Two coordination principles and their application in large scale systems control

Mesarović, Mihajlo D.; Macko, Donald; Takahara, Yasuhiko

doi:10.1016/0005-1098(70)90097-x

Cited by 76 publications

(26 citation statements)

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“…Having identified the interactions, coordinated and decentralized control can then be achieved with these local controllers by implementing a higherlevel coordinator or a peer-level communication. [2][3][4] These coordination mechanisms thus require a knowledge of the interaction to achieve the desired level of coordination. This interaction thus needs to be identified under controlled conditions using closed-loop identification methodologies.…”

Section: Introductionmentioning

confidence: 99%

Identification for decentralized model predictive control

Gudi

Rawlings

2006

AIChE Journal

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Section: Introductionmentioning

confidence: 99%

Identification for decentralized model predictive control

Gudi

Rawlings

2006

AIChE Journal

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“…These methods are based on two principles, namely the Interaction Balance Principle (IBP) and the Interaction Prediction Principle (IPP), postulated by Mesarovic et al [27,28]. In the interaction prediction approach, which is used in this paper, the overall system is first decomposed into several interactive sub-systems, where the optimization problem is redefined for each sub-system.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical optimal control of large-scale nonlinear chemical processes

Ramezani

Sadati

2009

ISA Transactions

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“…In an attempt for improving this strategy, Mesarovic et al. presented one of the earliest formal quantitative treatments of hierarchical systems by postulating two coordination principles; Interaction Prediction Principle (IPP) and Interaction Balance Principle (IBP) [1], [2] the coordination of large-scale systems are mainly based on these two principles.…”

mentioning

confidence: 99%

A Novel Fuzzy Reinforcement Learning Approach in Two-Level Intelligent Control of 3-DOF Robot Manipulators

Sadati

Emamzadeh

2007

2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning

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In this paper, a fuzzy coordination method based on Interaction Prediction Principle (IPP) and Reinforcement Learning is presented for the optimal control of robot manipulators with three degrees-of-freedom. For this purpose, the robot manipulator is considered as a two-level large-scale system where in the first level, the robot manipulator is decomposed into several subsystems. In the second level, a fuzzy interaction prediction system is introduced for coordination of the overall system where a critic vector is also used for evaluating its performance. The simulation results on using the proposed novel approach ,for optimal control of robot manipulators show its effectiveness and superiority in comparison with the centralized optimization methods. I. INTRUDUCTIONThe optimal control is one of the most important topics in control theory and optimization of large-scale systems. The problems such as complexity, high dimensionality of variables, geographical separation of subsystems, etc., usually are the burdens for solving the overall problem in a centralized fashion. That is way during past three decades, many approaches such as coordination strategies in multi-level systems and decentralized schemes have been proposed by researchers.In decentralized methods, the system is decomposed into several subsystems where their optimization only depends on local variables while the effects and interactions of other subsystems are either ignored, or considered resulting in robust decentralized sub-optimal control schemes.In the coordination methods, similar to the decentralized approaches, the system is first decomposed into several subsystems, while the effects among them are compensated through a coordinator. In this approach, the control of largescale systems is done by using the hierarchical multi-level control scheme. So hierarchical multi-level control is a common approach that has been presented as an important and efficient method in control of large scale systems.The basic principle of hierarchical control is decomposition of a given large-scale system into several smaller scale systems and then coordination of the resulted sub-systems to reach the optimum solution. In an attempt for improving this strategy, Mesarovic et al. presented one of the earliest formal quantitative treatments of hierarchical systems by the coordination of large-scale systems are mainly based on these two principles.In [3]-[6], using these two principles (IPP & IBP), two new gradient based coordination schemes are introduced that have much faster convergence rates than the classical methods. In [7], [8], two new neuro-fuzzy reinforcement strategies are introduced for intelligent coordination of large-scale systems based on IPP and IBP, where critic vectors are used for evaluation of their performances. In [9]-[11], using the new gradient based coordination schemes, the optimal control of robot manipulators have also been considered. In this paper, by using the novel strategy [7], the optimal control of robot manipulators is investigated. ...

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Two coordination principles and their application in large scale systems control

Cited by 76 publications

References 1 publication

Identification for decentralized model predictive control

Identification for decentralized model predictive control

Hierarchical optimal control of large-scale nonlinear chemical processes

A Novel Fuzzy Reinforcement Learning Approach in Two-Level Intelligent Control of 3-DOF Robot Manipulators

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