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Item type:Publication, Access status: without files , A Novel Sequence Weighting Method for First-Order Consensus Problems(2019-01-18); In this paper we present a novel method for constructing stochastic weighting matrices with the help of a finite sequence that can be chosen according to the application in a distributed manner. In addition, we propose three algorithms that determine how every agent decides on assigning these weights to its neighbours. Then, the so-called sequence weighting method is compared with other existing approaches for the special case of a one-dimensional lattice graph. For this purpose, we derive the characteristic polynomial of a quasi- Toeplitz matrix. Considering the sequence weighting method we calculate a bound for the second greatest eigenvalue that can be bounded away from 1 independent of the network size. Using a recently reported result about uniform packet loss, we show that bounds on the convergence speed not only hold in the loss-free case, but also when uniform packet loss occurs. Simulation results with non-uniform packet loss confirm a better performance using the sequence weighting method in comparison to existing strategies.Publicationtype: Conference Paper165 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed Approach to Dynamic Quantization for Multi-Agent Systems(2018-08-09); ; We propose a distributed approach to dynamic quantization for discrete time multi-agent systems with local memory storage at each agent. The usual assumption of a common memory storage is relaxed and the zooming-in/zooming-out strategy is based on a scaling factor which is additionally communicated. We use the notion of small lp signal lp stability to prove the effectiveness of our approach and provide a bound on the quantization error.Publicationtype: Conference Paper152 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Multiple Source Seeking using Glowworm Swarm Optimization and Distributed Gradient Estimation(2018-08-09); In this paper we propose a novel mission control strategy for a group of agents to locate unknown multiple extrema of a time-invariant scalar field. The goal is to provide a complete solution for realistic scenarios such as malfunction of agents or time-varying topology. Here we present MESA - Multiple Extrema Seeking Algorithm, where a group of autonomous agents are able, from any arbitrary initial location (in a bounded area), to locate multiple, unknown extrema. The proposed algorithm includes a combination of bio-inspired attraction with estimated gradient and formation control, where the task switching methodology is performed under the specification of Linear Temporal Logic formula. At first, agents find neighbors and construct groups for completing the search procedure. Once an extremum is 'located', a virtual repulsive force is produced by the pioneer group which as a result cause others to search for other unexplored extrema. We propose a new measure-source density - evaluating the algorithm efficiency which is tested under different simulations for a unicycle agent's model.Publicationtype: Conference Paper140 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimal convergence speed of consensus under constrained damping for multi-agent systems with discrete-time double-integrator dynamicsThis paper considers the optimization of the convergence speed of consensus under given damping constraints for multi-agent systems with discrete-time double-integrator dynamics with fixed interconnection topology. This work summarizes and details existing results in the case of undirected topologies and extends them to directed ones. The interconnection topology is assumed to be connected or to contain a rooted-out branching, respectively. Depending on the minimum required damping, for undirected interconnection topologies in most cases analytic solutions are provided. The structure of these solutions is independent of the size of the network and only depends on the largest and second smallest eigenvalue of the corresponding Laplacian. For the remaining cases without analytic solutions provided, a combined bisection grid search is presented that solves the constrained optimization problem efficiently. This algorithm can also be applied to directed interconnection topologies and, as for the undirected case, converges to the single optimum. Simulation results are provided that demonstrate the effectiveness of the proposed approach.Publicationtype: Journal Article116 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Cooperative source seeking and level curve tracking for multi-agent systemsIn this thesis, the cooperative source seeking and the level curve tracking problem using a group of agents under an undirected constrained communication topology are addressed. To solve it, distributed control algorithms enabling agents to drive towards an unknown scalar field's source or to move along a desired scalar field's level curve are proposed. Each agent estimates a gradient direction based only on neighbouring agents' relative position information and scalar field's concentration values at their own location. The control algorithms presented are based on both a formation control component ( keeping agents in a desired geometric driving pattern) and a trajectory control component (steering agents in the source's direction or along the desired level curve). Along this work single and double integrators are considered, as well as linear time invariant (LTI) and non-holonomic systems. Stability conditions and simulation results are provided for single and double integrators, as well as for LTI systems.Publicationtype: Doctoral Thesis783 838