State Space Realization of Model Predictive Controllers Without Active Constraints
نویسندگان
چکیده
منابع مشابه
State Space Realization of Model Predictive Controllers Without Active Constraints
To enable the use of traditional tools for analysis of multivariable controllers such as model predictive control (MPC), we develop a state space formulation for the resulting controller for MPC without constraints or assuming that the constraints are not active. Such a derivation was not found in the literature. The formulation includes a state estimator. The MPC algorithm used is a receding h...
متن کاملDesigning model predictive controllers with prioritised constraints and objectives
This paper shows how a class of objective functions can be incorporated into a prioritised, multi-objective optimisation problem, for which a solution can be obtained by solving a sequence of single-objective, constrained, convex programming problems. The objective functions considered in this paper typically arise in Model Predictive Control (MPC) of constrained, linear systems. The framework ...
متن کاملState-space interpretation of model predictive control
A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal predic...
متن کاملState-Space Constrained Model Predictive Control
Constrained State-space Model Predictive Control is presented in the paper. Predictive controller based on incremental linear state-space process model and quadratic criterion is derived. Typical types of constraints are considered – limits on manipulated, state and controlled variables. Control experiments with nonlinear model of multivariable laboratory process are simulated first and real ex...
متن کاملOn State Space Model Based Predictive Control
An input and output model is used for the development of a model based predictive control framework for linear model structures. Diierent MPC algorithms which are based on linear state space models or linear polynomial models t into this framework. A new identiication horizon is introduced in order to represent the past.
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: Modeling, Identification and Control: A Norwegian Research Bulletin
سال: 2003
ISSN: 0332-7353,1890-1328
DOI: 10.4173/mic.2003.4.4