Robust Formation Control against Observation Losses

Robust Formation Control against Observation Losses

Li, Zhonggang (TU Delft Electrical Engineering, Mathematics and Computer Science)

Rajan, R.T. (mentor)
Leus, G.J.T. (graduation committee)
Loicq, J.J.D. (graduation committee)

Delft University of Technology

Electrical Engineering | Circuits and Systems

2022-08-25

Distributed formation control has received increasing attention in multiagent systems. Maintaining certain geometry in space is advantageous in many applications such as space interferometry and underwater sensing. At present, there is a variety of distributed solutions for agents to converge to desired formations and track a series of prescribed maneuvers. They typically rely on the relative kinematics e.g., relative positions of the neighboring agents as state observations for the local controller. In harsh working environments, the acquisition of the relative kinematics is challenged and observation losses might occur, which can be detrimental to the optimality of formation.

In this work, observation losses in noisy environments are addressed under a distributed formation control framework. Three types of solutions are proposed to enhance the robustness which is evaluated through the improvements of tracking error, convergence speed, and smoothness of trajectories in both random and permanent loss settings. Firstly, a relative localization technique is proposed using formation itself as a spatial constraint. Secondly, a dynamic model is established for the agents entailed by a Kalman filter-based solution. Finally, a fusion of the previous two types is inspired and it exhibits superior performance than both aforementioned types individually.

This work not only provides means of relative localization without additional sensor data but also shares insights into coping with random or permanent graph changes for stress-based formation control systems. This could potentially lead to the exploration of formation control with subgraphs or energy-efficient sensing as future directions.

Formation Control
Observation Losses
Relative Localization
Kalman Filter

http://resolver.tudelft.nl/uuid:c68927a3-2aca-425c-b1ef-d13178dfae25

Student theses

master thesis

© 2022 Zhonggang Li