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 Course  Lecture
  • Title: Intelligent Systems and Control
  • Department: Electrical Engineering
  • Author: Prof. Laxmidhar Behera
  • University: IIT kanpur
  • Type: WebLink
  • Abstract:


    Course Objectives

    1. Biological motivation to design intelligent systems and control
    2. The study of control-theoretic foundations such as stability and robustness in the
    frame work of intelligent control.
    3. Analysis of learning systems in conjunction with feedback control systems
    4. Computer simulation of intelligent control systems to evaluate the performance.
    5. Exposure to many real world control problems.

    Course Outline

    * Module I (9 classes): Biological foundations to intelligent systems I: Artificial neural networks, Back-propagation networks, Radial basis function networks, and recurrent networks.
    * Module II (6 classes): Biological foundations to intelligent systems II: Fuzzy logic, knowledge representation and inference mechanism, genetic algorithm, and fuzzy neural networks.
    * Module III (6 classes): Fuzzy and expert control (standard, Takagi-Sugeno, mathematical characterizations, design example), Parametric optimization of fuzzy logic controller using genetic algorithm.
    * Module IV (6 classes): System identification using neural and fuzzy neural networks.
    * Module V (6 classes): Stability analysis: Lyapunov stability theory and Passivity Theory.
    * Module VI (4 classes): Adaptive control using neural and fuzzy neural networks, Direct and Indirect adaptive control, and Self-tuning Pill Controllers.
    * Module VII (5 classes): Applications to pH reactor control, flight control, robot manipulator dynamic control, underactuated systems such as inverted pendulum and inertia wheel pendulum control and visual motor coordination.

List of Lectures

Introduction To Intelligent Systems And Control
Linear Neural Networks
Multi Layered Neural Networks
Back Propagation Algorithm Revisited
Non Linear System Analysis Part I
Non Linear System Analysis Part Ii
Radial Basis Function Networks
Adaptive Learning Rate
Weight Update Rules
Recurrent Networks Back Propagation Through Time
Recurrent Networks Real Time Recurrent Learning
Self Organizing Map - Multidimensional Networks
Fuzzy Sets - A Primer
Fuzzy Relations
Fuzzy Rule Base And Approximate Reasoning
Introduction To Fuzzy Logic Control
Neural Control A Review
Network Inversion And Control
Neural Model Of A Robot Manipulator
Indirect Adaptive Control Of A Robot Manipulator
Adaptive Neural Control For Affine Systems Siso
Adaptive Neural Control For Affine Systems Mimo
Visual Motor Coordination With Ksom
Visual Motor Coordination - Quantum Clustering
Direct Adaptive Control Of Manipulators - Intro
Nn Based Back Stepping Control
Fuzzy Control - A Review
Mamdani Type Flc And Parameter Optimization
Fuzzy Control Of A Ph Reactor
Fuzzy Lyapunov Controller - Computing With Words
Controller Design For A T-s Fuzzy Model
Linear Controllers Using T-s Fuzzy Model
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