Introduction

Dynamic systems and feedback control are fundamental concepts in mechanical engineering, systems engineering, and automation. Understanding the behavior of dynamic systems and how to design feedback control systems is essential for ensuring the stability, performance, and efficiency of mechanical systems. This 5-day training course on Dynamic Systems and Feedback Control provides a comprehensive understanding of system modeling, dynamic analysis, and advanced control strategies. The course will emphasize practical techniques for modeling, analyzing, and controlling mechanical systems, with a focus on real-world applications such as robotics, automotive systems, and process control.

Objectives

By the end of this course, participants will be able to:

1. Understand the fundamental principles of dynamic systems and how they are modeled and analyzed.
2. Analyze linear and nonlinear dynamic systems using state-space models, transfer functions, and differential equations.
3. Understand the core concepts of feedback control and the various types of controllers such as PID, LQR, and state feedback.
4. Apply stability analysis methods (e.g., Lyapunov, Routh-Hurwitz, and Bode plots) to evaluate the stability of dynamic systems.
5. Design and implement feedback control systems for regulating dynamic behaviors, including position, velocity, and force control.
6. Use advanced tools and techniques such as state-space representation, pole placement, and observability/controllability to optimize control systems.
7. Gain hands-on experience with control design, simulation, and analysis using industry-standard tools such as MATLAB, Simulink, and LabVIEW.
8. Troubleshoot and optimize dynamic control systems for real-world engineering applications in industries like automation, robotics, and aerospace.

Who Should Attend?

This course is designed for:

• Mechanical Engineers, Control Engineers, and Systems Engineers working with dynamic systems and control systems.
• Automation Engineers and Mechatronics Engineers interested in improving their knowledge of feedback control and system dynamics.
• R&D Engineers involved in designing and analyzing complex systems requiring dynamic modeling and control.
• Graduate students pursuing a career in control systems, robotics, and automation.
• Project Managers and System Integrators who are responsible for implementing control systems in real-world applications.
• Maintenance Engineers focused on the upkeep and optimization of dynamic systems in industrial settings.

Course Outline

Day 1: Introduction to Dynamic Systems and System Modeling

• Morning Session:

  1. Overview of Dynamic Systems: Definition, Characteristics, and Applications in Mechanical and Control Engineering
  2. Introduction to System Representation: Differential Equations and System Equations of Motion
  3. Linear vs. Nonlinear Systems: Behavior, Modeling, and Characteristics
  4. State-Space Representation: State Variables, Matrices, and State Equations

• Afternoon Session:

  1. Transfer Functions: Definition, Derivation, and Application in System Analysis
  2. System Behavior: Step Response, Impulse Response, and Frequency Response
  3. Stability Analysis: Introduction to Stability Criteria (BIBO, Lyapunov Stability)
  4. Hands-On Exercise: Model a Simple Mechanical System (e.g., Mass-Spring-Damper) Using State-Space and Transfer Function Formulas in MATLAB/Simulink

Day 2: Analysis of Dynamic Systems

• Morning Session:

  1. Time Domain Analysis: Step Response, Impulse Response, and Natural Frequency
  2. Laplace Transform: Application to Dynamic Systems and Solving Differential Equations
  3. Routh-Hurwitz Criterion: Stability Analysis for Higher-Order Systems
  4. Pole-Zero Analysis: Location of Poles and Zeros and Their Effect on System Dynamics

• Afternoon Session:

  1. Frequency Domain Analysis: Bode Plots, Nyquist Plots, and Root Locus
  2. System Stability: Understanding Gain Margin, Phase Margin, and Stability Regions
  3. Nonlinear Dynamics: Introduction to Nonlinear Behavior in Systems (e.g., Saturation, Hysteresis)
  4. Hands-On Exercise: Use MATLAB/Simulink to Generate Bode Plots and Root Locus Diagrams for a Dynamic System

Day 3: Feedback Control Systems Fundamentals

• Morning Session:

  1. Introduction to Feedback Control: Definition, Importance, and Types (Negative and Positive Feedback)
  2. PID Control: Proportional, Integral, and Derivative Control—Principles and Applications
  3. System Response with Feedback: How Feedback Affects Stability, Performance, and Transient Response
  4. Error Analysis: Steady-State Error, Transient Error, and Error Constants (Position, Velocity, Acceleration)

• Afternoon Session:

  1. Root Locus Technique: Design of Feedback Control Using Root Locus for Stability and Performance
  2. Pole Placement: Controlling System Dynamics by Placing Poles in the Left Half-Plane
  3. State Feedback Control: Full-State Feedback and Pole Placement Method
  4. Hands-On Exercise: Design a Basic PID Controller and Implement it Using MATLAB/Simulink

Day 4: Advanced Control Techniques and Design

• Morning Session:

  1. Linear Quadratic Regulator (LQR): Optimal Control, Cost Function, and State Feedback Control Design
  2. State Estimation: Kalman Filters and Observers for Estimating System States from Output Measurements
  3. Observer-Based Control: Full-State Observers and Luenberger Observer Design
  4. Nonlinear Control Techniques: Introduction to Sliding Mode Control, Backstepping Control, and Feedback Linearization

• Afternoon Session:

  1. Robust Control: Handling Uncertainties in System Models with H-infinity Control
  2. Adaptive Control: Implementing Controllers that Adjust to Changes in System Dynamics
  3. Sampled-Data Systems: Discrete-Time Control and Z-Transforms for Digital Systems
  4. Hands-On Exercise: Design an LQR Controller for a Simple Multi-Variable System Using MATLAB/Simulink

Day 5: Implementation, Optimization, and Future Trends in Feedback Control

• Morning Session:

  1. Real-Time Implementation of Feedback Control Systems: Considerations for Hardware and Software Integration
  2. Optimization of Control Parameters: Tuning PID Controllers and LQR for Optimal Performance
  3. Industrial Control Applications: Robotics, Automotive Control, Process Control, and Aerospace Systems
  4. Mechatronics and Control: Integrating Feedback Control with Sensors, Actuators, and Embedded Systems

• Afternoon Session:

  1. Control System Design for Nonlinear Systems: Practical Techniques for Nonlinear Control in Real-World Applications
  2. Smart Systems and AI in Control: The Role of Machine Learning and Artificial Intelligence in Advanced Control Systems
  3. Hands-On Exercise: Implement a Full-Scale Feedback Control System (e.g., Motor Control or Autonomous Robot) in Simulink
  4. Wrap-Up and Certification: Final Discussion, Course Summary, and Distribution of Certificates

Certification

Upon successful completion of the course, participants will receive a Certificate of Completion in Dynamic Systems and Feedback Control. This certification acknowledges the participant’s expertise in modeling, analyzing, and designing feedback control systems for dynamic mechanical systems.