Intelligent Control Methods

Lecture 1: Introduction. Reasons for ICM, Basic Concepts

Slovak University of TechnologyFaculty of Material Science and Technology in Trnava

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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)

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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:

)()()(12

2 tutudttduRC

RCssUsUsS

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)()()(

1

2

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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:

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Relation between the image of the output parameter and the input parameter by zero start conditions

Image Transmission:

)()()(

1

2

sUsUsS

dtetxtxLsX st

0

)())(()(

Vocabulary for Laplace-transformation available!

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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:

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Control Loop with Negative Feed-Back:

C(s) Image transmission of the controller S(s) Image transmission of the controlled

systemTransmission of control circuit with NF-B:

w(t) + v(t) +h(t) + e(t) u(t) y(t) - + +C(s) S(s)

)()(1)()(

)()(

sSsRsSsR

sHsY

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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:

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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:

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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):

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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):

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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:

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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:

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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)

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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.

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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.