Distributed model reference adaptive control for cooperative tracking of uncertain dynamical multi‐agent systems

Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang; Wang, Hao

doi:10.1049/iet-cta.2012.0765

Cited by 82 publications

(58 citation statements)

References 36 publications

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“…Lemma 1 [15]: Suppose the network G is undirected and connected, and at least one agent has access to the state of the leader node. Then L + G is positive definite.…”

Section: Preliminariesmentioning

confidence: 99%

“…, N , which represents the state tracking error between ith follower and leader. The global tracking error system can be described by (see (15))…”

Section: Distributed Adaptive Fault-tolerant Protocol Designmentioning

confidence: 99%

“…Since in some practical circumstances the agents dynamics may be subject to uncertainties or external disturbances, the assumption that the considered model precisely represents the actual system and is exactly known in [12][13][14] is restrictive to some extent. In [15], a new model reference cooperative tracking controller is proposed based on neuro-adaptive method for uncertain MASs with matching uncertainties. Furthermore, by introducing non-linear function relied on relative local state information, a novel distributed adaptive protocol has been investigated for uncertain MASs with heterogeneous matching uncertainties in [16].…”

Section: Introductionmentioning

confidence: 99%

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Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

Zhao

Cao

2016

IET Control Theory & Applications

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This study considers the distributed fault-tolerant consensus problem for uncertain multi-agent systems using adaptive protocol. A more general time-varying actuator fault model is given, which includes loss of effectiveness, stuck, bias and outage fault. A new distributed adaptive control scheme is developed to compensate the effect of fault, multiple delayed state perturbations, mismatched parameter uncertainties and external disturbances on leader-follower multiagent systems. Based on the local state information of neighbouring agents, the adaptive updating protocol gains are adjusted online, which remove the assumption that the upper bounds of unknown uncertainty, delayed state perturbation and external disturbances should be known. Moreover, the consensus errors of leader-follower systems can asymptotically converge to zero. Finally, a simulation example is given to show the effectiveness of the theoretical analysis.

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“…Lemma 1 [15]: Suppose the network G is undirected and connected, and at least one agent has access to the state of the leader node. Then L + G is positive definite.…”

Section: Preliminariesmentioning

confidence: 99%

“…, N , which represents the state tracking error between ith follower and leader. The global tracking error system can be described by (see (15))…”

Section: Distributed Adaptive Fault-tolerant Protocol Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

Zhao

Cao

2016

IET Control Theory & Applications

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“…The expansion of the cyberphysical system turns into simply duplicating agents without accommodating control policy. To deal with the physical coupling of networked system, one common approach is to decouple subsystems in control design [5][6][7][8]. Each subsystem may utilize state information of neighbored subsystems for mitigating their physical interference, or the designer treats their physical interference as random disturbance [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Stable Distributed Neural Controller for Physically Coupled Networked Discrete‐Time System via Online Reinforcement Learning

Sun

2018

Complexity

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The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well.

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“…The feasibility of applying neural networks in the MRAC for identification and control of nonlinear systems has been demonstrated through numerous studies. [3][4][5] Among these research works, neural networks are mostly used to approximate models with unknown nonlinearities, thus removing the need for a priori knowledge of system nonlinearities. Most of these methods mainly apply the backpropagation (BP) learning algorithm for adjusting the network parameters.…”

Section: Introductionmentioning

confidence: 99%

Robust kernel-based model reference adaptive control for unstable aircraft

Yang

Zhao

Bao

et al. 2016

Advances in Mechanical Engineering

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In this article, a robust kernel-based model reference adaptive control is proposed for an unstable nonlinear aircraft. The heart of the proposed kernel-based model reference adaptive control scheme comprises an offline neural identifier and an online neural controller. In the offline neural identifier, the kernel-based unified extreme learning machine algorithm is used to identify the aircraft model with the available input-output data in a finite time interval. The finite time interval is selected to avoid the response of the unstable aircraft growing unbounded. In the kernel-based unified extreme learning machine, the hidden layer feature mapping is determined by the kernel matrix. However, the unified extreme learning machine is a batch learning algorithm and is not suitable for the online control learning. To solve the problem, a recursive version of the unified extreme learning machine is developed in this study. Based on a given reference model and the identified model, the recursive version of the unified extreme learning machine algorithm is applied to construct the online control law to compensate for the changes in the aircraft dynamics or characteristics. The performance of the proposed kernel-based model reference adaptive control scheme is validated through the simulation studies of a locally nonlinear longitudinal high-performance aircraft. Simulation studies are also compared with a model reference adaptive control based on the back-propagation algorithm and a model reference adaptive control based on the basic extreme learning machine algorithm in terms of the identification and tracking abilities. The results show that the proposed kernel-based model reference adaptive control can achieve better identification and tracking performance.

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Distributed model reference adaptive control for cooperative tracking of uncertain dynamical multi‐agent systems

Cited by 82 publications

References 36 publications

Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

A Stable Distributed Neural Controller for Physically Coupled Networked Discrete‐Time System via Online Reinforcement Learning

Robust kernel-based model reference adaptive control for unstable aircraft

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