LI Aijun

Introduce yourself   Where you from   Major   supervisor

Basic Information Course No Hours 40 （ ） College Automation Prerequisites Matlab/Simulink, Control System Instructor Li Aijun ( Professor) ： Cell phone: Office ： Room 211

Grading Policy Grades will be determined as follows: Assignment Points Attendance and in-class participation 1 0 % project50 % Final test40 %

Textbook and References The textbook for this class is Lecture notes “ Intelligent Control ”. However, the textbook will not be followed very closely even though it covers most of the topics. The following are useful references for this class (but they might be difficult to understand if you do not have the appropriate background). 1. Yong-Zai Lu. “Industrial Intelligent Control: Fundamentals and Applications”. John Wiley and Sons, Chichester Magnus Nørgaard, Ole Ravn, Niels K. Poulsen and Lars K. Hansen. “Neural Networks for Modelling and Control of Dynamic Systems” Springer-Verlag, London, Kevin M. Passino and Stephen Yurkovich. “Fuzzy Control”. Addison-Wesley – an Imprint of Addison-Wesley Longman, Inc Jerry M. Mendel. “Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions: An expanded and richer fuzzy logic”. Prentice Hall

Introduction - Conventional vs Intelligent Control Fuzzy control Neural Network control Expert Control Systems Contents

Lecture 1 Introduction

Distinguishing Between Conventional and Unconventional Control

1.1 Conventional Control Classical control theory –three-term (PID) controllers –used to control all kinds of devices, –cannot always satisfy the increasing complexity of plants and the demands Modern control –introduced in the early 1960s –Invaluable for finding solutions to well-structured control problems –Application-----disappointing and few

PID Control Proportional Control –Pure gain adjustment acting on error signal Integral Control –Adjust accuracy of the system Derivative Control –Adjust damping of the system

PID Control

1.1 Conventional Control relies on explicit mathematical model the model of the plant be simplified based on low order holistic models of the physical plant. controllers can only perform at their best at the nominal operating point of the plant gain-scheduling Adaptive controllers --periodic identification is re- quired

1.2 Unconventional control --Intelligent Control based on the knowledge and experience of human operators --- Soft Control based on a microscopic description of the controlled plant using differential or difference equations --- Hard Control

Automatic control development open-loop control Feedback control Optimal control Stochastic control Adaptive/robust control Self-learning control Intelligent control Complexity of control Intelligent control is the highest level

1.2 Unconventional control --Intelligent Control A fundamental difference between conventional and Intelligent Control distinct entities single entity

1.2 Unconventional control --Intelligent Control

Intelligent Control is the fusion of Systems Theory, Computer Science, Operations Research and Computational Intelligence

1.3 Intelligent Control Techniques Computational Intelligence provides the tools with which to make intelligent control a reality.

-Intelligent controllers use empirical models Instead of relying on explicit mathematical models -The fundamental problem in developing an intelligent controller is elicitation and representation of the knowledge and experience of human operators in a manner that is amenable to computational processing. 1.3 Intelligent Control Techniques

Definition of intelligent control system It is a more flexible control system which incorporates other elements, such as logic, reasoning and heuristics into the more algorithmic techniques provided by conventional control theory. Astrom, K J & McAvoy, T J. “ Intelligent control: an overview and evaluation ” in White D A et al: “ Handbook of intelligent control: neural, fuzzy adaptive approaches ”. (Van Nostrand, N.Y., 1992, pp.3-34)

IEEE ICS specification Intelligent control system(ICS) Technical Committee on Intelligent Control in IEEE Control System Society general characteristics of ICS: An ability to emulate human capabilities, such as planning, learning and adaptation.

1.3 Intelligent Control Techniques Basic elements of an intelligent controller

1.5 Autonomy and Intelligent Control a high degree of autonomy is required in intelligent Autonomy in setting and achieving goals is an importan characteristic of intelligent control systems. Autonomy of operation is the objective and intelligent control is the means to this objective. When a system has the ability to ac appropriately in an uncertain environment for extended periods of time without external intervention it is considered to be highly autonomous.

1.5 Autonomy and Intelligent Control Hierarchical structure of an Intelligent System Saridis’ principle of “increasing precision with decreasing intelligence ” hierarchical intelligent con- trol systems have three layers Distributed architecture

1.6 Knowledge-Based Systems techniques of Computational Intelligence Expert Systems Fuzzy Logic Artificial Neural Networks Neuro-fuzzy Systems Evolutionary Computation