Evolution of Intelligent Control in Robotics

Intelligent Control, Its evolution, Recent Technology on Robotics M.Yamakita Dept. of Mechanical and Control Systems Eng. Tokyo Inst. Of Tech.

Key Technology of Intelligent Control 1. Machine Learning (Iterative Learning Control+ Q-Learning ) 2. Physically Inspired Non-Linear Control ( Passivity Based (Adaptive ) Control) 3. Fuzzy Control : Stability Issue 4. Evolutional Algorithm 5. Hybrid System

Organization Level ORGANIZER DISPACHER Coordination Level MOTION COORDINATOR VISION COORDINATOR PLANNING COORDINATOR … MOTION CONTROLLER VISION CONTROLLER COMMUNICATION CONTROLLER … Execution Level ACTUATORS VISION HARDWARE NETWORK HARDWARE … Hierarchical Intelligent Control PRECISION INTELLIGENCE

y,r t Error Execution Level Coordination Level (PID etc.) Iterative Learning Control (ILC) r(t) y(t) C P If the same operation is repeated, can we reduce the error based on the error of the previous trial ?

Structure of ILC Plant Learning Filter Memory - +

a1,a2,a3 a1 S2 S1 a3 a2,a3 S4 S3 a2 a1 Q-Learning Statistic Iterative Optimization Method Learning of optimal sequence of actions Q table

Learning Rule Action Section Randomly select action j at state i by a probability (T is artificial temperature) Update of Q-Table aj r(si,aj) is positive reward Si S’

New Representation of Systems State Space Represenation Port-Controlled Hamiltonian System Representation

Shift of Equilibrium State Disturbance Attenuation

Control Example Mechanical Equation Generator Electrical Dynamics

B C A D A B B A C C E D E D E Evolutional Computation (EC) Optimization Method inspired by Gene Dynamics Example：　Travel Salesman Problem Coding ・・ . .

Evolutional Operations (I) Selection and Duplication n g1 g2 gn

Evolutional Operations (II) Permutation Optimization Process START Coding Evolutional Operation Mutation Good Gen ? END

Stability Issue of Fuzzy Control Takagi-Sugeno Model If x is M11 and x is M12 then . . If x is Mn1 and x is Mn2 then Singleton Fuzzifier + Product Inference + Weighted Average Deffuzifier

Sufficient Condition of Stability of TS Model [Theorem] If there exists a positive definite matrix P satisfying then the TS mode is globally asymptotically stable.

Proof of the Theorem Let consider a following criterion function as a candidate of Lyapunov function: Time derivative of the function along the trajectory is given by From the Lyapunov stability theorem, we have the conclusion.

Controller Automaton Interface Actuator Generator Plant D/D System Hybrid System Roughly Speaking Hybrid System = Automation + Differential/Difference Eq.

Formal Definition of a Hybrid System Controller (Discrete Event System:DES) Plant

Generation of Event State transition of controller is occurred immediately when is generated. Detection of event Generation of event Generation of input

Simple Example (Temperature Control) 35 30 off on V

References • Watkins eta.: Technical Note: Q-Learning, Machine Learning 8, pp. 279/292 (1992) • M.Yamakita and K.Furuta: • T.Shen eta. : Adaptive L2 Disturbance Attenuation of Hamiltonian Systems with Parametric Perturbation and Application to Power Systems, submitted to Asian Journal of Control (2000) • D.B.Fogel: Evolutionary Computation: A New Transactions, IEEE Trans. On Evolutionary Computation, 1-1, 1(1998) • S.S.Farinwata eta. Ed.:Fuzzy Control, Wiley (2000) • K.Hirota eta.: Soft-Computing as a Breakthrough, Vol.39, Mach 2000, J. of SICE (2000) (in Japanese)

Evolution of Intelligent Control in Robotics