industry due to its ability to handle constraints on inputs and outputs, to a class of discrete event systems that can be modeled using the operations maximization, minimization, addition and scalar multiplication. This class encompasses max-plus-linear systems, min-max-plus systems, bilinear max-plus...
متن کاملModel predictive control for max-min-plus systems
Model predictive control (MPC) is a widely used control design method in the process industry. Its main advantage is that it allows the inclusion of constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. We extend MPC to max-min-plus discrete event systems. In general the resulting optimization problems are nonlinear and nonconvex. However, if the state equations a...
متن کاملModel predictive control for uncertain max-min-plus-scaling systems
In this paper we extend the classical min-max model predictive control framework to a class of uncertain discrete event systems that can be modeled using the operations maximization, minimization, addition and scalar multiplication, and that we call max-min-plus-scaling (MMPS) systems. Provided that the stage cost is an MMPS expression and considering only linear input constraints then the open...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC Proceedings Volumes
سال: 2005
ISSN: 1474-6670
DOI: 10.3182/20050703-6-cz-1902.00826