Reference : Consensus and Pursuit-Evasion in Nonlinear Multi-Agent Systems
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Multidisciplinary, general & others
Consensus and Pursuit-Evasion in Nonlinear Multi-Agent Systems
Thunberg, Johan mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
KTH Royal Institute of Technology, ​Stockholm, ​​Sweden
Applied and Computational Mathematics
[en] Multi-agent systems ; consensus ; attitude synchronization ; nonlinear control ; optimization ; pursuit-evasion
[en] Within the field of multi-agent systems theory, we study the problems of consensus
and pursuit-evasion. In our study of the consensus problem, we first provide some
theoretical results and then consider the problem of consensus on SO(3) or attitude
In Chapter 2, for agents with states in Rm, we present two theorems along the lines of Lyapunov’s second method that, under different conditions, guarantee asymptotic state consensus in multi-agent systems where the interconnection topologies are switching. The first theorem is formulated by using the states of the agents in the multiagent system, whereas the second theorem is formulated by using the pairwise states for pairs of agents in the multi-agent system.
In Chapter 3, the problem of consensus on SO(3) for a multi-agent system with directed and switching interconnection topologies is addressed. We provide two different types of kinematic control laws for a broad class of local representations of SO(3).
The first control law consists of a weighted sum of pairwise differences between positions of neighboring agents, expressed as coordinates in a local representation. The structure of the control law is well known in the consensus community for being used in systems of agents in the Euclidean space, and here we show that the same type of control law can be used in the context of consensus on SO(3). In a later part of this chapter, based on the kinematic control laws, we introduce torque control laws for a system of rigid bodies in space and show that the system reaches consensus when these control laws are used.
Chapter 4 addresses the problem of consensus on SO(3) for networks of uncalibrated cameras. Under the assumption that each agent uses a camera in order to measure its rotation, we prove convergence to the consensus set for two types of kinematic control laws, where only conjugate rotation matrices are available for the agents. In these conjugate rotations, the rotation matrix can be seen as distorted by the (unknown) intrinsic parameters of the camera. For the conjugate rotations we introduce distorted versions of well known local parameterizations of SO(3) and show consensus by using control laws that are similar to the ones in Chapter 3, with the difference that the distorted local representations are used instead.
In Chapter 5, we study the output consensus problem for homogeneous systems of agents with linear continuous time-invariant dynamics. We derive control laws that solve the problem, while minimizing a cost functional of the control signal. Instead of considering a fixed communication topology for the system, we derive the optimal control law without any restrictions on the topology. We show that for all linear output controllable homogeneous systems, the optimal control law uses only relative information but requires the connectivity graph to be complete and in general requires measurements of the state errors. We identify cases where the optimal control law is only based on output errors.
In Chapter 6, we address the multi-pursuer version of the visibility pursuit-evasion problem in polygonal environments. By discretizing the problem and applying a Mixed Integer Linear Programming (MILP) framework, we are able to address problems requiring so-called recontamination and also impose additional constraints, such as connectivity between the pursuers. The proposed MILP formulation is less conservative than solutions based on graph discretizations of the environment, but still somewhat more conservative than the original underlying problem. It is well known that MILPs, as well as multi-pursuer pursuit-evasion problems, are NP-hard. Therefore we apply an iterative Receding Horizon Control (RHC) scheme, where a number of smaller MILPs are solved over shorter planning horizons. The proposed approach is illustrated by a number of solved examples.

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