Decentralized Fault Tolerant Control of Modular Manipulators System Based on Adaptive Dynamic Programming

Zhou, Fan; Nie, Fujie; An, Tianjiao; Ma, Bing; Li, Yuanchun

doi:10.1007/s12555-021-0120-2

Cited by 5 publications

(9 citation statements)

References 31 publications

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“…In terms of the gradient descent method, 24,25 the weight updating laws (23) are proposed to ensure that the weight vectors W ^ i , i ∈ V converge to ideal values W i , i ∈ V .…”

Section: Distributed Optimal Ftc Schemementioning

confidence: 99%

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

Duan

Liu

Dai³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article investigates the optimal consensus problem for unmanned aerial vehicle formation systems with actuator faults based on nonlinear multi-agent systems. Initially, for fault-free multi-agent system, the distributed optimal controllers are constructed based on the adaptive dynamic programming technique. A critic neural network is applied to approximate the solution of the nonlinear Hamilton–Jacobi–Bellman equations, in which the weight updating laws are built to guarantee the weight vectors of the critic neural network convergence. Second, the fault compensators and corresponding tuning laws are proposed to compensate for actuator faults. Through a combination of optimal controllers and fault compensators, the distributed optimal fault-tolerant controllers are obtained. Then, according to Lyapunov extension theorem, some stability criteria for ensuring the stability of the aircraft and the normal flight of the unmanned aerial vehicle formation are established in the event of an actuator failure. Finally, an example of an unmanned aerial vehicle formation system is introduced to verify the efficiency and reliability of the designed optimal fault-tolerant control scheme.

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“…In terms of the gradient descent method, 24,25 the weight updating laws (23) are proposed to ensure that the weight vectors W ^ i , i ∈ V converge to ideal values W i , i ∈ V .…”

Section: Distributed Optimal Ftc Schemementioning

confidence: 99%

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

Duan

Liu

Dai³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…By establishing the augmented system (7), the transformation from the original robust tracking control problem into an optimal robust regulation problem can be implemented. It is desired to accomplish the design of a controller that can not only stabilize the constructed augmented system (7) but also ensure an adaptive level of performance.…”

Section: System Descriptionmentioning

confidence: 99%

“…The aforementioned schemes mainly concentrate on the stability and neglect the control cost. To simultaneously stabilize Euler–Lagrange systems and optimize control costs, optimal control algorithms based on adaptive dynamic programming (ADP) were developed in References 3,7,8. ADP was initially proposed in Reference 9 and has developed into an effective channel to tackle the optimal control problems of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Robust tracking control for uncertain Euler–Lagrange systems via dynamic event‐triggered and self‐triggered ADP

Chen,

Hao

2023

Intl J Robust & Nonlinear

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This article investigates the robust tracking control problem for a class of Euler–Lagrange systems in presence of parameter uncertainties and external disturbances. Through system transformation and theoretical analysis, an adaptive dynamic programming (ADP) algorithm with two adaptive neural networks (NNs) and a suitable triggering mechanism is proposed to attain the robust stability of the closed‐loop system. A single critic NN is leveraged to implement the approximate optimal controller design. Particularly, an NN‐based feedforward compensation is developed to cope with the uncertainties with unknown bounds. Two different triggering mechanisms are respectively constructed to reduce the budget of sampling, communication and computation, namely the dynamic event‐triggering mechanism (DETM) and the self‐triggering mechanism (STM). The DETM is utilized to decide the update of remote controller and critic NN weight, which can yield a larger inter‐event interval than the static event‐triggering mechanism. Also, the Zeno‐free behavior is guaranteed. Moreover, it is a novel attempt to introduce the STM into ADP design, which relaxes the demand of dedicated hardware online monitoring the event‐triggering condition. Then it is demonstrated that all signals in the closed‐loop system are uniformly ultimately bounded (UUB) via Lyapunov‐based stability analysis. Finally, a simulation example of 2‐link robotic system is implemented to verify the feasibility and effectiveness of the proposed algorithm.

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“…Accordingly, decentralized estimation has received widespread attention. [23][24][25][26][27][28][29] Decentralized estimation is classified into three categories in Reference 30. The first is that agents share their observations to a fusion center and obtain centralized estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, decentralized estimation itself has important applications, which is widely used in various fields including agriculture, 14 power system, 15,16 mobile robot, 17 navigation system, 18 information fusion, sensor networks, 19‐22 and so on. Accordingly, decentralized estimation has received widespread attention 23‐29 …”

Section: Introductionmentioning

confidence: 99%

Team optimal decentralized estimation by multiple agents with asymmetric information

Sun

Wang

et al. 2023

Optim Control Appl Methods

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In this paper, we are concerned with the team optimal decentralized state estimation problem where the state is transmitted to a remote agent with packet dropouts and is observed by local agents with additive noises. In particular, the information of the remote agent is available to all local agents with hierarchical relationships, which leads to an asymmetric information structure. The main contribution is to derive the analytical expressions for the team optimal decentralized state estimation. Different from the existing decentralized estimation results, the novelty of the paper is to comprehensively investigate the influence of packet dropouts and additive noises, which occur frequently in networked control systems.

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Decentralized Fault Tolerant Control of Modular Manipulators System Based on Adaptive Dynamic Programming

Cited by 5 publications

References 31 publications

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

Robust tracking control for uncertain Euler–Lagrange systems via dynamic event‐triggered and self‐triggered ADP

Team optimal decentralized estimation by multiple agents with asymmetric information

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