In process industry, there exist many Wiener systems with input magnitude constraints for which, however, most of the existing control algorithms cannot guarantee to have sufficiently large regions of asymptotic stability. In this paper, the subspace method is applied to separate the nonlinear and linear blocks in a constrained multi-input/multi-output (MIMO) Wiener system and a novel dual-mode nonlinear model predictive control algorithm is developed to maximize the region of the asymptotic stability. Simulation results are presented to demonstrate the virtues of this new control algorithm. The limitation is the requirement that the state and input matrices of the Wiener system's linear block should be accurately identified. Copyright © 20...
In this dissertation, we present research on identifying Wiener systems with known, noninvertible no...
This paper presents a development for the model predictive control (MPC) of nonlinear systems employ...
In this paper, we present a novel subspace identification algorithm in which all non-causal terms ar...
Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations f...
The control of multi-input multi-output (MIMO) systems is a common problem in practical control scen...
For large numbers of degrees of freedom and/or high dimensional systems, nonlinear model predictive ...
In this paper, a simulation of predictive control of a two-input two-output (TITO) system with nonmi...
Non-minimum phase Multi-input Multi-Ouput (MIMO) systems are known to be difficult to control. Model...
This paper describes a computationally efficient (sub-optimal) nonlinear predictive control algorith...
A tracking problem is considered for a Wiener model. A two-layer hierarchical control structure is d...
The model predictive control problem with guaranteed stability for a class of linear distributed par...
Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of ...
In this paper a novel predictive control design method is proposed. Using a modified subspace state-...
In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open l...
The benefits of using the Wiener model based identification and control methodology presented in thi...
In this dissertation, we present research on identifying Wiener systems with known, noninvertible no...
This paper presents a development for the model predictive control (MPC) of nonlinear systems employ...
In this paper, we present a novel subspace identification algorithm in which all non-causal terms ar...
Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations f...
The control of multi-input multi-output (MIMO) systems is a common problem in practical control scen...
For large numbers of degrees of freedom and/or high dimensional systems, nonlinear model predictive ...
In this paper, a simulation of predictive control of a two-input two-output (TITO) system with nonmi...
Non-minimum phase Multi-input Multi-Ouput (MIMO) systems are known to be difficult to control. Model...
This paper describes a computationally efficient (sub-optimal) nonlinear predictive control algorith...
A tracking problem is considered for a Wiener model. A two-layer hierarchical control structure is d...
The model predictive control problem with guaranteed stability for a class of linear distributed par...
Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of ...
In this paper a novel predictive control design method is proposed. Using a modified subspace state-...
In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open l...
The benefits of using the Wiener model based identification and control methodology presented in thi...
In this dissertation, we present research on identifying Wiener systems with known, noninvertible no...
This paper presents a development for the model predictive control (MPC) of nonlinear systems employ...
In this paper, we present a novel subspace identification algorithm in which all non-causal terms ar...