A design of a strongly stable generalized predictive control using coprime factorization approach

This paper proposes a new generalized predictive control (GPC) having new design parameters. In selecting the design parameters, the controller becomes a strongly stable GPC, that is, not only the closed-loop system is stable, but also the controller itself is stable. The parameters are introduced by applying the coprime factorization approach and comparing Youla parametrization of stabilizing compensators to the controller by the standard GPC</p

Continuous-time anti-windup generalized predictive control of non-minimum phase processes with input constraints

This paper deals with a design problem of a continuous-time anti-windup generalized predictive control system using coprime factorization approach for non-minimum phase processes with input constraints. Based on the proposed design scheme, a condition for stability of the closed-loop system with input constraints and a straightforward method to improve the output response of the system with input constraints are given. Simulation results are presented to support the theoretical analysis.</p

A state-space based design of generalized minimum variance controller equivalent to transfer-function based design

Proposes a generalized minimum variance controller (GMVC) using a state-space approach. The controller consists of a state feedback and a reduced-order observer with poles at z=0. A coprime factorization of the state-space based controller is also obtained. It is shown that the GMVC designed by state-space approach is equivalent to the GMVC given by solving Diophantine equations and a polynomial approach. The equivalence is proved by comparing coprime factorizations of the two controllers. From the results of the paper, it may be possible to apply advanced design schemes given by state-space control theory to the design of GMVC</p

A state-space based design of generalized minimum variance controller equivalent to transfer-function based design

Proposes a generalized minimum variance controller (GMVC) using a state-space approach. The controller consists of a state feedback and a reduced-order observer with poles at z=0. A coprime factorization of the state-space based controller is also obtained. It is shown that the GMVC designed by state-space approach is equivalent to the GMVC given by solving Diophantine equations and a polynomial approach. The equivalence is proved by comparing coprime factorizations of the two controllers. From the results of the paper, it may be possible to apply advanced design schemes given by state-space control theory to the design of GMVC</p

An extension of generalized minimum variance control for multi-input multi-output systems using coprime factorization approach

This paper proposes a new generalized minimum-variance controller (GMVC) having new design parameters by using the coprime factorization approach for a multi-input multi-output (MIMO) case. The method is directly extended from a conventional GMVC and used to construct the controller; it needs to solve only one Diophantine equation as in the conventional method. In this paper, by using double-coprime factorization, a simple formula for the closed-loop system given by the parametrized controller is obtained; and using the formula, it is proved that the closed-loop characteristic from the reference signal to plant output is independent of the selection of the design parameters and the poles of the controller can be chosen by the design parameters without changing the closed-loop system</p

A design of a strongly stable generalized predictive control using coprime factorization approach A Design of a Strongly Stable Generalized Predictive Control Using Coprime Factorization Approach

Abstract This paper proposes a new generalized predictive control (GPC) having new design parameters. In selecting the design parameters, the controller becomes a strongly stable GPC, that is, not only the closed-loop system is stable, but also the controller itself is stable. The parameters are introduced by applying the coprime factorization approach and comparing Youla parametrization of stabilizing compensators to the controller by the standard GPC

An extension of generalized minimum variance control for multi-input multi-output system using coprime factorization approach

Abstract This paper proposes a new generalized minimumvariance controller (GMVC) having new design parameters by using coprime factorization approach for multiinput multi-output (MIMO) case. The method is directly extended from a conventional GMVC and to construct the controller, it needs to solve only one Diophantine equation as in the conventional method. In this paper, by using double-coprime factorization, a simple formula for closed-loop system given by the parametrized controller is obtained and using the formula, it is proved that the closed-loop characteristic from reference signal to plant output is independent of the selection of the design parameters and the poles of the controller can be chosen by the design parameters without changing the closed-loop system