220  Process Control and PID

220.1 Learning Objectives

By the end of this chapter series, you will be able to:

  • Explain Feedback Concepts: Describe how feedback loops work in electronic systems and IoT applications
  • Implement PID Controllers: Design and tune Proportional-Integral-Derivative controllers for IoT actuator systems
  • Distinguish Control Types: Compare open-loop and closed-loop control strategies and their applications
  • Analyze System Response: Evaluate system behavior including overshoot, settling time, and steady-state error
  • Apply Control Theory: Implement temperature, motor, and position control systems using microcontrollers
  • Tune PID Parameters: Use manual and automatic tuning methods to optimize controller performance
TipMVU: Minimum Viable Understanding

Core concept: PID control uses three strategies working together - Proportional responds to current error, Integral eliminates persistent offset, and Derivative prevents overshoot - to smoothly maintain a setpoint instead of oscillating on/off. Why it matters: Simple on/off control causes temperature swings, wastes energy, and wears out actuators; PID enables the precise, stable control required for greenhouses, manufacturing, and smart homes. Key takeaway: Start tuning with P only (increase until oscillation), add I to eliminate steady-state error, then add D to reduce overshoot - not every application needs full PID; PI often suffices.

220.2 Chapter Overview

This topic has been split into four focused chapters for better learning progression:

220.2.1 1. Feedback Fundamentals

Difficulty: Foundational | Time: ~15 minutes

Learn the basic concepts of feedback control that form the foundation for all control systems:

  • What feedback is and why it matters
  • Everyday feedback examples (thermostats, cruise control)
  • Positive vs negative feedback
  • Feedback in IoT applications
  • Distributed feedback across network boundaries

220.2.2 2. Open-Loop and Closed-Loop Systems

Difficulty: Intermediate | Time: ~20 minutes

Compare and contrast control architectures:

  • Closed-loop systems with continuous feedback
  • Open-loop systems without feedback
  • Advantages and disadvantages of each approach
  • Decision framework for selecting control architecture
  • Edge vs cloud control placement

220.2.3 3. PID Control Theory

Difficulty: Advanced | Time: ~25 minutes

Deep dive into PID controller mathematics and behavior:

  • The PID equation and its components
  • Proportional (P) control and steady-state error
  • Integral (I) control and error accumulation
  • Derivative (D) control and overshoot prevention
  • Integral windup and anti-windup techniques
  • Comparing P, PI, PD, and full PID configurations

220.2.4 4. PID Tuning and Applications

Difficulty: Advanced | Time: ~20 minutes

Practical implementation and real-world examples:

  • Systematic tuning approaches
  • Real-world industrial brewery case study
  • Domain-specific tuning (HVAC, motors, irrigation, vehicles)
  • Performance metrics and ROI analysis
  • Design considerations for IoT systems

220.3 Prerequisites

Before starting this chapter series, you should be familiar with:

This chapter connects to multiple learning resources:

Interactive Tools:

  • Simulations Hub - Try the PID tuning simulator to experiment with Kp, Ki, and Kd values
  • Power Budget Calculator in Energy Management - Calculate control system power consumption

Visual Learning:

Assessment:

  • Quizzes Hub - Test your understanding of feedback loops and PID tuning

Common Challenges:

  • Knowledge Gaps Hub - Address misconceptions about steady-state error and integral windup

Knowledge Structure:

  • Knowledge Map - See how PID control connects to sensors, actuators, and IoT architectures

220.4 Quick Reference: PID Term Summary

Term Reacts To Purpose Issue to Watch
P Current error magnitude Provide proportional response Steady-state error, overshoot
I Accumulated error over time Eliminate steady-state error Integral windup, slow response
D Rate of error change Dampen overshoot, predict future Noise amplification, hard to tune

Simplified Summary:

  • P corrects present error
  • I corrects past accumulated error
  • D corrects future predicted error

220.5 What’s Next

Start with Feedback Fundamentals to build a solid foundation, then progress through the chapters in order. Each chapter builds on concepts from the previous ones.

For hands-on practice, visit the Simulations Hub to experiment with the interactive PID tuning simulator.