This module focuses on identifying simple systems using state space methods. Students will learn various approaches to simplify identification processes, gaining fundamental skills applicable to more complex systems.
This module serves as an introduction to the course, outlining its objectives and significance in the field of control systems. Students will learn about the importance of advanced control systems and their applications across various industries.
This module discusses various control structures and their corresponding performance measures. Students will explore the different types of controllers, including feedback and feedforward strategies, and learn how to assess their effectiveness through performance metrics.
This module focuses on time and frequency domain performance measures of control systems. Students will learn to evaluate system responses using different time-domain specifications and frequency response techniques, enhancing their ability to design effective control strategies.
This module delves into the design of controllers, providing students with a comprehensive understanding of design principles and methodologies. Emphasis will be placed on practical aspects of controller design to ensure successful implementation in real-world applications.
This module addresses the specific challenges associated with designing controllers for Single Input Single Output (SISO) systems. Students will learn various design techniques tailored for SISO applications, preparing them for practical scenarios in control engineering.
This module covers the design of controllers for Two Input Two Output (TITO) processes. Students will explore complex interaction effects and strategies to manage system dynamics, ensuring robust control for multi-input multi-output scenarios.
In this module, students will identify and discuss the limitations of PID controllers. Understanding these limitations is crucial for developing improved control strategies and overcoming common challenges encountered in control system applications.
This module introduces PI-PD controllers specifically for SISO systems. Students will learn about the benefits and applications of PI-PD control strategies, emphasizing their effectiveness in enhancing system performance.
This module discusses the implementation of PID-P controllers for Two Input Two Output systems. Students will explore how to effectively manage multiple inputs and outputs to optimize control in complex systems.
This module examines the effects of measurement noise and load disturbances on control system performance. Understanding these factors is essential for designing robust controllers and ensuring system reliability under varying conditions.
This module focuses on the identification of dynamic models of plants. Students will learn various techniques for accurately identifying plant dynamics, which is crucial for effective controller design and system analysis.
This module presents relay control systems as a method for plant identification. Students will explore the use of relay feedback to derive important system parameters and improve identification accuracy.
This module covers off-line identification of process dynamics. Students will learn about various techniques and methodologies for gathering data and developing accurate dynamic models in a controlled environment.
This module focuses on on-line identification of plant dynamics, teaching students how to gather real-time data to update dynamic models dynamically. Practical applications of this knowledge will be emphasized throughout the module.
This module delves into state space-based identification methods. Students will discover how to utilize state space models for identifying system dynamics, enhancing their ability to analyze and control complex systems effectively.
This module emphasizes state space analysis of systems. Students will learn how to analyze control systems using state space representations, providing them with critical skills for modeling and designing advanced control systems.
This module continues exploring state space-based identification of systems. Students will work through advanced techniques for identifying systems in state space, further deepening their understanding of this critical area in control engineering.
This module provides further insights into state space-based identification of systems, offering students practical exercises and case studies to solidify their knowledge and application of these advanced methods.
This module focuses on identifying simple systems using state space methods. Students will learn various approaches to simplify identification processes, gaining fundamental skills applicable to more complex systems.
This module discusses identifying First Order Plus Dead Time (FOPDT) models. Students will explore techniques and approaches for accurately characterizing these common dynamic systems to enhance their control strategies.
This module tackles the identification of second order plus dead time models. Students will learn the intricacies involved in identifying these models and the implications for control system design and performance.
This module focuses on the identification of Second Order Plus Dead Time (SOPDT) models. Students will explore methods for estimating parameters and understanding the dynamics involved in these systems.
This module discusses steady state gain calculations derived from asymmetrical relay tests. Students will learn how to analyze data from these tests to determine system gains accurately.
This module covers the identification of SOPDT models with pole multiplicity. Students will learn about unique challenges and methods for accurately capturing the dynamics of such complex systems.
This module examines the existence of limit cycles in unstable systems. Students will explore the conditions under which these phenomena occur and their implications for control system design.
This module provides an overview of identification procedures. Students will familiarize themselves with various approaches used to identify system parameters and dynamics, leading to effective control strategies.
This module focuses on identifying underdamped systems, teaching students critical techniques for analyzing and controlling these systems effectively. Real-world applications will be emphasized throughout the learning process.
This module covers off-line identification techniques for TITO systems. Students will learn methodologies for accurately identifying the dynamics of multi-input multi-output systems in a controlled setting.
This module focuses on on-line identification of TITO systems, teaching students how to gather real-time data and update dynamic models. The emphasis on practical applications will enhance their learning experience.
This module reviews time-domain based identification methods, emphasizing how to utilize these techniques for effective system identification. Students will gain practical insights into their application and effectiveness.
This module introduces discrete Fourier-based analytical expressions for on-line identification. Students will learn how to apply Fourier techniques for dynamic model identification, enhancing their analytical capabilities.
This module discusses model parameter accuracy and sensitivity. Students will learn how to assess model parameters' reliability and their impact on control system performance, reinforcing the importance of precise modeling.
This module explores improved identification techniques using Fourier series and wavelet transforms. Students will learn how these advanced methods can enhance model parameter estimation and dynamic system identification accuracy.
This module reviews discrete Fourier-based identification methods, providing an overview of techniques and their applications in control systems. Students will gain insights into the benefits and challenges of these methods.
This module introduces the advanced Smith predictor controller, focusing on its design and application. Students will explore how this controller can enhance system performance in the presence of delays.
This module covers the design of controllers specifically for the advanced Smith predictor. Students will learn methodologies to optimize the design process, ensuring these controllers are effective in practical scenarios.
This module explores model-free controller design methodologies. Students will gain insights into how to create controllers without relying on detailed system models, focusing on adaptability and performance.
This module delves into model-based PID controller design, offering students advanced techniques for creating effective PID controllers suited for various control applications in engineering.
This module provides advanced methodologies for model-based PI-PD controller design. Students will learn how to effectively implement these controllers in various systems for enhanced performance.
This module focuses on tuning reconfigurable PID controllers. Students will explore techniques for adjusting controller parameters dynamically to optimize performance in changing environments.