BEE4333 Intelligent Control

1. BEE4333 Intelligent Control Hamzah Ahmad Ext: 2055/2141

2. Course Outline

3. Reference

4. Achievements :PO1, CO1, CO5, LO1 Chapter 1 Artificial Intelligence: What is it all about?

5. Today contents and achievements • LO1 : Able to understand intelligent system and its application 1.1 Overview of artificial intelligence (AI)

6. According to the Oxford and Penguin English Dictionaries the word “intelligence” can be defined as follows: • ability to understand • reason • perceive • quickness in learning • mental alertness • ability to grasp relationships • clever • information • news

7. One way to understand “intelligence” is by looking at our own capabilities, which means that humans are able to: • think • understand • recognize • perceive • generalize • adapt • learn • make decisions • solve daily problems

8. Artificial Intelligence (AI) ? AI is a study about inventing machines/computers that capable of mimicking human/animal intelligent behavior. http://xmb.stuffucanuse.com/xmb/viewthread.php?tid=6825 The ultimate objective is to develop a system that can think and act rationally like humans.

9. How do we design Intelligence? • Study from biological models (brain, genetic, DNA, life, Molecular biology, ….)  neural nets, GA, Artificial Life, DNA Computing, Quantum Computing, Robotics, etc. • Study from human phenomena (common sense, reasoning, predicting, observing, inference, …)  fuzzy logic, expert systems, search techniques, etc. • Need to develop mathematical/logical algorithms based on the above biological models or phenomena

10. Achievements :PO1, CO1, CO5, LO2 Chapter 1 Artificial Intelligence: Comparison between Classical control and intelligent control

11. Today contents and achievements • LO1 : Able to understand intelligent system and its application • LO2 : Able to compare classical control system and modern intelligent system 1.2 Artificial intelligence applications 1.3 Comparison with classical controller

12. Intelligent Control  Classical Control

13. Intelligent Control  Classical Control Classical Control INTELLIGENCE Intelligent Control

14. Some Pre-requisites in understanding AI • Some mathematical background • HLL Programming • Discrete-time systems • Some aspects of biological systems as well as philosophy and psychology • ………..

15. Where AI can/should be applied? • Data is overwhelming/abundance • Too many manual operations/procedures • Optimization is possible • Parallel/Distributed procedures/architectures are needed • Decision making is required • When current techniques are too complicated to be used/designed

16. Where AI can/should be applied? .. Cont’d • Mathematical models are too complex/impossible • To increase efficiency • To reduce cost • To improve performance and reliability

17. Some Important Facts, you need to know…. • AI is not the only solution • AI is only one part of technology • AI is just a tool for improvement • You must know your domain/target application

18. Expert systems Fuzzy logic Neural networks GA …………. Expert systems Fuzzy logic Neural networks GA …………. Cognition (Algorithms) Intelligent Man-Machine Interface Perception (sensors) Intelligent Systems; Conceptual Design Execution TASKS Algorithms, computations Intelligent machine

19. AI : Some of the approaches • Expert system • Fuzzy Logic • Genetic Algorithm • Swarm Intelligence • Ant Colony • etc

20. Expert System • Expert System (ES)is a branch of Artificial Intelligence that attempt to mimic human experts specifically in decision making process based on prior knowledge. • Expert systems can either support decision makers or completely replace them. • Expert systems are the most widely applied & commercially successful AI technology.

21. Types of ES • Ruled Based Expert System • Represented as a series of rules • Frame-Based System • Representation of the object-oriented programming approach • Hybrid System • Include several knowledge representation approach • Model-Based System • Structured around the model that stimulates the structure and function of the system under study • Ready-Made (Off-the-shelf) System • Custom-made, similar to application package such as an accounting general ledger or project management in operation mgmt.

22. Rules as a knowledge representation technique • The term rule in AI, which is the most commonly used type of knowledge representation, can be defined as an IF-THEN structure that relates given information or facts in the IF part to some action in the THEN part. A rule provides some description of how to solve a problem. Rules are relatively easy to create and understand. • Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).

23. Rules can represent relations, recommendations, directives, strategies and heuristics: • Relation IF the ‘fuel tank’ is empty THEN the car is dead • Recommendation IF the sea is very deep AND the sky is cloudy AND the forecast is danger THEN the advice is ‘do not go to the sea’ • Directive IF eat too much raya cakes, rendang AND the stomach is always aching THEN the action is ‘fasting in Syawal’

24. Fuzzy logic : human reasoning process (approximation) • Differs from binary set theory(true or false, or 1 or 0) • Similar to probability but not same in concept. • Fuzzy (degree of truth) • Probability (likelihood) • An elements might have partial characteristics of others or a subset of something but non-fuzzy is more deterministic.

25. Biological Network Artificial Neural Network 1 n An artificial neural network is a universal function approximator. Weights Wijare “learned” to fit any function. x1 a1 O1 x2 O2 a2 • • • xj ak Om 1 ji n kj W W • • •

26. ANN INPUT Weights Computation Node(s)/Neuron OUTPUT

27. Briefing about… AI in Industries

28. 3 General Types of Industries • Companies engaged in manufacturing • Automotive Parts ・Industrial Equipment • High-tech Industry ・Heavy Equipment • Planes … and others

29. Advantages of Adding Intelligence in Products/ Systems • Better performance • Longer Life • Reliability • Simpler operation • Cost effective • Higher efficiency • Self-organizing / self-optimization • Simpler design

30. Is there really a need for AI? • Manufacturers need to improve on their products • Need to satisfy customers • Need to improve products’ reliability • Need to improve products’ performance • Need to improve products’ features • Need to distinguish their products away from their competitors

31. 1980s Excellent Quality Expensive Leadership Balance of Payments High Technology Innovations 1960s Junk Cheap Poor Quality Copies Low Technology Imitation Made in Japan(based on a video on innovations and the need for change)

32. World Industrial Leaders by Country(1975-2000)(MIS for the Info Age) In 1994 alone Japan sold US$34 billion worth of consumer products using fuzzy logic technology

33. ASIMOAdvanced Step in Innovative MObility Camera Eyes [AI] Antenna Battery (Fuel Cell) Gyro Sensor Measuring Body Angle Actuators and Other Peripheral Systems Controlling leg movements [AI] Load Sensors In Leg Intelligent Real-time Flexible Walking [AI]

34. The New ASIMO The key features of the new ASIMO include:Advanced communication ability thanks to pattern recognition technology 1. Recognition of moving objects    4. Sound recognition 2. Posture/gesture recognition    5. Face recognition 3. Environment recognition      

35. Mitsubishi’s- Annanova

36. Sony’s AIBO

37. Fujitsu’s – HOAP (Miniature Human Robot) PINO ROBOTS

38. Sony’s SDR (Sony Dream Robots) • SDR-4X II • Introducing 4 new technologies • Small robot actuators (ISA-4) • Real-time Integrated Adaptive Motion Control • Motion creation software • Real-time Real World Space perception • Multi-modal Human-Robot Perception

39. SDR - specifications

40. Intelligent Servo Actuators (ISA-4)

41. Other Features • Multi-face Detection • Emotional Expression

42. Some Postures of SDR

43. ROBOT Speed (Fast) Not tired-Can do repetitive job (Fuel Cell) Not imaginative/Not creative Better speech and pattern recognition Some emotion Entertainment Personal Friend HUMAN Slow Intelligent Easily tired Imaginative/Creative Emotional Desire Etc. …… Future Research in Humanoids

44. Issues to be considered… • Do not apply AI when • Lack of Data • Simpler techniques are available / sufficient • Further optimization is not possible • The AI Machine faulty • Are Robots More Intelligent than Humans? • Can Robots Replace Humans? • Human vsMachine

45. AI Applications • Group Assignments ( 1 hour ) • Freshen up your industrial attachments… • List down industries application which use the classical control in their company. • Identify any of the application in the industries that has applied AI in their system • Propose any one of the AI method for any suitable/appropriate system in industries and explain why did you choose the proposed technique.

46. AI Applications • Select a specific machine/system and propose AI technique to make the machine/system more intelligence. • You need to draw/sketch the machine and show where to apply the AI technique • Explain why you should apply AI in the machine/system

47. What have you learned today? • AI in three(3) techniques; ES, Fuzzy, ANN • Description about each techniques • Differences between AI and Classical control • Applications of AI in industries • Capability to analyze and proposing new technology to the industries Achieving CO1 and LO1, LO2