231  Processes and Systems: PID Control

231.1 Overview

This section covers PID (Proportional-Integral-Derivative) control, the most widely used feedback control algorithm in industrial and IoT applications. PID controllers enable precise, automated control of processes ranging from temperature regulation to motor speed control.

Block diagram of PID controller showing error signal splitting into three parallel paths: Proportional term (Kp times error for immediate response), Integral term (Ki times accumulated error for steady-state correction), and Derivative term (Kd times error rate for overshoot prevention), all summing to produce final control output
Figure 231.1: PID control system block diagram showing the three parallel control actions working together

231.2 What You’ll Learn

The PID control content is organized into three focused chapters:

231.2.1 Part 1: PID Control Fundamentals

Learn the foundation of PID control:

  • PID Components: Understanding the three terms (P, I, D) and their roles
  • Error Calculation: How setpoint, process variable, and error relate
  • Proportional Control: How P-term responds to current error magnitude
  • P-Only Limitations: Why proportional control alone causes steady-state error
  • PID Equation: The mathematical foundation for controller output

231.2.2 Part 2: Integral and Derivative Control

Master the I and D terms:

  • Integral Control: Eliminating steady-state error through error accumulation
  • Integral Windup: Preventing dangerous over-accumulation
  • Derivative Control: Using rate of change to reduce overshoot
  • PID Configuration Selection: When to use P, PI, PID, or PD
  • Tuning Philosophy: Why conservative gains outperform aggressive tuning

231.2.3 Part 3: PID Implementation and Labs

Hands-on implementation:

  • Arduino/ESP32 Code: Complete PID controller class in C++
  • Lab Exercises: Systematic tuning exploration with P, PI, and PID
  • Python Framework: Production-ready simulation and auto-tuning
  • Performance Metrics: Measuring settling time, overshoot, and error
  • Distributed Control: Edge vs cloud control architecture decisions

231.3 Prerequisites

Before starting this section, you should understand:

  • Basic feedback control concepts (open-loop vs closed-loop)
  • Process variables, setpoints, and error signals
  • Simple programming concepts (variables, loops, functions)

231.4 Learning Path

Table 231.1: PID Control Learning Path
Chapter Focus Difficulty Time
PID Fundamentals Theory & P-term Intermediate 25 min
Integral & Derivative I & D terms Intermediate 30 min
Implementation Code & Labs Advanced 45 min

231.5 Quick Reference

PID Equation: \[ u(t) = K_p \cdot e(t) + K_i \cdot \int_{0}^{t} e(\tau) \, d\tau + K_d \cdot \frac{de(t)}{dt} \]

Common Configurations:

Mode When to Use
P Simple systems, some error acceptable
PI Most common, general-purpose control
PID High-performance, minimal overshoot needed
PD Fast servos without steady-state concerns

231.6 Start Learning

Recommended: Begin with PID Control Fundamentals for a solid foundation.

Advanced: Jump directly to Implementation and Labs if you already understand PID theory.