Intelligent Control, Its evolution, Recent Technology on Robotics

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Presentation transcript:

Intelligent Control, Its evolution, Recent Technology on Robotics M.Yamakita Dept. of Mechanical and Control Systems Eng. Tokyo Inst. Of Tech. 2019/2/23

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 2019/2/23

Hierarchical Intelligent Control INTELLIGENCE PRECISION Organization Level Coordination Execution MOTION COORDINATOR VISION COMMUNICATION CONTROLLER ACTUATORS HARDWARE NETWORK … ORGANIZER DISPACHER PLANNING 2019/2/23

(PID etc.) Iterative Learning Control (ILC) r(t) y(t) C P t y,r Coordination Level Execution Level (PID etc.) Iterative Learning Control (ILC) r(t) y(t) C P t y,r Error If the same operation is repeated, can we reduce the error based on the error of the previous trial ? 2019/2/23

Structure of ILC Plant Learning Filter Memory - + 2019/2/23

Q-Learning Statistic Iterative Optimization Method Learning of optimal sequence of actions Q table S1 S2 S3 S4 a1 a2 a3 a2,a3 a1,a2,a3 a1 a2 a3 S1 Q(1,1) S2 S3 S4 2019/2/23

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’ 2019/2/23

New Representation of Systems State Space Represenation Port-Controlled Hamiltonian System Representation 2019/2/23

Shift of Equilibrium State Disturbance Attenuation 2019/2/23

Control Example Mechanical Equation Generator Electrical Dynamics 2019/2/23

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Evolutional Computation (EC) Optimization Method inspired by Gene Dynamics Example: Travel Salesman Problem A B C D E Coding A B C D E ・ 1 2 3 4 5 A ○ B C D E B A C E D . 2019/2/23

Evolutional Operations (I) Selection and Duplication n g1 g2 gn 2019/2/23

Evolutional Operations (II) Permutation Optimization Process START Coding Evolutional Operation Mutation Good Gen ? END 2019/2/23

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 2019/2/23

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. 2019/2/23

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. 2019/2/23

Hybrid System Roughly Speaking Hybrid System = Automation + Differential/Difference Eq. Controller Plant Actuator Generator Interface Automaton D/D System 2019/2/23

Formal Definition of a Hybrid System Controller (Discrete Event System:DES) Plant 2019/2/23

Generation of Event State transition of controller is occurred immediately when is generated. Detection of event Generation of event Generation of input 2019/2/23

Simple Example (Temperature Control) 35 30 off on V 2019/2/23

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) 2019/2/23