BMAS 2005 VHDL-AMS based genetic optimization of a fuzzy ...€¦ · VHDL-AMS based genetic...

Electronic Systems Design Group

University of Southampton, UK

School of Electronics and Computer Science

BMAS 2005BMAS 2005

VHDLVHDL--AMS based genetic optimization of AMS based genetic optimization of a fuzzy logic controller for a fuzzy logic controller for

automotive active suspension systemsautomotive active suspension systems

Leran Wang and Tom KazmierskiLeran Wang and Tom Kazmierski{lw04r,tjk}@ecs.soton.ac.uk{lw04r,tjk}@ecs.soton.ac.uk

BMAS, San Jose, CA, 22-23 Sep 2005 2

OutlineOutline

•• Introduction and system modelIntroduction and system model•• Shape optimization of fuzzy logic membership functionsShape optimization of fuzzy logic membership functions•• Integrated GA optimiser in VHDLIntegrated GA optimiser in VHDL--AMS AMS testbenchtestbench

implemented as a state machine implemented as a state machine •• Experimental results Experimental results •• ConclusionConclusion

Introduction and system modelIntroduction and system model•• VHDLVHDL--AMS recommended by a European automotive AMS recommended by a European automotive

consortium as a unified automotive consortium as a unified automotive modelingmodeling language language •• Automotive active suspension systemAutomotive active suspension system

• Active suspension• Actuator controller

Fuzzy logic controller

Fuzzy logic controller (FLC)Fuzzy logic controller (FLC)

• Based on the general principles of fuzzy set theory (L. Zadeh, 1965)• Input and output variables are similar to a conventional controller• Handling uncertain and complex systems, e.g. active suspension system

Fuzzy logic controller (FLC)Fuzzy logic controller (FLC)•• Regular membership functionsRegular membership functions

• Triangular

Fuzzy logic controller (FLC)Fuzzy logic controller (FLC)•• Regular membership functionsRegular membership functions

• Trapezoidal

Genetic algorithm (GA)Genetic algorithm (GA)

•• Optimization method based on natural Optimization method based on natural selection (D. Goldberg, 1989)selection (D. Goldberg, 1989)

•• A GA usually has the following elementsA GA usually has the following elements• Population of chromosomes• Selection according to fitness• Crossover to produce new offspring• Random mutation of new offspring

Active suspension modelActive suspension model

Sprung and Sprung and unsprungunsprung mass equationsmass equations

FLCFLC

••Inputs: sprung mass velocity and accelerationInputs: sprung mass velocity and acceleration••Output: actuator forceOutput: actuator force••Three linguistic variables: Three linguistic variables: PositivePositive (P), (P), ZeroZero (Z) and (Z) and NegativeNegative (N)(N)••Fuzzy rules setFuzzy rules set••MaxMax--product inferenceproduct inference••CenterCenter of gravity of gravity defuzzificationdefuzzification

PPPPZZNN

PPZZNNZZ

ZZNNNNPP

VelocityVelocity

NNZZPP

AccelerationAcceleration

Shape optimization of fuzzy logic membership functionsShape optimization of fuzzy logic membership functions

••Fuzzy logic membership functionFuzzy logic membership function• Graphical representation of input’s degree of participation in a

fuzzy set• Shapes may affect FLC performance (A. Barr and J. Ray, 1996)• Shape optimization using a GA

Shape optimization of fuzzy logic membership functionsShape optimization of fuzzy logic membership functions

Integrated GA optimizer in VHDLIntegrated GA optimizer in VHDL--AMS AMS testbenchtestbench

••Integrated hardware system performance optimizer wholly Integrated hardware system performance optimizer wholly implemented in VHDLimplemented in VHDL--AMSAMS

• Active suspension system• FLC• GA optimization

Block diagram of one chromosome (VHDLBlock diagram of one chromosome (VHDL--AMS entity) AMS entity)

Flow chart of a parallel GAFlow chart of a parallel GA

• Evaluation – using peak-to-peak value of xs(t) as fitness• Tournament selection – chromosomes with small xpp are more

likely to be selected to produce offspring• Elitism – artificially inserting the best solution into each new

generation• Arithmetic crossover – generate new offspring for real number

genes• Gene mutation – introduce new solutions into the next population

••VHDLVHDL--AMS finite state machine controls the optimizerAMS finite state machine controls the optimizer

GA features:GA features:

Experimental resultsExperimental results••GA optimized membership functions with irregular shapesGA optimized membership functions with irregular shapes

• Velocity

Experimental resultsExperimental results••GA optimized membership functions with irregular shapesGA optimized membership functions with irregular shapes

• Acceleration

Simulation waveformsSimulation waveforms

PeakPeak--toto--peak and RMS values of peak and RMS values of xxss(t)(t)

6.46.436.036.0TrapezoidalTrapezoidal

6.26.235.735.7TriangularTriangular

4.64.628.028.0GA optimizedGA optimized

RMS (mm)RMS (mm)PeakPeak--toto--peak (mm)peak (mm)FLC typesFLC types

ConclusionConclusion

•• A novel way to improve FLC performance developed and A novel way to improve FLC performance developed and successfully implemented in an HDLsuccessfully implemented in an HDL•• Novel approach to hardware performance optimisation Novel approach to hardware performance optimisation proposed and implemented proposed and implemented

• Integrated VHDL-AMS optimiser using parallel GA•• New type of FLC with irregular membership functions proposed New type of FLC with irregular membership functions proposed for automotive active suspension systemfor automotive active suspension system

• Superior performance to conventional FLCs with triangular or trapezoidal membership functions

• More than 20% improvement in the peak-to-peak value of sprung mass displacement

Thank you!Thank you!

BMAS 2005 VHDL-AMS based genetic optimization of a fuzzy ...€¦ · VHDL-AMS based genetic...

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