A MARINE FAULTS TOLERANT CONTROL SYSTEM BASED

ON INTELLIGENT MULTI-AGENTS

Tianhao Tang and Gang Yao

Department of Electrical & Control Engineering, Shanghai Maritime University

1550 Pudong Road, Shanghai, 200135, P. R. China

Keywords: Multi-agent system, Faults tolerant control, Communication, Coalition, FIPA Agent Platform.

Abstract: This paper presents a hybrid intelligent multi-agent method for marine faults tolerant control (FTC). A new

FTC schema, implemented by different kinds of agent, is discussed as well as the structure and functions of

those agents, which have been encapsulated with intelligent algorithms to carry out different aspects in FTC.

These agents could, having a purpose of trying to earn payoff as much as possible in a mission,

communicate and form a coalition via negotiation when they find cooperation would bring them more

benefits. Simulation experiments and its results are shown at last to demonstrate the efficiency of the

proposed system.

1 INTRODUCTION

The development of modern marine vehicles is

evolving rapidly towards the direction of large scale

and complexity, as well as the trend of unmanned

intervention, to satisfy the increasing requirements

of international marine transportation trade. These

features, hereby, call for more and more safer

mechanism to guarantee the reliability of ship

manoeuvring. For this reason, that applying fault

tolerant control (FTC) theory, especially combined

with distributed artificial intelligece (DAI), into

marine control system attracted much attentions in

recent years by researchers and engineers.

FTC could diagnosis component failures in a

control system and maintain the system performance

at a possibly low but acceptable level. Accordingly,

it is possible to improve the system efficiency and to

guarantee the operation safety in the control process

(Edgar, 2000).

However, the control of marine vehicle, being a

typical large scale and complicated system, is a

nonlinear, undetermined, time-variable, and open

process. The complexity of the FD and FTC system

is growing with the increasing complexity of control

plants. To keep the FD and FTC system effective, it

is essential to encapsulate different tasks and to

define strict interfaces between plant components

and between components of the monitoring and

diagnosis system, although it is quite difficult. To

guarantee flexibility -- changing needs in case of an

industrial application, the monitoring and FTC

system has to be configurable and expandable

without the need of modifying any line of code

(Luder, 2001). The diagnostic knowledge about an

industrial process is available on different parties

(process specialists, component manufacturers, etc.).

A modern FD and FTC system should be able to

integrate the diagnostic knowledge from all

available sources, even if different diagnostic

mechanisms are applied. To achieve an overall

diagnosis of a control process, several diagnostic

tasks have to be performed in parallel. This requires

new strategies to handle diagnostic conflicts that

might occur between different diagnostic results.

Multi-agent system (MAS), about which rapid

progress has been made, is an important research

branch in DAI parallelized with distributed problem

solving (DPS). Possessing modularity, adaptability

and other attractive characteristics, MAS drew much

attention in recent years and is adopted by many

researches in control systems.

This paper presents a hybrid intelligent mutli-

agent method for mrine fault tolerant control. The

architecture of the MAS, as well as the structure of

an agent, and the control diagram are designed in

section 2. Then, the algorithms encapsulated in the

information processing mudule of agents are briefly

mentioned in section 3. In section 4, simulation

experiments, applying the proposed method in

357

Tang T. and Yao G. (2008).

A MARINE FAULTS TOLERANT CONTROL SYSTEM BASED ON INTELLIGENT MULTI-AGENTS.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - ICSO, pages 357-362

DOI: 10.5220/0001506203570362

 SciTePress

marine FTC system, is carried out. And the

conclusion of this paper is made in section 5.

2 SYSTEM ARCHITECTURE

2.1 MAS Perspective

The framework of hybrid intelligent multi-agent

method, with hierarchical and federal organized

intelligent agents that are responsible for different

tasks, is presented in figure 1.

Figure 1: Architecture of MAS organization.

As shown in the figure, many agents with different

capabilities are connected together by accessing

FIPA Agent Platform and through communication

networks to form a MAS. In this system, each

individual has a special function that can work

autonomously and independently. Although being

fully autonomous, like human beings, on the other

hand, agents in the figure trend to seek cooperation

to fulfil more difficult task if they believe that better

payoff will gain by form a coalition or the job

assigned to some agents is impossible to achieve

with their own capability. To facilitate a coalition,

superadditive environment (Nicholas, 2007) is

assumed in this paper.

At agent level, all agents in proposed system

have the same hybrid architecture, where the agents

are capable of reactive and deliberative behaviours.

The proposed agent architecture is based on

horizontal layering where all layers are connected to

the perception and actuation of the agents with the

environment. In the reactive layer, an agent could

deal with urgent situations according to the rule in

its rules library while it will do some inference, in

normal case, in its information processing module

(IPM), which has been encapsulated with intelligent

algorithms and upon which different type of agents

are determined, according to the current mental state,

knowledge and goal in deliberation layer.

Details about the MAS and agent architecture

could refer to (Yao, 2007).

In figure 1, twelve species of agent, which are

determined by different algorithms in an agent’s

IPM, are involved: MAs are mainly used in FIPA

Platform to manage the life cycle of agents; FAs

provide yellow page services; DAAs are responsible

for interoperation with database; RGUIAs and

LGUIAs are graphic user interface extendedly and

locally respectively; FDAs are in charge of diagnosis

faults for the whole system; FTCAs are going to do

fault tolerant control in case that the system actuator

lost efficiency. SEAs are supposed to replace failed

sensors by estimating state signals and feedback

them to keep the system stable; SIAs collaborate

with the FTCA to do fault tolerant control; SCAs

answer for switching feedback signals from failed

sensors to SEAs; OMAs maintain term mapping

tables between different domain ontologies; CRAs

resolve conflicts in diagnosis results reported by

different FDA. When faults exist in a control system

or a user instruction is issued, all the above species

of agent will act autonomously and could

communicate to form coalitions to fulfil the task

with lease cost.

2.2 Control System Perspective

The architecture of MAS was discussed in last

section. In order to illuminate how those agents

implement fault tolerant control in a control system,

this section describes MAS based FTC system

architecture in a state feedback control system

perspective, as shown in figure 2.

In the figure, r is system reference; u is the

output of an adaptive fuzzy neural network (AFNN)

based FTCA, which acts as system controller; OV is

omen variables collected by fault diagnosis agents;

is the difference between x (the detected value

from system sensor) and x

(the output value of an

output recurrent neural network (ORNN) based

SEA), which is use by IPM of SEA to train the state

OMA

FDA

FTCA

SEA

LGUIA

RGUIA

CRA

DAA

Communication

Networ

SCA

SIA

MA-Management Agent; RGUIA/LGUIA-Remote/Local

Graphic User Interface Agent; FDA-Fault Diagnosis Agent;

SCA-State Control Agent; FTCA-Fault Tolerant Control

Agent; SEA-State Estimate Agent; FA-Facilitate Agent;

OMA–Ontology Mediate Agent; SIA-System Identification

Agent; CRA-Conflict Resolution Agent; DAA-Database

Access A

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estimation neural network on line; SCAs are used to

cut off failed sensors.

Figure 2: Architecture of MAS based FTC system.

In this FTC system there are three distinct parts that

differ from traditional control systems: fault

diagnosis agents to diagnose system failure, state

estimation agents to estimate state approximately

when the sensor failed, and system self-adaptive

controller, realized by FTCA, for FTC under

actuator faults:

(1) In the LIF based FDA, two kinds of faults,

actuator faults and sensor faults will be diagnosed. If

actuator faults happened, FTCA will take some

responding actions according to the fault state.

While the sensor faults have been found, the SEA

will play a part in the sensors.

(2) A SEA based on an output recurrent neural

network is proposed to provide the system states

estimation to replace the fault sensors. Usually, SCA

controls the switch to be on the position 2. In this

case, the system state feedback signals will be from

sensors. If the sensor faults occur, SCA will switch

to the position 1. In this case, the signals from SEAs

will be used as the system feedback.

(3) The FTCA, system controller, will give

different control strategy according to the system

state and fault cases to keep the system performance.

For example, when a fault from the actuator was

detected, the FTCA based on AFNN could adjust the

control signal to overcome the influence of the fault

according to the system response on line.

3 INTELLIGENT ALGORITHMS

OF AGENTS

As mentioned above, algorithms encapsulated in an

agent’s IPM determines its functions. In the IPM of

a FDA, multi-layer information fusion technology is

adopted for fault diagnosis, which separated fault

diagnosis into two parts: local diagnosis fusion

implemented by multi-sensors fuzzy inference and

global diagnosis fusion implemented by a three-

layer fuzzy neural network. In SEA, a new output

recurrent neural network is designed to construct the

system state estimator. For FTCA, a self-adaptive

fuzzy neural network is proposed as its information

process method. Detail about these solutions could

refer to (Yao, 2006)

4 SIMULATION EXPERIMENTS

4.1 Experiment Platform

In this simulation, the MAS framework is coded in

JADE platform, a software development framework

for agent application developed by TILAB. The

algorithms mentioned in section 3 are coded in

Matlab 6.5.

To implement calling Matlab methods from

JADE, JMatLink, a small toolkit to connect Java

with Matlab, is used to call for the functions in an

m-file.

The main user interface of MAS compiled in

JADE is shown in figure 3.

4.2 Working Flow of MAS

A prominent advantage of MAS is that agents could

discover an optimized way to fulfil tasks by

negotiation, coordination and cooperation via

sending messages. Accordingly, the communication

and cooperation between agents are the most two

important research topics regarding MAS.

The communication among agents, as well as

their knowledge and mental state, is based upon

domain ontology. But different ontologies regarding

one domain may exist sometimes in a system. These

ontologies contain different terms, which engender

great obstacles for agent communication, to express

same or similar concepts. Dealing with this problem,

a method called term substitution based on

intelligent ontology mapping is proposed and

implemented by ontology mediation agent (OMA).

OMA maintain glossary mapping tables between

domain ontologies. When an agent receives an ACL

message containing a few baffling words, the agent

forwards this message to OMA for interpretation. If

OMA could find terms in an ontology upon which

this agent based corresponding to those baffling ones,

it will substitute them and send the message back.

And then, the agent will understand the message

meaning.

AFNN-Adaptive Fuzzy Neural Network

ORNN-Output Recurrent Neural Network

LIF- Layered Information Fusion

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Figure 3: MAS interface in JADE.

Cooperation will bring agents more efficiency and

benefits, while coalition is an important solution to

implement it. As mentioned in section 2.1,

superadditive environment is assumed to facilitate

the coalition formation. Under this circumstance,

agents in MAS are inclined to form a grand coalition,

because they believe that they will earn payoff in

this coalition at least as much as those they will get

if they work alone. New task and payoff allocation

algorithms are also put forward. Details about these

solutions regarding with the agent communication

and coalition will be specified in future papers. And

some research results about these solutions have

been applied in agents work flow in this simulation

experiment, as illustrated in figure 4.

4.3 Marine FTC System Framework

The method proposed above had been used in

marine automatic steering system for actuator fault

tolerant control. The structure of ship FTC system is

shown in figure 5.

In order to represent the rudder faults, a

coefficient called as loss of efficient (LOE) is

introduced to simulate the faults. When the rudder is

normal, define LOE=0. And define LOE=1 if the

rudder is whole failure. So the coefficient LOE

expresses the fault degree of the rudder. In the

simulation system, the faults of the rudder servo

system could be set by fault factor L, which is

defined as L=1-LOE.

Figure 4: Flow chart of MAS based FTC process.

Figure 5: Marine FTC System.

Marine vehicles will definitely be disturbed by

ocean wave which is the uppermost factor to cause

ship rolling. Usually, wave motions on a ship can be

analytically computed using strip theory. The ocean

wave spectrum model adopted in the simulation is

shown in figure 6.

4.4 Simulation Results

During the simulation, Sniffer, a monitoring tool in

JADE, is used to sniffer the communication of

agents. The monitoring result is shown in figure 7.

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Figure 6: Ocean wave spectrum.

Figure 7: The communication of cooperated agents.

Some rudder faults with different degrees are used to

test the performance of the proposed system. In

these cases, the effect of FTC is simulated with and

without wave disturbances.

To judge how well the FTC method performs, a

PID controller is used to perform the same tasks

under the same conditions. Some simulation results

are shown in figure 8 ~ figure 11.

In order to make a detailed comparison between

the two kinds of controllers with rudder faults, the

rise times of the ship steering system are recorded to

evaluate the performances, where the rise time is

defined as the time taken for the ship to rise from

five to ninety five percent of the demanded yaw

angle. The rise times of the two controllers in

different fault conditions without and with

disturbances have been compiled in Table 1 and

Table 2 shown below.

Figure 8: Ship courses with PID controller (without

disturbance).

Figure 9: Ship courses with FTCA (without disturbance).

Figure 10: Ship courses with PID controller (with

disturbance).

A MARINE FAULTS TOLERANT CONTROL SYSTEM BASED ON INTELLIGENT MULTI-AGENTS

361

Figure 11: Ship courses with FTCA (with disturbance).

Table 1: Rise Times without Disturbances (s).

LOE =0 LOE =10% LOE =20% LOE =30%

PID

70 81 87.5 96.5

FTCA

70 70 70.5 71

Table 2: Rise Times with Disturbances (s).

LOE =0 LOE =10% LOE =20% LOE =30%

PID

75 84 90 98.5

FTCA

75 75.5 77 79.5

From the figure 7, it could be seen that different

types of agents in MAS followed the working flow

shown in figure 4 to work cooperatively. When a

FDA found there exists baffling terms in an ACL

message, it will ask for help to OMAs automatically.

If an agent finds that extra value will be gained if

tasks are re-assigned, it will send proposing message

to others about coalition by informing this value.

When the coalition is formed, tasks will be done and

coalition value will be divided according to each

agent’s contribution.

From the showings of figure 8, figure 10 and two

tables, it is easy to find that the PID control system

doesn’t have the fault tolerant capability; when the

rudder servo system lost some efficiency, the system

performance is greatly reduced with increased rise

time and stable time.

Figure 9, figure 11 and also those two tables

show that in the MAS based marine fault tolerant

control system, the system’s performance can be

distinctively improved in the condition of actuator

failure; it is nearly close to the normal performance.

The rising time and stable time are not influenced

much. The target of actuator fault tolerant control is

fulfilled.

From the simulation experiment above, it is easy

to find that the MAS based marine fault tolerant

control system can adapt to different failure modes

when the rudder servo system is partially failed. It

successfully realized fault tolerant control for

actuator failure by the multi-agents organization

shown in figure 1. The agents could communicate to

seek coalition autonomously. Adopting this MAS

based method will need no object’s mathematic

model and could realize cooperation between

heterogeneous intelligent algorithms.

5 CONCLUSIONS

This paper describes a concept of building a hybrid

intelligent fault tolerant control system for marine

vehicles based on the application of MAS, and also

proposed a new fault tolerant control schema

integrates several algorithms implemented within the

MAS method, which allows the flexibility, the

extendibility, and a cost-effective development of

the system. Details about the overall architecture,

algorithm encapsulated in IPM, and coding tools are

discussed. And at last, some simulation experiment

results are given to demonstrate the efficiency of the

presented system.

ACKNOWLEDGEMENTS

This work was supported by Natural Science Fund

of China (NSFC 60434020, 60572051); the

Education Key Project (07ZZ102) and the Education

Development Project (08YZ109) from Shanghai

Municipal Education Commission.

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