This elective course in electrical/electronics and communication engineering, "Advanced Control Systems," provides postgraduate students with a thorough understanding of advanced control system analysis and design.
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This module serves as an introduction to the field of advanced control systems, focusing on key concepts and their significance in engineering.
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By the end of this module, students should have a foundational understanding of the principles guiding advanced control systems and their relevance in real-world scenarios.
This module delves into various control structures and their performance measures, essential for designing effective control systems.
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Students will learn how to assess the effectiveness of different control strategies and their impacts on system behavior.
This module focuses on time and frequency domain performance measures, crucial for analyzing control system behavior.
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By mastering these concepts, students can effectively evaluate and enhance control systems' performance.
This module covers the design principles of controllers, focusing on both theoretical and practical aspects of control system design.
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By the end of this module, students will have the skills to create effective controllers tailored to specific control problems.
This module focuses on the design of controllers specifically for Single Input Single Output (SISO) systems, which are common in control engineering.
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Students will gain practical experience in designing and testing controllers for SISO systems, preparing them for real-world engineering challenges.
This module addresses controller design for Two Input Two Output (TITO) processes, which are more complex but critical in advanced control applications.
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Students will work on case studies to understand the intricacies of designing and implementing controllers for TITO systems.
This module discusses the limitations of PID controllers, which are widely used in control systems. While PID controllers are effective for various applications, they have certain constraints that can impact performance.
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Students will gain insights into scenarios where PID controllers may not be the best choice and explore alternative control strategies.
This module introduces the PI-PD controller specifically for Single Input Single Output (SISO) systems. It explains the fundamental principles of PI-PD control and how it differs from traditional PID control.
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Students will learn how to design and analyze PI-PD controllers tailored for specific SISO system requirements.
This module focuses on the PID-P controller for Two Input Two Output (TITO) systems. It examines the complexities and advantages of using PID-P controllers in multi-variable control systems.
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Students will explore how PID-P controllers can enhance performance in TITO systems and address common control challenges.
This module covers the effects of measurement noise and load disturbances on control system performance. Understanding these factors is essential for designing robust control systems.
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By the end of this module, students will be equipped with knowledge to analyze and improve control systems under adverse conditions.
This module introduces the identification of dynamic models of plants, which is crucial for effective control system design. Understanding plant dynamics is essential for accurate modeling and control.
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Students will learn various techniques and tools for dynamic model identification, essential for advanced control applications.
This module explores relay control systems for identification purposes. Relay control systems are pivotal in determining dynamic characteristics of plants and improving control strategies.
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By the end of the module, students will understand how relay control systems can aid in the identification of plant dynamics for better control implementation.
This module focuses on the process of off-line identification of process dynamics, which is crucial for understanding system behavior before implementation.
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Students will engage in practical exercises to apply theoretical concepts and enhance their understanding of system dynamics.
The on-line identification of plant dynamics module teaches students how to dynamically assess and respond to system behavior in real-time.
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This module emphasizes hands-on experience, allowing students to directly apply concepts in simulated environments.
This module introduces state space based identification, a powerful method for modeling and analyzing dynamic systems.
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Students will develop skills in applying these techniques to complex systems and understanding their advantages.
This module focuses on state space analysis of systems, allowing students to understand the behavior and performance of dynamic systems through state variables.
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Students will engage in simulations to enhance their analytical skills and understanding of complex systems.
This module delves into state space based identification of systems, enhancing students' ability to model dynamic systems using state space methods.
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Students will engage in case studies to apply their knowledge to real-world systems, developing practical skills.
This module continues the exploration of state space based identification of systems, providing deeper insights into advanced techniques and applications.
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The focus will be on developing a comprehensive understanding of state space approaches and their practical utility.
This module covers the identification of simple systems, focusing on the fundamental techniques used in system analysis. Students will learn:
By the end of this module, students will be equipped with the skills to identify and model simple systems effectively, laying the groundwork for more complex identification processes.
This module focuses on the identification of First-Order Plus Dead Time (FOPDT) models, which are essential for understanding dynamic systems. Key topics include:
Students will gain practical insights into how to apply these models for effective control strategies.
This module delves into the identification of second-order plus dead time (SOPDT) models, which are critical in control engineering. Students will learn about:
By the end of the module, students will understand the complexities involved in modeling second-order systems and how to address them in practical scenarios.
This module focuses on the identification of Second-Order Plus Dead Time (SOPDT) models with pole multiplicity. It covers advanced identification methods, including:
Students will explore how to accurately model systems with repeated poles, enhancing their capability to handle complex control challenges.
This module discusses the determination of steady-state gain from asymmetrical relay tests, a vital technique in control system analysis. Key components include:
By the end of this module, students will have practical knowledge on how to apply relay testing to identify system characteristics.
This module explores the identification of Second-Order Plus Dead Time (SOPDT) models with pole multiplicity, a complex yet critical area in control theory. Topics covered include:
Students will enhance their analytical skills and learn how to tackle the nuances of pole multiplicity in control system design.
This module discusses the existence of limit cycles in unstable systems, focusing on the conditions under which limit cycles can occur. Through analytical approaches and graphical methods, students will learn to identify and analyze these cycles.
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By the end of this module, students will understand how limit cycles can affect system performance and stability.
This module covers various identification procedures crucial for modeling systems accurately. Students will explore both theoretical foundations and practical applications of system identification.
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Students will engage in hands-on exercises to reinforce the concepts learned, ensuring they understand how to apply these identification techniques.
This module focuses on the identification of underdamped systems, which are common in many engineering applications. Participants will learn to distinguish underdamped systems and analyze their dynamic behavior.
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By the end of this module, students will be equipped to identify and model underdamped systems effectively.
This module delves into the off-line identification of Two-Input Two-Output (TITO) systems. Students will learn the significance of TITO systems in complex control scenarios and the techniques used for their identification.
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The module emphasizes practical examples to illustrate the concepts, providing students with a thorough understanding of TITO system identification.
This module covers on-line identification techniques for TITO systems, allowing students to understand how to dynamically adjust models based on real-time data.
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Students will gain hands-on experience with software tools used for real-time identification, enhancing their practical skills.
This module provides a review of time domain-based identification methods, revisiting key concepts and practical applications. Students will analyze various time-domain techniques and their relevance in control system modeling.
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The module ensures students can effectively utilize time domain methods for system identification and modeling.
This module explores the DF-based analytical expressions that facilitate on-line identification in control systems. It covers essential concepts such as:
Students will engage in practical examples and case studies to solidify their understanding of the concepts presented.
This module focuses on model parameter accuracy and sensitivity analysis in control systems. Key topics include:
Through extensive examples, students will learn to evaluate and improve model accuracy, providing a strong foundation for effective control system design.
This module introduces improved identification techniques utilizing Fourier series and wavelet transforms. The content includes:
Students will gain hands-on experience through practical exercises, enhancing their skills in advanced control techniques.
This module reviews DF-based identification methodologies, providing a comprehensive overview of their application in control systems. Key aspects covered include:
Students will analyze case studies to understand the practical implications of these methodologies in control system design.
This module delves into the advanced Smith predictor controller, a critical component in control systems. Key topics include:
Students will engage in practical design exercises to reinforce their understanding of the Smith predictor's capabilities and applications.
This module emphasizes the design of controllers for the advanced Smith predictor, covering essential design strategies and methodologies. The content includes:
By engaging in hands-on simulations, students will learn to navigate the complexities of designing effective controllers for advanced systems.
This module focuses on model-free controller design techniques, emphasizing their relevance in modern control systems. Students will explore:
By the end of this module, students will have a solid understanding of how to implement and evaluate model-free controllers in various scenarios.
This module covers the fundamentals of model-based PID controller design. Students will learn:
By completing this module, students will gain practical skills for designing effective PID controllers tailored to specific applications.
This module delves into model-based PI-PD controller design, emphasizing its benefits and methodologies. Key topics include:
Students will develop a comprehensive understanding of how to apply PI-PD design principles in practice, enhancing their skills in control system engineering.
This module addresses the tuning of reconfigurable PID controllers, which are essential for adapting to varying system dynamics. Key points include:
Students will leave this module equipped with the knowledge and tools necessary to effectively tune PID controllers for diverse applications.