Distributed optimal consensus control for nonlinear multi-agent systems with input saturation based on event-triggered adaptive dynamic programming method

Shi, Zhengqing; Zhou, Chuan

doi:10.1080/00207179.2020.1790663

Cited by 12 publications

(12 citation statements)

References 27 publications

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“…i indicate the next event-triggered instant determined by the sufficient condition (30). According to (32), the sufficient condition (30) can be divided into two situations during the time interval [t h i , th+1 i ), which contains: 1) there is no events occuring for all rigid bodies in N i , and 2) there exists at least one event for the rigid body j ∈ N i .…”

Section: B Dynamic Event-triggered Mechanismmentioning

confidence: 99%

“…In [24]- [27], the eventtriggered mechanism is introduced to solve the optimal control of an individual system. The optimal consensus of multiagent systems is considered in [28]- [30] by using the eventtriggered reinforcement learning method. However, the eventtriggered conditions in all of the above event-triggered reinforcement learning methods [24]- [30] include the continuous information.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal consensus of multiagent systems is considered in [28]- [30] by using the eventtriggered reinforcement learning method. However, the eventtriggered conditions in all of the above event-triggered reinforcement learning methods [24]- [30] include the continuous information. Therefore, all agents need to obtain the state information of themselves and their neighbors in real-time to determine whether the event-triggered condition is satisfied, which inevitably increases the communication resources.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Jin¹,

Mao²,

Kocarev³

et al. 2022

Preprint

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In this paper, an event-triggered Reinforcement Learning (RL) method is proposed for the optimal attitude consensus of multiple rigid body networks with unknown dynamics. Firstly, the consensus error is constructed through the attitude dynamics. According to the Bellman optimality principle, the implicit form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman (HJB) equations are obtained. Because of the augmented system, the optimal controller can be obtained directly without relying on the system dynamics. Secondly, the self-triggered mechanism is applied to reduce the computing and communication burden when updating the controller. In order to address the problem that the HJB equations are difficult to solve analytically, a RL method which only requires measurement data at the event-triggered instants is proposed. For each agent, only one neural network is designed to approximate the optimal value function. Each neural network is updated only at the event-triggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of the closed-loop system is obtained, and the Zeno behavior is also avoided. Finally, the simulation results on a multiple rigid body network demonstrate the validity of the proposed method.

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Section: B Dynamic Event-triggered Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Jin¹,

Mao²,

Kocarev³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In summary, adaptive dynamic programming has a complete theoretical foundation and is well suited to solve multi-agent systems' consensus optimal control problems. Therefore, ADP approaches have been applied on multi-agent systems to deal with the optimal distributed control problems [38]- [40]. [38] investigates the robust optimal consensus for nonlinear multi-agent systems through the local adaptive dynamic programming (ADP) approach and the event-triggered control method.…”

Section: Introductionmentioning

confidence: 99%

“…[39] is concerned with the design of distributed optimal coordination control for nonlinear multi-agent systems based on eventtriggered adaptive dynamic programming method. In [40], non-quadratic cost functions are introduced to handle input constraints and a novel distributed optimal consensus protocol is derived based on event-triggered adaptive dynamic programming method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using Adaptive Dynamic Programming

et al. 2022

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The consensus optimal control problem for a class of linear multi-agent systems with directed communication networks is studied in this paper using adaptive dynamic programming. To overcome the restrictions of the agent's low processing capability and to extend the actuator's lifetime, consider the event-triggered. In the beginning, a dynamic event-triggered is provided, with several existing static event-triggered serving as special examples. When using the dynamic event-triggered, a longer interval can be shown between any two consecutive event-triggered. Designing a dynamic event-triggered control law becomes more challenging when implemented in directed networks. In addition, on the basis of dynamic event-triggered, a novel adaptive dynamic programming is used to construct a suitable dynamic event-triggered control law, which employs just the interaction information between agents and does not need model dynamics, overcoming the difficulty of solving the algebraic Riccati equation. Finally, the effectiveness of the proposed method is verified by simulation results, and no agent exhibits Zeno behavior.

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Optimal control of partially unknown constrained‐input systems: A dynamic event‐triggered‐based approach

Zou

Zhang

2023

Optim Control Appl Methods

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This article presents an identifier‐based dynamic event‐triggered optimal control scheme for partially unknown constrained‐input systems. First, an event‐triggered‐based neural network (NN) identifier is constructed to estimate the unknown system dynamics. Then, an adaptive dynamic programming algorithm with actor‐critic NN structure is adopted to obtain an approximate solution of the Hamilton–Jacobi–Bellman equation. The above considers that transmitted measurements are only available at the triggering instants, and the update of all three NN weights depends on the established dynamic event‐triggered mechanism. Different from existing static event‐triggered mechanism, the proposed dynamic event‐triggered mechanism can further obtain a reasonable trade‐off between performance and communication resources by introducing a dynamic variable, and the Zeno behavior can be excluded by devising an exponential term. It is proved that all the closed‐loop system signals are uniformly ultimately bounded under the established event‐triggered mechanism. Finally, two numerical examples are provided, including the spring‐mass‐damper system, to validate the proposed control scheme.

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Distributed optimal consensus control for nonlinear multi-agent systems with input saturation based on event-triggered adaptive dynamic programming method

Cited by 12 publications

References 27 publications

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using Adaptive Dynamic Programming

Optimal control of partially unknown constrained‐input systems: A dynamic event‐triggered‐based approach

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