Decentralized Control for Multi-agent Missions Based on Flocking Rules

Ribeiro, Rafael; Silvestre, Daniel; Silvestre, Carlos

doi:10.1007/978-3-030-58653-9_43

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…We draw the attention of the reader to the fact that, in approach (i), we had to enumerate all solutions  1 1 , … ,  1 4 using a polynomial approximation algorithm (similar for the observability). Then, one computes the cost for each combination of solutions, leading to a computationally expensive algorithm.…”

Section: Approach (Ii)mentioning

confidence: 99%

The robust minimal controllability and observability problem

Ramos

Silvestre

2021

Intl J Robust & Nonlinear

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In this paper, we study the Robust Minimal Controllability and Observability Problem (rMCOP). The scenario that motivated this question is related to the design of a drone formation to execute some task, where the decision of which nodes to equip with a more expensive communication system represents a critical economic choice. Given a linear time-invariant system for each of the vehicles, this problem consists of identifying a minimal subset of state variables to be actuated and measured, ensuring that the overall formation model is both controllable and observable while tolerating a prescribed level of inputs/outputs that can fail. Based on the tools in the available literature, a naive approach would consist of enumerating separately all possible minimal solutions for the controllability and observability parts. Then, iterating over all combinations to find the maximum intersection of sensors/actuators in the independent solutions, yielding a combinatorial problem. The presented solution couples the design of both controllability and observability parts through a polynomial reformulation as a minimum set multi-covering problem under some mild assumptions. In this format, the algorithm has the following interesting attributes: (i) only requires the solution of a single covering problem; 9ii) using polynomial approximations algorithms, one can obtain close-to-optimal solutions to the rMCOP. K E Y W O R D Scontrol applications, control design, minimal controllability and observability problem, robustness INTRODUCTIONConsidering a Multi-Agent System (MAS) composed of vehicles interconnected by a communication network is a recurrent proposal for surveillance, exploration, and measuring tasks to be accomplished by unmanned and automatic robotic systems. Missions entailing the use of a large number of such vehicles can adopt a leader/followers approach 12 characterized by having: (i) expensive nodes (leaders) that can communicate with a ground station to receive mission commands and that might be equipped with sophisticated sensors or localization equipment: (ii) cheaper drones (followers) implementing local controllers based on onboard sensors that measure relative localization and receive a small amount of data from the leaders. In this scenario shown in Figure 1, a critical task is to minimize the number of leaders for economic reasons without compromising the controllability and observability of the overall system. In distributed environments, we should ensure these two critical properties. 3,4 We will refer to this challenge as the Minimal Controllability and Observability Problem (MCOP).

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Section: Approach (Ii)mentioning

confidence: 99%

The robust minimal controllability and observability problem

Ramos

Silvestre

2021

Intl J Robust & Nonlinear

Self Cite

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“…With the decentralized control method, the follower can get the state of the leader. 10,11 However, for distributed control, the follower cannot get the state of the leader and needs to exchange information through the communication topology. 12,13 Generally, distributed control is more widely used and currently it is the main multi-agent control method.…”

Section: Introductionmentioning

confidence: 99%

Observer-based distributed convex optimization of bipartite containment control for higher order nonlinear uncertain multi-agent systems

Hao,

Hu,

Yuan

et al. 2023

Measurement and Control

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This paper studies the distributed convex optimization of bipartite containment control problem for a class of higher order nonlinear multi-agent systems with uncertain states. For the optimization problem, the penalty function is constructed by summing the local objective function of each agent and combining the penalty term formed by the adjacency matrix. For the unknown nonlinear function and unpredictable states in the system, this paper construct radial basis function Neural-networks and state observer for approaching, respectively. In order to avoid “explosion of complexity,” under the framework of Lyapunov function theory, we propose the dynamic surface control (DSC) technology and design the distributed adaptive backstepping neural network controller to ensure all the signals remain semi-global uniformly ultimately bounded in the closed-loop system and all agents can converge to the convex hull containing each boundary trajectory as well as its opposite trajectory different in sign. Simulation results confirm the feasibility of the proposed control method.

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“…In the current state-of-the-art literature in autonomous systems, vehicles can use sensor measurements and setvalued observers for self-localization. The generated sets containing the true state and can be used for collision avoidance by checking intersection with the sets describing obstacles (Ribeiro et al (2020(Ribeiro et al ( , 2021). Therefore, these methods benefit from accurate set representations as conservatism will lead to worse trajectories or even unfeasibility.…”

Section: Introductionmentioning

confidence: 99%

Set-valued estimators for Uncertain Linear Parameter-Varying systems

Silvestre

2022

Systems & Control Letters

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Decentralized Control for Multi-agent Missions Based on Flocking Rules

Cited by 16 publications

References 14 publications

The robust minimal controllability and observability problem

The robust minimal controllability and observability problem

Observer-based distributed convex optimization of bipartite containment control for higher order nonlinear uncertain multi-agent systems

Set-valued estimators for Uncertain Linear Parameter-Varying systems

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