A Suboptimality Approach to Distributed H Optimal Control

Jiao, Junjie; Trentelman, Harry L.; Camlibel, M. Kanat

doi:10.1016/j.ifacol.2018.12.027

Cited by 11 publications

(16 citation statements)

References 34 publications

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“…then there exists a positive definite matrix P satisfying 36) then there exists a positive definite matrix P satisfying…”

Section: (531)mentioning

confidence: 99%

“…The distributed H 2 optimal control problem is the problem of finding a distributed protocol that minimizes the associated H 2 cost while the controlled network achieves consensus or synchronization, see e.g. [36,39,53,55,112]. As before, due to the fact that the proposed distributed protocols have a particular structure imposed by the network graph, the problem of distributed H 2 optimal control is a non-convex optimization problem.…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

“…Later on, in [112] these results were generalized to directed graphs. For a given H 2 cost criterion that penalizes the weighted differences between the outputs of the communicating agents, in [36] a distributed suboptimal synchronizing protocol based on static relative state feedback was established.…”

Section: Introductionmentioning

confidence: 99%

“…For H 2 cost functionals of a particular form, [55] and [53] proposed distributed suboptimal protocols that stabilize the controlled multi-agent network. In [36], a distributed H 2 suboptimal control problem was addressed using static state feedback. The results in [36] were then generalized in [39] to the case of dynamic output feedback.…”

Section: Introductionmentioning

confidence: 99%

“…In [36], a distributed H 2 suboptimal control problem was addressed using static state feedback. The results in [36] were then generalized in [39] to the case of dynamic output feedback.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Linear Quadratic Control and Filtering: a suboptimality approach

Jiao

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Samenvatting 1. Introduction leaderless homogeneous multi-agent systems. In Chapter 3, we extend the results in Chapter 2 on distributed linear quadratic suboptimal control for leaderless multi-agent systems to the case of distributed linear quadratic suboptimal tracking control for leader-follower multi-agent systems. The computation of the local control gains in Chapters 2 and 3 requires complete knowledge of the eigenvalues of the Laplacian matrix or of a given positive definite matrix associated with the communication graph interconnecting the agents, often called global information. In Chapter 4, we aim at removing this dependence on global information. For multiagent systems with single integrator agent dynamics, we establish a decentralized computation method for computing suboptimal local control gains. Chapters 5-7 deal with the distributed H 2 suboptimal control problem. In Chapter 5, we study this problem for homogeneous multi-agent systems by static relative state feedback, and the results are then generalized in Chapter 6 to the case of dynamic relative output feedback. In Chapter 7, we further generalize the results in Chapters 5 and 6, and investigate the distributed H 2 suboptimal control problem for heterogeneous multi-agent systems. In Chapter 8, we study H 2 and H ∞ suboptimal distributed filtering problems for linear systems. In Chapter 9, we formulate the conclusions of this thesis, and discuss directions for possible future research.

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“…then there exists a positive definite matrix P satisfying 36) then there exists a positive definite matrix P satisfying…”

Section: (531)mentioning

confidence: 99%

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [36], a distributed H 2 suboptimal control problem was addressed using static state feedback. The results in [36] were then generalized in [39] to the case of dynamic output feedback.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Linear Quadratic Control and Filtering: a suboptimality approach

Jiao

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Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms

Ban

Lennox

et al. 2021

Mobile Netw Appl

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Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.

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