Finding an optimal control strategy for a nonlinear uncertain system is a challenging problem in the area of nonlinear controller design. In this paper, a two-level control algorithm is developed for robust optimal control of large-scale nonlinear systems. For this purpose, using a decomposition/coordination framework, the large-scale nonlinear system is first decomposed into several smaller subsystems, at the first level, where a closed-form solution as a feedback of states and interactions is obtained to optimize each subsystem. At the second level, a substitution-type prediction method, as a coordination strategy, is used to compensate the nonlinear terms of the system and to predict the interaction between subsystems. The coordinator ma...
A hierarchical structure for on-line steady-state optimizing control of intercon-nected systems is d...
AbstractA new approach is proposed for the solution of large-scale constrained optimal control probl...
Complex systems with a very large number of state and control variables are difficult to analyse. mu...
Current applications of nonlinear model predictive control algorithms are restricted to small-scale ...
This paper presents a novel decentralized control strategy for a class of uncertain nonlinear large-...
In this paper, robust control for a class of nonlinear large-scale systems possessing similar subsys...
[[abstract]]This paper is concerned with the problem of robust stabilization for nonlinearly perturb...
This paper presents a new method to construct a decentralized nonlinear robust H∞ controller for a c...
In this paper, a class of nonlinear large-scale systems with similar subsystems is studied. Both mat...
This paper presents a new method to construct a decentralized nonlinear robust H controller for a cl...
In this paper, we present an adaptive optimal control approach applicable to a wide class of large-s...
This paper focuses on optimal control problems for large scale systems with a decomposable cost func...
This paper presents a novel approach to a new problem of global stabilization with L2 disturbance at...
International audienceThis paper presents a new methodology for distributed model predictive control...
A two-neural network approach to solving nonlinear optimal control problems is described. This appro...
A hierarchical structure for on-line steady-state optimizing control of intercon-nected systems is d...
AbstractA new approach is proposed for the solution of large-scale constrained optimal control probl...
Complex systems with a very large number of state and control variables are difficult to analyse. mu...
Current applications of nonlinear model predictive control algorithms are restricted to small-scale ...
This paper presents a novel decentralized control strategy for a class of uncertain nonlinear large-...
In this paper, robust control for a class of nonlinear large-scale systems possessing similar subsys...
[[abstract]]This paper is concerned with the problem of robust stabilization for nonlinearly perturb...
This paper presents a new method to construct a decentralized nonlinear robust H∞ controller for a c...
In this paper, a class of nonlinear large-scale systems with similar subsystems is studied. Both mat...
This paper presents a new method to construct a decentralized nonlinear robust H controller for a cl...
In this paper, we present an adaptive optimal control approach applicable to a wide class of large-s...
This paper focuses on optimal control problems for large scale systems with a decomposable cost func...
This paper presents a novel approach to a new problem of global stabilization with L2 disturbance at...
International audienceThis paper presents a new methodology for distributed model predictive control...
A two-neural network approach to solving nonlinear optimal control problems is described. This appro...
A hierarchical structure for on-line steady-state optimizing control of intercon-nected systems is d...
AbstractA new approach is proposed for the solution of large-scale constrained optimal control probl...
Complex systems with a very large number of state and control variables are difficult to analyse. mu...