Introduction

Proportional-Integral-Derivative (PID) control is the foundation of modern automation, robotics, industrial control, automotive systems, and aerospace engineering. As one of the most widely used control strategies, PID controllers are essential for stability, accuracy, and efficiency in real-world applications.

This course provides a comprehensive understanding of PID control theory, covering tuning methods, performance optimization, advanced modifications (adaptive, robust, and model-based PID control), and real-world applications. Participants will gain hands-on experience in MATLAB, Simulink, Python, and embedded platforms to design and implement high-performance PID controllers.

Objectives

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

1. Understand the fundamentals of PID control and its variations.
2. Design and tune PID controllers using Ziegler-Nichols, Cohen-Coon, and modern optimization techniques.
3. Implement PID controllers for mechanical, electrical, and process control systems.
4. Optimize PID performance for overshoot reduction, disturbance rejection, and robustness.
5. Apply feedforward, adaptive, and model-based PID control for advanced applications.
6. Implement PID controllers on microcontrollers, PLCs, and real-time hardware.
7. Explore AI-enhanced PID control and future trends in industrial automation.

Who Should Attend?

This course is ideal for:

• Control System Engineers working in automation and industrial control.
• Robotics & Mechatronics Engineers designing precise motion control systems.
• Automotive Engineers working on cruise control, ABS, and engine control systems.
• Process Control Engineers optimizing chemical, oil & gas, and manufacturing systems.
• Electrical Engineers working with power electronics, motor drives, and HVAC systems.
• Graduate Students & Researchers studying advanced control strategies.

Course Outline

Day 1: Fundamentals of PID Control

Session 1: Introduction to Control Systems & PID Control

• Basics of closed-loop and open-loop control systems.
• Introduction to Proportional (P), Integral (I), and Derivative (D) control.
• Mathematical representation and transfer functions of PID controllers.

Session 2: Understanding PID Tuning Parameters

• Effects of P, I, and D gains on system response.
• Stability, transient response, and steady-state error analysis.
• Performance metrics: Overshoot, settling time, and rise time.

Session 3: Manual Tuning and Trial-and-Error Methods

• Best practices for manual tuning of PID controllers.
• Case study: PID tuning for temperature control systems.
• Introduction to simulation tools: MATLAB, Simulink, and Python.

Hands-On Workshop: Implementing basic PID control in MATLAB and Simulink.

Day 2: PID Tuning Techniques and Optimization

Session 1: Classical PID Tuning Methods

• Ziegler-Nichols and Cohen-Coon tuning techniques.
• PID tuning for underdamped and overdamped systems.
• Case study: PID tuning for DC motor speed control.

Session 2: Modern PID Tuning Approaches

• Auto-tuning methods and adaptive PID tuning.
• Optimization-based tuning using genetic algorithms (GA) and particle swarm optimization (PSO).
• Case study: PID tuning for robotic arm position control.

Session 3: Robust PID Control & Anti-Windup Mechanisms

• Windup effects in integral action and anti-windup techniques.
• Robust PID control for varying system dynamics.
• Case study: PID control for UAV altitude stabilization.

Hands-On Workshop: Auto-tuning a PID controller in MATLAB/Simulink and Python.

Day 3: Advanced PID Control Strategies

Session 1: Feedforward and Cascade PID Control

• Feedforward compensation to enhance disturbance rejection.
• Cascade control systems for improved response in multi-stage processes.
• Application: PID control in HVAC and industrial automation.

Session 2: Adaptive and Model-Based PID Control

• Self-tuning adaptive PID controllers.
• Model-based PID control using state-space methods.
• Case study: PID control for a quadcopter drone system.

Session 3: Digital PID Implementation & Discretization

• Converting continuous-time PID to discrete-time PID.
• Digital filtering and noise reduction techniques.
• Implementation on embedded systems and PLCs.

Hands-On Workshop: Implementing adaptive PID control in Python and Simulink.

Day 4: Real-World Applications of PID Control

Session 1: PID Control in Industrial Automation

• PID controllers in chemical processing, oil & gas, and manufacturing.
• Tuning PID for pressure, level, and flow control.
• Application: PID control in boiler temperature regulation.

Session 2: PID in Robotics and Motion Control

• PID control for robotic joint position and velocity tracking.
• Inverse kinematics and trajectory optimization.
• Case study: PID control in robotic manipulator arms.

Session 3: PID in Power Systems and Automotive Applications

• PID control in motor drives, HVAC, and renewable energy systems.
• PID in automotive applications: Cruise control, ABS, and engine tuning.
• Case study: PID for solar panel maximum power point tracking (MPPT).

Hands-On Workshop: Implementing PID control for motor speed control in Simulink.

Day 5: Future Trends in PID Control & AI Integration

Session 1: AI-Enhanced PID Controllers

• Machine learning and AI-based PID tuning.
• AI-driven adaptive and self-learning PID control.
• Application: PID controllers in self-driving cars.

Session 2: Real-Time PID Control in Embedded Systems

• Implementing PID controllers on Arduino, Raspberry Pi, and ARM Cortex-M.
• Real-time operating systems (RTOS) for PID control.
• Case study: PID-based autopilot system for drones.

Session 3: Future of Control Engineering

• The role of digital twins and Industry 4.0 in control systems.
• Integration of PID with advanced control methods (MPC, neural networks).
• Research trends in autonomous and intelligent PID control systems.

Final Project Presentation: Participants develop and present a PID-controlled system for an industrial or robotic application.

Final Assessment & Certification

• Knowledge Check: Final assessment covering PID control concepts.
• Project Presentation: Participants present their PID system design and implementation.
• Certification: Certificate of completion awarded upon successful participation.