1. Intelligent Control Methods Lecture 1: Introduction. Reasons for ICM, Basic Concepts Slovak University of Technology Faculty of Material Science and Technology in Trnava

2. 2 Classic Control Theory: C(s)Image transmission of the controller S(s)Image transmission of the controlled system w(t) + v(t) + h(t) + e(t) u(t) y(t) - + + C(s)S(s)

3. 3 Classic (proper):  60 years old  Based on the system external description (relation input – output)  Continuous systems: Differential equations (linear, non-linear) ⇨ Image transmission  Examples: RC-unit, liquid level Classic Control Theory:

4. 4 Modern:  30 years old  Based on the system internal description (relation input – state – output) x´(t) = A x(t) + B u(t) y(t) = C x(t) + D u(t) Classic Control Theory:

5. 5 Relation between the image of the output parameter and the input parameter by zero start conditions Image Transmission: Vocabulary for Laplace-transformation available!

6. 6 From differential equations (if available) As result of the system identification according to system standardized signals response  Dirac impulse (impulse characteristic)  Unit-pulse signal (transmission characteristic) Methods for image transmission estimation from impulse or transmission characteristic available! Image Transmission Estimation:

7. 7 Control Loop with Negative Feed-Back: C(s)Image transmission of the controller S(s)Image transmission of the controlled system Transmission of control circuit with NF-B: w(t) + v(t) + h(t) + e(t) u(t) y(t) - + + C(s)S(s)

8. 8 Starting point: Mathematical model (transmission) S(s) of the system Design of the controllers with the transmission C(s) so as the closed control loop has desired properties  Feed-back control quality (output time behavior should be similar to the desired one)  Stability of the controlled system Control Design in Classic Control Theory:

9. 9 Systems, approaches to description (first of all linear dynamic systems) System response to normalized input signals, system behavior appreciation according to response System stability, determination and criteria Transmission algebra (global transmission of more connected systems) Feed-back control loop Controllers synthesis, PID-controllers Feed-back control quality, kriteria Lectures in Classic Control Theory:

10. 10 Mathematical model needed (input-output, input- state-output) Model complicated or unsolvable  Non-linear  Too many parameters Time behavior of systems (models too) varies (parts mature, pipe-lines foul, supplies falter...) Problems of the Classic Control Theory (1):

11. 11 Not only deterministic but also stochastic system behavior Not all inputs controllable Control signals have physical restrictions (valves, supplies,...) Time delay (algebraic => exponential equations) Problems of the Classic Control Theory (2):

12. 12 Classic control theory supplies and complements with intelligent methods (soft computing) ICT are used (programming languages and environments, simulation, industrial programmable controllers, AI, NN, GA, fuzzy sets,...) The goal: to create systems  intelligent  optimal  adaptive  robust From above mentioned results:

13. 13 To simplify the mathematical model, or its replacement with description:  Linguistic description (fuzzy sets)  Modeling and simulation (simulating tools and environments, NN,...) To react on system time behavior changes  Adaptive methods To handle the uncertainty in system behavior (Bayes probability, fuzzy approach) To master symbolic (non-numerical) information It means:

14. 14 The mentioned properties allow to be used in all control levels: EIS DSS MIS PID-level Top-level control (Executive IS, ES, DSS) MIS, production processes (systems) control PID-level (technological processes control)

15. 15 ICM-lectures structure: 1. Introduction. Classic and modern CT, direction to ICM. 2. Artificial intelligence. 3. Problems solution in artificial intelligence systems (resolution method, state space) 4. Production systems. Rules chaining as solution method. 5. Expert systems. 6. Knowledge base design. Knowledge acquisition in databases. 7. Uncertainty. Bayes´s and fuzzy approach. 8. Fuzzy systems, fuzzy control. 9. Genetic algorithms. 10. GA in optimizing, control and regulation. 11. Neuronal nets (NN). 12. Process modeling with NN.

16. 16 Literature: 1. Nillson, N.J.: Principles of Artificial Intelligence. Addison-Wesley, London, New York, 1991. 2. Man, K.F., Tang, K.S., Kwong, S., Halang, W.A.: Genetic Algorithms: Concepts and Designs. Springer Verlag, London 1999. 3. Karr, C.L., Freeman, L.M.: Industrial Applications of Genetic Algorithms. Boca Raton, London, New York, Washington D.C., 1999. 4. ATP Journal plus 7/2005: Artificial Intelligence in Practise. Automation, Robotics, Mechatronics. Advanced Control Techniques, Discrete Manufacturing Systems.