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

2. 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
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3. 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
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4. 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 (
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5. Structure Of Intelligent Control
１． Hierarchical Intelligent Control (Albus, Saridis)
2. Reactive Intelligent Control (Brooks)
(Subsumption Architecture)
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6. Hierarchical Intelligent Control (Saridis)
INTELLIGENCE
PRECISION
Organization
Level
Coordination
Execution
MOTION
COORDINATOR
VISION
COMMUNICATION
CONTROLLER
ACTUATORS
HARDWARE
NETWORK …
ORGANIZER
DISPACHER
PLANNING
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7. 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
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8. 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)
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9. Dynamical System Representation (State Space Representation)
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10. Robust Optimal Control
Set of uncertain systems
A nominal system
Model set of uncertain systems
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11. Adaptive Control
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12. Symbolic System Representation (Rule Based Representation)
Area3
Area2 ?
Area1
Classical AI, Automaton etc.
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13. Crisp Logic vs. Fuzzy Logic
Tall
Mr.A
１８０ｃｍ
１７０ｃｍ ？
Mrs.B （
１７０ｃｍ
１６０ｃｍ
Mr.C
Short
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14. 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
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15. Introduction of membership functions
Degree of
property
100%
50% ) x
160
170
180
Height
Short
Tall
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16. 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 !
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17. Mimic the brain function !D
Mimic the brain function !
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18. No Hidden Layer Adjustable Weights Activation Function
（Rosenbratto　Type　Perceptron）
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19. Multi Layered Neural Network
Adjustable　Weights
Activation Function .
Generalized delta rule, Back-propagation algorithm
(Amari, Rumelhalt)
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20. 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)
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