Adaptive Leader-Follower Coordination of Lagrangian Multi-Agent Systems under Transient Constraints

Verginis, Christos K.; Dimarogonas, Dimos V.

doi:10.1109/cdc40024.2019.9029560

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Moreover, many of these works deal with parametric model uncertainties or assume uniform boundedness and/or growth conditions on the unknown dynamical terms. Discontinuous control schemes for system uncertainties are also used in our works [27], [28] that tackle multi-agent problems. The model uncertainties in these works, however, are restricted to uniformly bounded disturbances and terms bounded by the state norm.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Stability of Uncertain Lagrangian Systems with Prescribed Transient Response

Verginis

Dimarogonas

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper considers the asymptotic tracking problem for 2nd-order nonlinear Lagrangian systems subject to predefined constraints for the system response, such as maximum overshoot or minimum convergence rate. In particular, by employing discontinuous adaptive control protocols and nonsmooth analysis, we extend previous results on funnel control to guarantee at the same time asymptotic trajectory tracking from all the initial conditions that are compliant with the given funnel. The considered system contains parametric and structural uncertainties, with no boundedness or approximation/parametric factorization assumptions. The response of the closed loop system is solely determined by the predefined funnel and is independent from the control gain selection. Finally, simulation results verify the theoretical findings.

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Section: Introductionmentioning

confidence: 99%

Asymptotic Stability of Uncertain Lagrangian Systems with Prescribed Transient Response

Verginis

Dimarogonas

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…Robust and adaptive control: A large class of works on multi-agent coordination with uncertain dynamics falls in the category of robust and adaptive control [6], [12]- [22]. Standard adaptive-control methodologies, however, assume certain linear parametric forms for the unknown terms of the dynamics, limiting the dynamic uncertainties to unknown constant terms [12]- [15].…”

Section: B Related Workmentioning

confidence: 99%

“…Standard adaptive-control methodologies, however, assume certain linear parametric forms for the unknown terms of the dynamics, limiting the dynamic uncertainties to unknown constant terms [12]- [15]. Additionally, many works that do not employ parametric assumptions consider dynamic uncertainties and disturbances that are uniformly bounded [16], [17] or satisfy growth conditions [6], [18], [19]. The works [20],…”

Section: B Related Workmentioning

confidence: 99%

“…In this work, we develop a distributed neuro-adaptive control algorithm for the formation control of continuous-time andstate multi-agent systems with unknown nonlinear dynamics. In contrast to the related works in the literature, we do not assume linear parametrizations [12], [13], neural-network approximations [26], [27], global boundedness or growth conditions [6], [16], [18], passivity properties [22], or known upper bounds [20], [21] for the unknown dynamic terms. According to the best of our knowledge, the distributed formation-control problem with unknown dynamics has not been solved in the absence of the aforementioned assumptions.…”

Section: B Related Workmentioning

confidence: 99%

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Non-Parametric Neuro-Adaptive Formation Control

Verginis¹,

Xu²,

Topcu³

2022

Preprint

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We develop a learning-based algorithm for the distributed formation control of networked multi-agent systems governed by unknown, nonlinear dynamics. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. The proposed algorithm avoids these drawbacks by integrating neural network-based learning with adaptive control in a two-step procedure. In the first step of the algorithm, each agent learns a controller, represented as a neural network, using training data that correspond to a collection of formation tasks and agent parameters. These parameters and tasks are derived by varying the nominal agent parameters and a user-defined formation task to be achieved, respectively. In the second step of the algorithm, each agent incorporates the trained neural network into an online and adaptive control policy in such a way that the behavior of the multiagent closed-loop system satisfies the user-defined formation task. Both the learning phase and the adaptive control policy are distributed, in the sense that each agent computes its own actions using only local information from its neighboring agents. The proposed algorithm does not use any a priori information on the agents' unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the achievement of the formation task.

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“…A common grouping for multi‐agent systems is to divide all agents into leaders and followers. Leaders and followers may have different goals, preferences, and capabilities, which can further complicate the coordination problem [18, 19]. Communication protocols between leaders and followers can help address these challenges by enabling agents to share information, coordinate actions, and achieve the objective [20, 21], and also are conducive to fully automate integration and interoperation among leaders and followers [22, 23].…”

Section: Introductionmentioning

confidence: 99%

A leader‐follower communication protocol for motion planning in partially known environments under temporal logic specifications

Yan,

Liu,

Chen

et al. 2024

IET Control Theory & Appl

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This paper considers the problem of communication protocols between leaders and its followers for motion planning in an initially partially known environment. In this setting, the leader observes the environment information to satisfy its own local objective and and the follower completes its own local objective by estimating the states of the leader and communicating with the leader to update its knowledge about the environment when it is necessary, where the local objectives can be expressed in temporal logic. A verifier construction is built first to contain all possible communication protocols between the leaders and the followers. Then, a two‐step synthesis procedure is proposed to capture all feasible communication protocol that satisfy the local objectives for the leader and follower, respectively. In the first step, a sub‐verifier is synthesized to satisfy the objective of the follower. In the second step, based on the obtained sub‐verifier, an iterative algorithm is proposed to extract communication protocols such that the objectives of the leader and follower are satisfied, respectively. A running example is provided to illustrate the proposed procedures.

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Adaptive Leader-Follower Coordination of Lagrangian Multi-Agent Systems under Transient Constraints

Cited by 7 publications

References 33 publications

Asymptotic Stability of Uncertain Lagrangian Systems with Prescribed Transient Response

Asymptotic Stability of Uncertain Lagrangian Systems with Prescribed Transient Response

Non-Parametric Neuro-Adaptive Formation Control

A leader‐follower communication protocol for motion planning in partially known environments under temporal logic specifications

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