A DISTURBANCE DECOUPLING CONTROL LAW WITH OUTPUT DYNAMIC MATCHING FOR NONLINEAR SYSTEMS. APPLICATION TO A BINARY DISTILLATION COLUMN

1987 ◽  
pp. 193-198
Author(s):  
R. Castro ◽  
J. Alvarez ◽  
G. Bornard
Keyword(s):  
Nonlinear Systems ◽  
Distillation Column ◽  
Decoupling Control ◽  
Control Law ◽  
Dynamic Matching ◽  
Disturbance Decoupling

Decoupling in a Class of Nonlinear Systems by State Variable Feedback

1972 ◽  
Vol 94 (4) ◽  
pp. 323-329 ◽  
Author(s):  
S. N. Singh ◽  
W. J. Rugh
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Bilinear Systems ◽  
Decoupling Control ◽  
Control Law ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Control Laws ◽  
State Variable ◽  
Necessary And Sufficient

For a class of nonlinear systems we derive a necessary and sufficient condition for the existence of a state variable feedback control law which accomplishes decoupling, as well as some conditions which characterize the class of decoupling control laws. Several examples are presented to illustrate the application of these results. For a special subclass which includes the so-called bilinear systems, we give two equivalent forms of the necessary and sufficient condition.

Nonlinear disturbance decoupling control of a binary distillation column

Automatica ◽  
1990 ◽  
Vol 26 (3) ◽  
pp. 567-572 ◽  
Author(s):  
Rafael Castro ◽  
Jaime Alvarez ◽  
Joaquin Alvarez
Keyword(s):  
Distillation Column ◽  
Decoupling Control ◽  
Disturbance Decoupling ◽  
Nonlinear Disturbance

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

Sign in / Sign up

Export Citation Format

Share Document