Distributed state estimation and model predictive control: Application to fault tolerant control

Menighed, Kamel; Aubrun, Christophe; Yamé, Joseph Julien

doi:10.1109/icca.2009.5410390

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…To simplify the design process of the control law, the overlapping decomposition is used to extract the decoupled subsystems embedded with only the inter-area mode of interest. We consider the case with two overlapped subsystems in states [2,[31][32][33][34][35]. In this case, the state variables are decomposed into two groups:…”

Section: Model Description and Decompositionmentioning

confidence: 99%

Decentralized Adaptive Fault-Tolerant Control for Complex Systems with Actuator Faults

Tellili¹,

Challouf²,

Abdelkrim³

2014

ComplexSystems

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In this paper, an adaptive fault-tolerant control scheme is extended to large-scale interconnected systems with actuator failures and external disturbances. Based on Lyapunov stability theory, a state feedback decentralized controller is designed to compensate for the fault and the disturbance effects, using the overlapping decomposition method. Applying the inclusion principle, the interconnected system is expanded in order to become disjoint subsystems. An adaptive controller is then synthesized separately for each obtained subsystem. The decentralized controller, designed for the expanded system, is finally contracted back to be implemented in the original space. An example of application illustrates the theoretical results and provides a comparative study with other robust and fault-tolerant methods.

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Section: Model Description and Decompositionmentioning

confidence: 99%

Decentralized Adaptive Fault-Tolerant Control for Complex Systems with Actuator Faults

Tellili¹,

Challouf²,

Abdelkrim³

2014

ComplexSystems

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“…Previous works on distributed MPC are reported in [6], [13]- [15]. A preliminary analysis of the control performance of distributed MPC has been addressed in [13].…”

Section: Introductionmentioning

confidence: 99%

“…A preliminary analysis of the control performance of distributed MPC has been addressed in [13]. Menighed et al [6] and Razavinasab et al [10] proposed a distributed state estimation strategy, developed for supporting distributed state feedback MPC for large-scale interconnected systems. In [15], two approaches for a coordination between sub-controllers are proposed leading to the so-called communication and cooperation based MPC.…”

Section: Introductionmentioning

confidence: 99%

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Menighed

Chemori

Boussaid

et al. 2020

Lecture Notes in Electrical Engineering

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This paper proposes a novel cooperative distributed control system architecture based on unsupervised and independent Model Predictive Control (MPC) using discretetime Laguerre functions to improve the performance of the whole system. In this distributed framework, local MPCs algorithms might exchange and require information from other sub-controllers via the communication network to achieve their task in a cooperative way. In order to reduce the computational burden in the local rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are used to approximate the predicted control trajectory. Simulation results show that the proposed architecture could guarantee satisfactory global performance even under strong interactions among the subsystems.

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“…Typically, MPC is implemented in a centralized fashion [7] . However, for interconnected systems, it is necessary to introduce a decentralized approach to solve the problem of interaction between subsystems [8,9] .…”

Section: Introductionmentioning

confidence: 99%

Decomposition Based Fuzzy Model Predictive Control Approaches for Interconnected Nonlinear Systems

Dalhoumi

Chtourou

Djemel

2016

Int. J. Autom. Comput.

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This paper proposes fuzzy model predictive control (FMPC) strategies for nonlinear interconnected systems based mainly on a system decomposition approach. First, the Takagi-Sugeno (TS) fuzzy model is formulated in such a way to describe the behavior of the nonlinear system. Based on that description, a fuzzy model predictive control is determined. The system under consideration is decomposed into several subsystems. For each subsystem, the main idea consists of the decomposition of the control action into two parts: The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems. According to such decomposition, two strategies are defined aiming to circumvent the problems caused by interconnection between subsystems. The feasibility and efficiency of the proposed method are illustrated through numerical examples.

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Distributed state estimation and model predictive control: Application to fault tolerant control

Cited by 14 publications

References 16 publications

Decentralized Adaptive Fault-Tolerant Control for Complex Systems with Actuator Faults

Decentralized Adaptive Fault-Tolerant Control for Complex Systems with Actuator Faults

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Decomposition Based Fuzzy Model Predictive Control Approaches for Interconnected Nonlinear Systems

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