Reconfigurable Fuzzy Automaton for Software Agents

Janos L. Grantner, Paolo A. Tamayo, Ramakrishna Gottipati, George A. Fodor

Presentation

By

Dr. Janos L. Grantner

WCCI/FUZZ-IEEE 06

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• Introduction

• HFB-FSM Model

• Intelligent Software Agents

• Reconfigurable Architecture Design

• Simulation Results

• Conclusion

Presentation Outline

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Introduction

The problems that characterize industrial process control innovation are:

1. Introducing new knowledge into a system2. Activating stored domain knowledge in an

autonomous way3. Validating the knowledge4. Recovering the system if the new,

activated knowledge is not suitable to handle the situation

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Introduction (contn’d)

These problems can be addressed using intelligent software agents with fuzzy automata • New knowledge can be implemented by adding agents

– New knowledge is introduced by means of states in the goal path of an event driven, sequential control algorithm

– Fuzzy automata is an effective approximation method to model continuous and discrete signals in a single theoretical framework

• Knowledge validation is achieved– By quantifying the degree of deviation from the nominal

operating conditions due to unexpected events– Execution monitoring is also performed with fuzzy automata

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Intelligent Software Agent

Object State

Application

To FSA-BROKER: architecture,Supervision, real-timeTo ALARM SERVERTo HMI Server

Connection to other objects

CommissioningPanel

All ports arebi-directional

All ports have a named type

ARCHITECTURE

APPLICATION

Fuzzy Automaton

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Hardware Implementation of Agents

• IP (Intellectual Property) modules are designed as generic fuzzy automaton agents

• Agents communicate via NoC (Network on Chip) to decrease the real estate needed for pathways on the chip

• Agent broker can be implemented on an FPGA

• A set of specialized architecture operations are needed to implement an agent broker on an FPGA

• Example of such implementation: NoC

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Synthesis of Network on Chip (NoC)• Input: IP components with cost figures: U1, U2, U3, V1, V2, V3

• Clustered constraints (clustering is NP-complete): U1,2,3 is one cluster, V1,2,3 is another cluster

• Communication Constrained Graph: Ui communicates with Vi, i=1,2,3

• Optimal synthesis – quadratic programming approach: have only one communication channel

• Method: CDCS (constrained driven communication synthesis)

• At present, software implementation takes minutes

u1

u2

u3

u1

u2

u3

v1v1

v2 v2

v3 v3

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HFB-FSM Model

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Example for Designing a Reconfigurable Fuzzy Automaton

• It is based upon the computational model of HFB-FSM

• Assumes a multi-fuzzy input and one fuzzy output (MISO) configuration

• Digital inputs and analog inputs with threshold are omitted at this point

• Each fuzzy input is mapped to a set of Boolean variables using the B-Algorithm (Fuzzy-to-Boolean mapping)

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Example (Contn’d)

k overlapping linguistic sub – intervals are mapped to n (n = 2k-1) non overlapping Boolean sub – intervals, and Xbi = 1 if the xc position of the fuzzy input maximum falls into Boolean sub – interval i(i = 1,…,n) and XBj = 0 for all j = i(j = 1,……,n)

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Example (Contn’d)

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Reconfigurable Architecture Design (Contn’d)

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Example (Contn’d)

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Example (Contn’d)

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Example (Contn’d)

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Example (Contn’d)

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Example (Contn’d)-Parameters

Component Description

Number of Fuzzy Inputs

Defines the number of fuzzy inputs of the system. This value also determines the number of parallel MOM and Interval Detection circuits.

ResolutionThe resolution of the degree of membership is resizable. This determines the number of bits needed to represent the degree of membership.

GranularityThis resizable property depends upon the number of elements in the universal set.

Boundary CountThis is the number of Boolean sub-intervals. It can be reset from problem to problem.

Boundary LimitsThe right-most element of each Boolean sub-interval. The total number of limits is equal to the Boundary Count.

Number of Fuzzy States

The number of fuzzy states in the particular state cluster to be implemented.

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Validation

Container Crane Problem• The Container-Crane problem simulates the

operation of transferring a container van from a ship into a railcar platform.

The Container-Crane problem is developed using the fuzzyTech software

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Validation (Contn’d)

Container Crane Problem• The two fuzzy inputs are

– Angle of displacement of the suspended load (X)• Left swing results in a negative angle• Right swing results in a positive angle

– Distance of the load from the rail car (Y)• Far , Near , Close (also the states of the system)

• Output is the power applied to the crane (Z)– Positive power, negative power and zero power

• A simplified HFB-FSM will have 3 states, each of which will be made of just one crisp state

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Validation (Contn’d)Rules Rule Description

For Crisp State 1 (far)

Rule 1 If X is zero and Y is far then Z is positive.

Rule 2 If X is negative and Y is far then Z is positive.

Rule 3 If X is positive and Y is far then Z is positive.

For Crisp State 2 (near)

Rule 1 If X is zero and Y is near then Z is positive.

Rule 2 If X is negative and Y is near then Z is positive.

Rule 3 If X is positive and Y is near then Z is negative.

Rule 4 If X is zero and Y is close then Z is zero.

Rule 5 If X is positive and Y is close then Z is negative.

Rule 6 If X is negative and Y is close then Z is zero.

For Crisp State 3 (close)

Rule 1 If X is zero and Y is close then Z is zero.

Rule 2 If X is positive and Y is close then Z is negative.

Rule 3 If X is negative and Y is close then Z is zero.

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Validation (Contn’d)

Normalized universal set

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Validation (Contn’d)

Present State

Next State

Fuzzy Input (Angle)

Fuzzy Input(Distance)

Fuzzy Output (Power) Defuzzified Value

1 1 Zero Far [0.0 0.0 0.0 0.0 0.5 1.0 1.0] 7

1 1 Negative Far [0.0 0.0 0.0 0.0 0.5 1.0 1.0] 7

1 1 Positive Far [0.0 0.0 0.0 0.0 0.5 1.0 1.0] 7

1 2 Negative Near [0.0 0.0 0.0 0.0 0.5 0.5 0.5] 6

2 2 Zero Near [0.0 0.0 0.5 0.5 0.5 1.0 1.0] 7

2 2 Negative Near [0.0 0.0 0.5 0.5 0.5 1.0 1.0] 7

2 2 Positive Near [1.0 1.0 0.5 0.5 0.5 0.5 0.5] 2

2 3 Zero Close [0.5 0.5 0.5 1.0 0.5 0.5 0.5] 4

3 3 Positive Close [1.0 1.0 0.5 0.5 0.5 0.0 0.0] 2

3 3 Negative Close [0.0 0.0 0.5 1.0 0.5 0.0 0.0] 4

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Validation (Contn’d)

Simulation

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Validation (Contn’d)

Inference and Model Building Operation Performance Summary

Type of Operation

Number of Rules per State

Size of 1 Fuzzy

State Rule

Number of Elements in the Universal Set

Number of Clock Cycles

Needed

Inference R NxN N N + K

Model Building

R NxN N (SxNxNxR) + 1 + K

Where : K is the constant overhead cycles when performing the operation, currently 4 clock cycles.S is the number of States

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Validation (Contn’d)

For the example:

• Inference will be N+K = 7 + 4 = 11 clock cycles. Model Building will be (SxNxNxR) + 1+K = (3x7x7x3) + 1+4 = 446 clock cycles

• At 100MHZ clock rate we can run approximately 220,000 Model Building Operations and 10 Million Inferences per second

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Conclusion• An intelligent software agent architecture with

fuzzy automaton was introduced• Online reconfiguration of this architecture is

needed to introduce new knowledge and for fault detection and identification and recovery

• IP (Intellectual property) modules are implemented on hardware in contemporary control systems

• Hardware implementation of a reconfigurable fuzzy automaton was presented.