Distributed robust adaptive control of high order nonlinear multi agent systems

Hashemi, Mahnaz; Shahgholian, Ghazanfar

doi:10.1016/j.isatra.2018.01.023

Cited by 30 publications

(39 citation statements)

References 46 publications

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“…An adaptive decentralized dynamic surface control approach is presented for a class of large‐scale nonlinear systems subject to actuator faults, time‐varying delays and uncertainties in Reference 11, where NN is employed to approximate the unknown nonlinear functions. Hashemi and Shahgholian 12 extend the work in Reference 11 to high‐order nonlinear multi‐agent systems. It is worth mentioning that the input and output constraints are factors that affect the performance of the practical control systems.…”

Section: Introductionmentioning

confidence: 82%

“…In the past few decades, many explorations on cooperative adaptive control strategies for interconnected nonlinear systems have been carried out, including Lyapunov function-based distributed adaptive control protocol, [1][2][3][4] adaptive iterative learning control method, 5,6 backstepping-based adaptive cooperative control scheme, 7,8 and dynamic surface control technique. [9][10][11][12] As universal approximators, neural networks (NN) or fuzzy logic control has found extensive applications for nonlinear systems with unknown functions, any smooth function could be identified with any accuracy by NNs only when the arguments of the smooth functions stay in a compact set no matter how large the compact set is. Neural network-based observers are designed to address the leader-follower consensus problem for a class of nonlinear multi-agent systems in Reference 13.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

2020

Intl J Robust & Nonlinear

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This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low-pass filters, while the trade-off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed-loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties.

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

2020

Intl J Robust & Nonlinear

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“…210 It can solve problems that are difficult or impossible for a single agent or a monolithic system to solve. 211 MAS has been and is a viable method for level distributed control system. 212,213 The focus of multiagent technology in applying the microgrid is on the control of frequency, voltage, and so on.…”

Section: Distributed Multiagent Controlmentioning

confidence: 99%

A brief review on microgrids: Operation, applications, modeling, and control

Shahgholian

2021

Int Trans Electr Energ Syst

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155

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Microgrid is an important and necessary component of smart grid development. It is a small-scale power system with distributed energy resources. To realize the distributed generation potential, adopting a system where the associated loads and generation are considered as a subsystem or a microgrid is essential. In this article, a literature review is made on microgrid technology.The studies run on microgrid are classified in the two topics of feasibility and economic studies and control and optimization. The applications and types of microgrid are introduced first, and next, the objective of microgrid control is explained. Microgrid control is of the coordinated control and local control categories. The small signal stability and methods in improving it are discussed.The load frequency control in microgrids is assessed.

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“…During the literature mentioned above, one of crucial issues is how to stabilize unstable or chaotic systems. In order to do this, state feedback controllers were always designed, 19–22 but, for MJSs, main limitations of this kind of controllers are that mode information of Markov chain and state variables are involved in controllers, that is, they are mode‐dependent and/or state‐dependent 2,4,13,16,23–25 . Actually, the assumption of Markov mode and system states availability may not be practical, which is because the measuring of mode information and state variables in most of practical systems is too expensive or they even cannot be measured directly in some cases.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation

Cao

Nie

2021

Intl J Robust & Nonlinear

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The observer‐based adaptive neural networks control problem is studied in this article for nonlinear Markovian jump systems (MJSs) with partial mode information and input saturation. The existing adaptive control scheme for MJSs works only when the Markov mode of system is completely known. For this reason, the concept of partial mode information of Markov chain is embedded into the framework of adaptive backstepping method and then a mode detector is set to emit the mode. To achieve the control objective, only the emitted mode is used to design the state observer and adaptive control scheme. Besides, an auxiliary signal is introduced in MJSs to compensate the effect of input saturation and this signal appears in controller as the input of neural networks instead of a separate term, which makes the form of proposed controller simpler than that in the literature. Finally, a numerical example and a practical example are provided to demonstrate the feasibility of our control scheme.

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Distributed robust adaptive control of high order nonlinear multi agent systems

Cited by 30 publications

References 46 publications

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

A brief review on microgrids: Operation, applications, modeling, and control

Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation

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