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Real time learning of vehicle suspension control laws 1997

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Real-time Learning of Vehicle Suspension Control Laws
Talk given in 1997 on applying learning automata so control

Published in: Automotive, Education, Technology
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Real time learning of vehicle suspension control laws 1997

  1. 1. Real-time Learning of Vehicle Suspension Control Laws M. N. Howell BSc MSc PhD G. P. Frost BEng T.J. Gordon BA MA PhD *Q.H. Wu MSc PhD
  2. 2. OUTLINE OF TALK • Introduction • Suspension Control Problem • Learning Control • Results • Conclusions
  3. 3. SUSPENSION CONTROL Objective: Improve Ride Comfort By Reduce Vehicle Body Acceleration
  4. 4. Types of Suspension • Passive Suspension Standard Spring and Damper • Active Force Generator Replaces Damper • Semi-Active Controllable Dampers
  5. 5. Motivation for Learning Control • Difficult System To Model • Non-linear • Interaction with Engine Modes • Noise Disturbance Input Measurement Noise • Uncertainty In System Parameters • System Identification is an Approximation
  6. 6. REINFORCEMENT LEARNING CONTROL • Direct Learning No System Model • On-Line Learning No Pre-Training • No Separate Training Stage Required
  7. 7. Pseudo Code Repeat Generate Actions Execute The Actions On The Environment Receive Performance Evaluation For This Action If "Favourable" Then Promote Action In Future Situations Until Convergence Criterion Satisfied.
  8. 8. CONTINUOUS ACTION REINFORCEMENT LEARNING AUTOMATA ( CARLA ) • Related To Discrete Learning Automata • Each CARLA Operates On A Single Action Variable • Continuous Parameter Range • Reward/Inaction Based • Suitable For Stochastic Systems • Distributed Control • Simple Implementation
  9. 9. Learning Implementation Environment Actions Performance Evaluation Function β J (Vehicle) CARLA CARLA CARLA
  10. 10. PASSIVE REFERENCE STAGE LEARNING CONTROLLER TRANSIENT RECOVERY STAGE PASSIVE REFERENCE STAGE STAGE LEARNING CONTROLLER STAGE ( 2 Seconds ) ( 8 Seconds ) ( 0.1 Seconds ) ( 2 Seconds )
  11. 11. Learning on Real Roads
  12. 12. Conclusions • Learning Approach Vindicated • Ride Improvement Achieved • Minimal System Modelling Required • Reduced Design Time • Robust to Noise

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