Introduction To Intelligent Control M.Yamakita Dept. of Mechanical and Control Systems Eng. Tokyo Inst. Of Tech. 2018/9/21

Controlled system becomes more and more complex. It is almost impossible to represent mathematical differential and difference equation representation of the systems. Emergent technology is needed ! Intelligent Control 2018/9/21

Trends from ’60 Artificial Intelligence (AI) Crisp Logic Fuzzy Logic Symbolic Representation Non-Symbolic Representation (ANN) Control Theory Classical Control Theory （PID) Modern Control Theory Robust Control Theory Adaptive Control Theory Hybrid System Control 2018/9/21

What’s Intelligent Control ? (G.Saridis,1979) Information Processing Formal Language Planning Scheduling Management Dyn. Feedback Optimization Memory Dynamics Coordination AI CONTROL OR Intelligent Control Intelligent Control ≠ Fuzzy Control ( 2018/9/21

Structure Of Intelligent Control １． Hierarchical Intelligent Control (Albus, Saridis) 2. Reactive Intelligent Control (Brooks) (Subsumption Architecture) 2018/9/21

Hierarchical Intelligent Control (Saridis) INTELLIGENCE PRECISION Organization Level Coordination Execution MOTION COORDINATOR VISION COMMUNICATION CONTROLLER ACTUATORS HARDWARE NETWORK … ORGANIZER DISPACHER PLANNING 2018/9/21

Reactive Intelligent Control (Brooks) Modify the World BEHAVIORAL MODULE I R S RESET INPUT OUTPUT Suppressor Inhibitor Create Maps Discover New Area Avoid Collisions Move Around 2018/9/21

Supporting Technologies １．　Extensions of conventional control technologies Robust optimal control Adaptive control Learning control 2. New technologies FAN(Fuzzy, AI, and Neural network) technology (Fukuda) Soft computing (Zadeh) 2018/9/21

Dynamical System Representation (State Space Representation) 2018/9/21

Robust Optimal Control Set of uncertain systems A nominal system Model set of uncertain systems 2018/9/21

Adaptive Control 2018/9/21

Symbolic System Representation (Rule Based Representation) Area3 Area2 ? Area1 Classical AI, Automaton etc. 2018/9/21

Crisp Logic vs. Fuzzy Logic Tall Mr.A １８０ｃｍ １７０ｃｍ ？ Mrs.B （ １７０ｃｍ １６０ｃｍ Mr.C Short 2018/9/21

When we describe real world symbolically, there always exist ‘gray zone’ state. It is very difficult to describe the gray zone property by conventional crisp logic. Or, we must define undesirably many categories. Fuzzy Logic 2018/9/21

Introduction of membership functions Degree of property 100% 50% ) x 160 170 180 Height Short Tall 2018/9/21

Perceptron O1 O2 B B A C A C D A--B B--C C--D A--B　（A　is connected to B) B--C C--A Triangle NOT Triangle Human easily recognize O2 as triangle ! 2018/9/21

Mimic the brain function ! D Mimic the brain function ! 2018/9/21

No Hidden Layer Adjustable Weights Activation Function （Rosenbratto　Type　Perceptron） 2018/9/21

Multi Layered Neural Network Adjustable　Weights Activation Function . Generalized delta rule, Back-propagation algorithm (Amari, Rumelhalt) 2018/9/21

References 1.M.M.Gupta,N.k.Sinha：Intelligent Control Systems, IEEE Press. (1996) 2. K.Furuta et.：Intelligent Control, Corona Pub. (1988) （in Japanese) 3.B.Widrow, E.Walach: Adaptive Inverse Control,Prentice Hall (1996) 2018/9/21