Hybrid Control of Structures Utilizing Fuzzy Logic and Genetic Algorithms

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Hybrid Control of Structures Utilizing Fuzzy
Logic and Genetic Algorithms
David Shook Friday July 18, 2003
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Outline

• Background Information
• Proposed Ideas
• Simulations
• Results
• Conclusions

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Background Information

• Fuzzy Logic
• Whats so fuzzy?
• Hybrid Control
• Why are two heads better than one?
• Genetic Algorithms (GA)
• Evolution of data?

LOGIC
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Fuzzy Logic?

• Non-Classical form of control
• Utilizes simple If.Then statements to control
  a building
• Basic Example If velocity is large and negative
  and displacement is large and negative, then
  control force is large and positive

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Big Picture - Earthquake
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Near-Field VS Far-Field Earthquakes

• Near-Field Earthquake
• Categorized by
• Large amplitudes
• Long periods
• High velocity pulses
• Far-Field Earthquakes
• Categorized by
• Smaller amplitude and period
• More consistent movements

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Hybrid Control

• Switching Rules
• Optimized by GA
• Intelligent Fuzzy Optimal Active Control (IFOAC)
• For Far-Field Earthquakes
• Utilizes EQ prediction data, Control Interval
• Reflective Fuzzy Active Control (RFAC)
• For Near-Field Earthquakes
• Optimized by GA

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Big Picture - IFOAC
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IFOAC

• Intelligent Fuzzy Optimal Active Control
• Best for Far-Field Earthquakes
• Takes into account subjective arguments
• Economy
• Comfort of inhabitants
• How?
• Based of man-made rules that can be designed with
  such parameters in mind

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IFOAC

• Earthquake prediction
• Uses a control interval for prediction
• Optimal control variable for the next interval is
  determined by fuzzy membership functions
• Three memberships function based on
• Structural Response (Y) - objective
• Control Force (U) constraint
• Stroke of Actuator (S) constraint

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IFOAC

• These three factors enter a Fuzzy Maximizing
  Decision
• By these factors ideal control variables are
  produced and then the control force is calculated
• Why best for far-field earthquakes?
• Far-field earthquakes are slower in variation
• Thus using a control interval for EQ prediction
  is suitable
• The Pros Takes into account subjective
  arguments
• The Cons Not as effective in near-field
  situations

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Big Picture - RFAC
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RFAC

• Reflective Fuzzy Active Control
• Best for Near-Field Earthquakes
• Fuzzy Rules created and optimized by Genetic
  Algorithms
• The Pros More effective in Near-Field
  Situations
• The Cons Doesnt take into account
  subjective arguments

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Big Picture Switching Rules
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Switching Rules

• Simple algorithm to determine which control
  system to use
• IFOAC or RFAC
• Rules based on building response in terms of
  displacement and velocity
• a and b values are determined by G.A.

• n0 and n1 are determined by G.A.
• Determined to be either IFOAC or RFAC

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Big Picture Genetic Algorithms
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Genetic Algorithms

• Based of the idea of Survival of Fittest
• Single member of population is called a
  Chromosome of base10 numbers
• Evolution occurs as follows
• Population is created
• Some of the population dies off due to poor
  evaluations
• Crossovers and mutations produce offspring
• A new generations is produced and the cycle
  repeats until a predefined accuracy is reached

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G.A. Example
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G.A. Example
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G.A. Example
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G.A. Example
1 0 0 0 1
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G.A. Example
1 0 0 0 1
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G.A. Example
1 0 0 0 0
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Big Picture
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Computer Simulations

• Programs were custom made in Delphi by Mr. Takagi
• Three parts
• Optimization of RFAC by G.A.
• Optimization of Switching Rules by G.A.
• Actual simulation

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Previous Case

• Published in 2002
• Utilized similar techniques as explained above,
  but a different optimization method is used for
  the RFAC
• Results of Previous Case

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Proposed Ideas

• Match/Improve results from previous case
• How?
• Use different data for optimization of G.A.
• Vary IFOAC parameters
• Change target displacements in optimization

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Varying Parameters

• Positive results from optimization on different
  data
• Positive results from varying IFOAC parameters
• Ineffective results from changing target
  displacements
• Reducing the target displacements causes fuzzy
  systems to be ineffective

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Optimization of RFAC

• Optimization with the 1995 Kobe Earthquake was
  found to have the best results
• When tested in other earthquakes it had the best
  results
• Fuzzy limits allowed for best distribution of
  resulting output

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Variation of IFOAC Parameters

• Proposed optimal values for IFOAC
• 50 less than previous case
• Maximum Displacement 3 cm
• Maximum Velocity 75 cm/sec
• Maximum Control Force 100 ton force
• Why reduce these values ?
• By reducing the fuzzy range a more efficient
  and effective evaluation occurs

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Results El Centro
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Results

• Percent Reduction of Maximum Displacement in four
  proposed earthquakes

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Problems

• RFAC is neglected by control algorithms
• Why?
• RFAC maximum values are too large thus
  ineffective in evaluation
• Current G.A. program is inefficient in producing
  smaller fuzzy maximum values for RFAC while
  optimizing on one earthquake
• Resolution Use smaller fuzzy limits of RFAC as
  proposed in previous case

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Future Work

• Continue work in United States at Texas AM
  University
• Professor Roschke
• Neuro-Fuzzy control with MR Dampers
• Look for possible implementation of methods
  learned
• EX Genetic Algorithms, Hybrid Control

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Acknowledgements

• 2003 REUJAT Program
• National Science Foundation
• Professor Dyke
• Professor Christenson
• Kobe University
• Professor Kawamura
• Professor Tani
• Students
• Professor Roschke, Texas AM

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• Questions

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Hybrid Control of Structures Utilizing Fuzzy
Logic and Genetic Algorithms
David Shook Friday July 18, 2003