Decentralized Optimal Control for Large-Scale Interconnected Systems

The paper proposes a method to integrate numerically an interconnected system, based on an idea of orthogonal approximation of functions. Here, block pulse functions (BPFs) are chosen as the orthogonal set. The main advantage of using this technique is its ability to transform the original optimal control problem to a mathematical programming problem relatively easier to solve. The primary focus of this paper is to exploit and rigorously develop the BPFs parametrization technique for the synthesis of a decentralized observer-based optimal control for large-scale interconnected systems. In addition, we develop a mathematical model of a double-parallel inverted pendulum coupled by a spring, taking into account all possible changes of the connecting position of the elastic spring. In so doing, we conducted advanced simulations applying the new optimal control method to the studied interconnected system. Our results demonstrate the validity and the effectiveness of the developed decentralized observer-based optimal control approach.

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A Control System Design Methodology for Large-Scale Interconnected Systems

10.23919/acc.1990.4790773 ◽

1990 ◽

Cited By ~ 1

Author(s):

Karl W. Flueckiger ◽

John R. Dowdle ◽

Timothy C. Henderson

Keyword(s):

Control System ◽

System Design ◽

Design Methodology ◽

Large Scale ◽

Interconnected Systems ◽

Control System Design

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Optimal Control Points Problem in Domination Game on Large Scale Multi-Agent Systems

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.994 ◽

2020 ◽

Vol 53 (2) ◽

pp. 3031-3036

Author(s):

Tomomitsu Suzuki ◽

Koji Tsumura

Keyword(s):

Optimal Control ◽

Large Scale ◽

Control Points ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

Domination Game

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Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219834611 ◽

2019 ◽

Vol 41 (13) ◽

pp. 3612-3625 ◽

Cited By ~ 1

Author(s):

Wang Qian ◽

Wang Qiangde ◽

Wei Chunling ◽

Zhang Zhengqiang

Keyword(s):

Neural Networks ◽

Tracking Control ◽

Large Scale ◽

Inverse Dynamics ◽

Small Neighborhood ◽

High Order ◽

Interconnected Systems ◽

Delay Systems ◽

Rbf Neural Networks ◽

Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

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Nonlinear Optimal Control of Two-Level Large-Scale Systems; Part I - Interaction Prediction Principle

2005 International Conference on Industrial Electronics and Control Applications ◽

10.1109/icieca.2005.1644371 ◽

2006 ◽

Cited By ~ 2

Author(s):

N. Sadati ◽

A.R. Momeni

Keyword(s):

Optimal Control ◽

Large Scale ◽

Nonlinear Optimal Control ◽

Large Scale Systems ◽

Interaction Prediction

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Dynamic output feedback control for nonlinear large-scale interconnected systems

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-10-2019-0427 ◽

2020 ◽

Vol 39 (4) ◽

pp. 801-821

Author(s):

Ezzeddine Touti ◽

Ali Sghaier Tlili ◽

Muhannad Almutiry

Keyword(s):

Decentralized Control ◽

Output Feedback ◽

Large Scale ◽

Optimization Problem ◽

Lyapunov Theory ◽

Interconnected Systems ◽

Dynamic Output Feedback ◽

Content Type ◽

Dynamic Output ◽

Control Scheme

Purpose This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded. Design/methodology/approach Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs). Findings The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system. Originality/value The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

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