Course

Intelligent Systems and Control

Indian Institute of Technology Kanpur

This course, titled "Intelligent Systems and Control," aims to provide a comprehensive understanding of designing intelligent systems by leveraging biological principles. The objectives of the course include:

  1. Understanding biological motivation for intelligent systems and control.
  2. Studying control-theoretic foundations such as stability and robustness within intelligent control frameworks.
  3. Analyzing learning systems alongside feedback control systems.
  4. Conducting computer simulations to evaluate intelligent control system performance.
  5. Gaining exposure to various real-world control problems.

The course is structured into several modules:

  • Module I (9 classes): Biological foundations to intelligent systems I, covering artificial neural networks, back-propagation networks, radial basis function networks, and recurrent networks.
  • Module II (6 classes): Biological foundations to intelligent systems II, including fuzzy logic, knowledge representation, inference mechanisms, genetic algorithms, and fuzzy neural networks.
  • Module III (6 classes): Fuzzy and expert control, focusing on standard and Takagi-Sugeno approaches, mathematical characterizations, design examples, and parametric optimization using genetic algorithms.
  • Module IV (6 classes): System identification using neural and fuzzy neural networks.
  • Module V (6 classes): Stability analysis through Lyapunov stability theory and Passivity Theory.
  • Module VI (4 classes): Adaptive control using neural and fuzzy neural networks, including direct and indirect adaptive control as well as self-tuning controllers.
  • Module VII (5 classes): Applications in pH reactor control, flight control, robot manipulator dynamic control, underactuated systems like inverted pendulum control, and visual motor coordination.
Course Lectures
  • This module serves as an introduction to the field of Intelligent Systems Control. It outlines the significance of intelligent systems in control applications.

    Key topics include:

    • Overview of intelligent systems and their applications.
    • Importance of biological inspiration in designing control systems.
    • Fundamental concepts of stability and robustness.
  • This lecture provides an in-depth exploration of linear neural networks. You'll learn about the architecture, functionality, and practical applications.

    Key learning points include:

    • Understanding the structure of linear neural networks.
    • Applications of linear networks in control systems.
    • Mathematical foundations and algorithms for training.
  • This lecture focuses on multi-layered neural networks, emphasizing their design and functionality. Students will gain insights into their advantages.

    Topics covered include:

    • Structure and layers of multi-layered networks.
    • Activation functions and their significance.
    • Training methods and backpropagation techniques.
  • This lecture revisits the Back Propagation Algorithm, a key method for training neural networks. Emphasis is placed on its mathematical foundations.

    Key elements include:

    • Step-by-step process of backpropagation.
    • Application in training multi-layered networks.
    • Common pitfalls and optimization strategies.
  • This module addresses non-linear system analysis, presenting methodologies for handling complex dynamic systems.

    Key topics include:

    • Understanding non-linear dynamics and their implications.
    • Analytical methods for stability analysis.
    • Case studies and examples of non-linear control applications.
  • This module continues to explore non-linear system analysis, diving deeper into advanced concepts and techniques.

    Highlights include:

    • Advanced techniques for analyzing non-linear systems.
    • Numerical methods for simulation and evaluation.
    • Applications in real-world control scenarios.
  • This lecture introduces Radial Basis Function (RBF) networks, focusing on their structure, functionality, and use in control systems.

    Key points include:

    • Understanding RBF network architecture.
    • Training methods and their applications.
    • Comparison with traditional neural networks.
  • This lecture discusses the concept of adaptive learning rates in neural networks, which is crucial for effective training and performance improvement.

    Topics covered include:

    • Importance of adaptive learning rates in training.
    • Strategies for implementing adaptive learning.
    • Impact on convergence and performance.
  • This module explores weight update rules in neural networks, key to enhancing network performance during training.

    Key areas of focus include:

    • Types of weight update rules and their applications.
    • Impact of different rules on learning efficiency.
    • Comparative analysis of weight update techniques.
  • This lecture covers recurrent networks and the concept of backpropagation through time, essential for training models that process sequences.

    Key points include:

    • Understanding the architecture of recurrent networks.
    • Application of backpropagation through time.
    • Challenges and solutions in training recurrent models.
  • This lecture further explores recurrent networks, focusing on real-time recurrent learning, a technique for continuous learning in dynamic environments.

    Topics include:

    • Principles of real-time recurrent learning.
    • Applications in real-time systems.
    • Advantages over traditional learning methods.
  • This lecture introduces self-organizing maps, a type of neural network used for unsupervised learning and clustering tasks.

    Key topics include:

    • Understanding the structure of self-organizing maps.
    • Applications in data clustering and visualization.
    • Comparison with supervised learning techniques.
  • This module introduces fuzzy sets, foundational elements in fuzzy logic and intelligent systems, essential for handling uncertainty.

    Key points include:

    • Definition and properties of fuzzy sets.
    • Role of fuzzy sets in decision-making.
    • Applications in control systems and artificial intelligence.
  • Module 2 Lecture 2 Fuzzy Relations
    Prof. Laxmidhar Behera

    This lecture focuses on fuzzy relations, which are crucial in modeling relationships between fuzzy sets and enhancing decision-making processes.

    Key topics include:

    • Understanding fuzzy relations and their properties.
    • Applications of fuzzy relations in various domains.
    • Methodologies for constructing and utilizing fuzzy relations.
  • This lecture introduces fuzzy rule bases and approximate reasoning, key components of fuzzy logic systems that facilitate inference and decision-making.

    Topics covered include:

    • Structure of fuzzy rule bases and their components.
    • Methods for approximate reasoning.
    • Applications in expert systems and control.
  • This module provides an introduction to fuzzy logic control, highlighting its principles and advantages in managing uncertainty in dynamic systems.

    Key components include:

    • Fundamental concepts of fuzzy logic control.
    • Applications in various engineering fields.
    • Comparison with traditional control strategies.
  • This lecture reviews neural control, providing insights into how neural networks can be utilized for control applications in various systems.

    Key topics include:

    • Overview of neural control systems.
    • Applications in robotics and automation.
    • Benefits and challenges of neural control.
  • This lecture discusses network inversion and control, focusing on methods to achieve desired system behavior through inversion techniques.

    Topics include:

    • Principles of network inversion for control.
    • Applications in various engineering fields.
    • Challenges in real-world implementation.
  • This lecture covers the neural model of a robot manipulator, emphasizing the use of neural networks for modeling and control of robotic systems.

    Key points include:

    • Understanding the dynamics of robot manipulators.
    • Applications of neural networks in robotics.
    • Benefits of using neural models for control.
  • This lecture focuses on indirect adaptive control of a robot manipulator, exploring strategies for adapting control parameters dynamically.

    Key components include:

    • Overview of adaptive control concepts.
    • Implementing indirect adaptive control strategies.
    • Applications in robotic systems and automation.
  • This module delves into adaptive neural control for Single Input Single Output (SISO) systems. Students will explore the fundamental principles of neural networks and their applications in control systems. Key topics include:

    • Introduction to affine systems and their characteristics
    • Designing adaptive controllers using neural networks
    • Implementation strategies for SISO systems
    • Performance evaluation and analysis of control systems

    By the end of this module, students will be equipped with the knowledge to design and implement adaptive neural controllers effectively.

  • This module focuses on adaptive neural control for Multi Input Multi Output (MIMO) systems. Students will learn about:

    • The complexity of MIMO systems and their control challenges
    • Advanced neural network structures for MIMO control
    • Techniques for adaptive control design
    • Real-world applications and case studies

    Students will gain hands-on experience in implementing MIMO control solutions using neural networks, enhancing their understanding of intelligent control systems.

  • This lecture covers the concept of visual motor coordination using Kernel Self-Organizing Maps (KSOM). Key topics include:

    • Fundamentals of visual motor coordination
    • Introduction to self-organizing maps and their applications
    • Utilizing KSOM for modeling coordination tasks
    • Case studies demonstrating practical applications

    Students will learn how KSOM can be employed to enhance robotic vision and control systems, leading to improved interaction and responsiveness.

  • This lecture introduces quantum clustering techniques for visual motor coordination. The content includes:

    • Overview of quantum clustering principles
    • Applications in visual motor tasks
    • Comparative analysis with traditional clustering methods
    • Case studies showcasing real-world applications

    Students will develop an understanding of how quantum clustering can enhance performance in intelligent control systems, particularly in dynamic environments.

  • This introductory lecture on direct adaptive control of manipulators focuses on:

    • Fundamental principles of adaptive control for robotic manipulators
    • Characteristics and challenges of manipulator systems
    • Direct adaptive control strategies and their implementation
    • Performance evaluation metrics for adaptive control systems

    Students will explore the concepts necessary to design and implement adaptive control systems specifically tailored for robotic applications.

  • This lecture presents neural network-based backstepping control techniques. Students will learn about:

    • Backstepping control methodology and its significance
    • Integration of neural networks in control design
    • Applications in dynamic system control
    • Case studies illustrating successful implementations

    By the end of this module, students will have a clear understanding of how to utilize neural networks for advanced control strategies in complex systems.

  • This module provides a comprehensive review of fuzzy control principles. Topics covered include:

    • Basic concepts of fuzzy logic and control
    • Fuzzy control system architecture and design
    • Performance metrics for fuzzy controllers
    • Applications of fuzzy control in real-world scenarios

    Students will gain insights into the effectiveness of fuzzy logic in control systems, preparing them for more advanced topics in fuzzy control design.

  • This lecture focuses on Mamdani-type fuzzy logic controllers and their parameter optimization. Key aspects include:

    • Introduction to Mamdani fuzzy controllers and their structure
    • Techniques for parameter tuning and optimization
    • Performance evaluation of fuzzy controllers
    • Case studies demonstrating effective parameter optimization

    Students will learn how to optimize fuzzy controllers to enhance control performance in various applications.

  • This module covers fuzzy control applications specific to pH reactor systems. Key topics include:

    • Overview of pH control challenges and requirements
    • Fuzzy logic strategies for pH regulation
    • Design and implementation of fuzzy controllers for reactors
    • Performance analysis and optimization techniques

    Students will gain practical knowledge on applying fuzzy control to maintain optimal pH levels in various industrial processes.

  • This lecture discusses the fuzzy Lyapunov controller and the concept of "computing with words." Topics covered include:

    • Introduction to Lyapunov stability theory in fuzzy control
    • Fuzzy logic applications in stability analysis
    • Computing with words and its implications in control systems
    • Case studies demonstrating the effectiveness of fuzzy Lyapunov controllers

    Students will learn how to leverage fuzzy logic for stability analysis, enhancing their understanding of control systems design.

  • This module focuses on controller design for Takagi-Sugeno (T-S) fuzzy models. Key topics include:

    • Overview of Takagi-Sugeno fuzzy model structure
    • Controller design methodologies for T-S models
    • Stability analysis and performance evaluation
    • Applications in various control scenarios

    Students will understand how to design effective controllers utilizing T-S models, preparing them for more complex control challenges.

  • This lecture discusses linear controllers using Takagi-Sugeno fuzzy models. Key aspects include:

    • Fundamental principles of linear control systems
    • Integration of T-S fuzzy models in linear control design
    • Performance metrics and evaluation techniques
    • Case studies showcasing practical applications

    Students will learn how to apply linear control principles using T-S fuzzy models, enhancing their control system design capabilities.