The University of Southampton
University of Southampton Institutional Repository

Autonomous multi-agent reconfigurable control systems

Autonomous multi-agent reconfigurable control systems
Autonomous multi-agent reconfigurable control systems
This thesis is an investigation of methods and architectures for autonomous multi-agent reconfigurable controllers. As part of the analysis two components are looked at: the fault detection and diagnosis (FDD) component and the controller reconfiguration (CR) component. The FDD component detects and diagnoses faults. The CR component on the other hand, adapts or changes the control architecture to accommodate the fault. The problem is to synchronize or integrate these two components in the overall structure of a control system. A novel approach is proposed. A multiagent architecture is used to interface between the two components. This method allows the system to be viewed as a modular structure. Three types of agent are defined. A planner agent Ap, a monitor agent Am and a control agent Ac. The monitor agent takes the role of the FDD component. The planner and control agents on the other hand take the roles of CR component.

The planner decides which controller to use and passes it on to Ac. It also decides on the parameter settings of the system and changes it accordingly. It belongs to the reactive agent category. The planner agent's internal architecture maps its sensor data directly to actions using a pre-set rule based conditional logic. It was decided that this architecture would reduce the overall complexity of the system. The monitor agent Am belongs to the learning agent category. It uses an algorithm called adaptive resonance theory neural network or ART-NN to autonomously categorize system faults. Am then informs the other agents of the fault status. ART-NN was chosen due to the fact that it does not need to be trained with sample data and learns to categorize data patterns on the fly. This allows Am to detect unmodelled system faults. The control agent Ac also belongs to the learning agent category. It uses a multiagent reinforcement learning algorithm to learn a controller for the system at hand. Once a suitable controller has been learnt, the parameters of the controller are passed to Ap for it to be stored in its memory and learning is terminated. During control execution mode, controller parameters are sent to Ac from Ap. The novel approach is demonstrated on a case study. Our laboratory-built 4-wheeled skid-steering vehicle complete with sensors is designed as a way of demonstration. Several faults are simulated and the response of the demo system is analyzed.
Abu Bakar, Badril
cd1b5643-deb8-4030-b28d-d1e0bce662d5
Abu Bakar, Badril
cd1b5643-deb8-4030-b28d-d1e0bce662d5
Thomas, Trevor
bccfa8da-6c8b-4eec-b593-00587d3ce3cc

Abu Bakar, Badril (2013) Autonomous multi-agent reconfigurable control systems. University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis, 176pp.

Record type: Thesis (Doctoral)

Abstract

This thesis is an investigation of methods and architectures for autonomous multi-agent reconfigurable controllers. As part of the analysis two components are looked at: the fault detection and diagnosis (FDD) component and the controller reconfiguration (CR) component. The FDD component detects and diagnoses faults. The CR component on the other hand, adapts or changes the control architecture to accommodate the fault. The problem is to synchronize or integrate these two components in the overall structure of a control system. A novel approach is proposed. A multiagent architecture is used to interface between the two components. This method allows the system to be viewed as a modular structure. Three types of agent are defined. A planner agent Ap, a monitor agent Am and a control agent Ac. The monitor agent takes the role of the FDD component. The planner and control agents on the other hand take the roles of CR component.

The planner decides which controller to use and passes it on to Ac. It also decides on the parameter settings of the system and changes it accordingly. It belongs to the reactive agent category. The planner agent's internal architecture maps its sensor data directly to actions using a pre-set rule based conditional logic. It was decided that this architecture would reduce the overall complexity of the system. The monitor agent Am belongs to the learning agent category. It uses an algorithm called adaptive resonance theory neural network or ART-NN to autonomously categorize system faults. Am then informs the other agents of the fault status. ART-NN was chosen due to the fact that it does not need to be trained with sample data and learns to categorize data patterns on the fly. This allows Am to detect unmodelled system faults. The control agent Ac also belongs to the learning agent category. It uses a multiagent reinforcement learning algorithm to learn a controller for the system at hand. Once a suitable controller has been learnt, the parameters of the controller are passed to Ap for it to be stored in its memory and learning is terminated. During control execution mode, controller parameters are sent to Ac from Ap. The novel approach is demonstrated on a case study. Our laboratory-built 4-wheeled skid-steering vehicle complete with sensors is designed as a way of demonstration. Several faults are simulated and the response of the demo system is analyzed.

PDF
Thesis_Abubakar.pdf - Other
Download (9MB)

More information

Published date: 17 March 2013
Organisations: University of Southampton, Aeronautics, Astronautics & Comp. Eng

Identifiers

Local EPrints ID: 351346
URI: https://eprints.soton.ac.uk/id/eprint/351346
PURE UUID: dafae285-31d3-41b1-9d5d-0551512dffdf

Catalogue record

Date deposited: 22 Apr 2013 13:52
Last modified: 18 Jul 2017 04:26

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×