226 Processes & Systems: Labs and Review

226.1 Learning Objectives

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

Simulate PID Controllers: Build Python simulations to experiment with control systems
Tune P, I, D Gains: Understand how each parameter affects system response
Analyze Control Performance: Measure overshoot, settling time, and steady-state error
Apply to IoT Systems: Design temperature, motor, and position control for smart devices
Handle Disturbances: Design controllers robust to external environmental changes
Validate Control Designs: Use simulation to test before hardware implementation

226.2 Chapter Overview

This comprehensive lab and review section has been organized into three focused chapters for more effective learning:

Chapter Structure

This content covers practical labs, understanding checks, and decision guidance for IoT control systems. Each sub-chapter focuses on a specific learning goal:

Hands-on simulation and implementation
Scenario-based understanding checks
Decision frameworks and practical guidance

226.3 Sub-Chapters

226.3.1 1. PID Control Simulation Lab

Estimated Time: 1.5 hours | Difficulty: Intermediate to Advanced

Hands-on lab experience with PID control simulation:

Complete IoT process flow from sensors to actuators
Python PID simulator with visualization
Four progressive lab tasks (P-only → PI → PID → Tuning)
ESP32 Arduino implementation code
Performance metrics: rise time, settling time, overshoot

Key Deliverables: - Working PID simulation - Tuned controller parameters - Hardware-ready ESP32 code

226.3.2 2. Control Systems Understanding Checks

Estimated Time: 45 minutes | Difficulty: Advanced

Real-world scenario-based learning:

Process vs. System Architecture - $500K thermostat production decision
Open-Loop Economics - Agricultural irrigation ROI analysis
Derivative Control - High-inertia oven temperature control
Steady-State Error - Aquarium heater precision requirements
Distributed Feedback - Fish farm life-safety architecture

Key Insights: - When to use each PID term - Business case for closed-loop control - Local vs. cloud control trade-offs

226.3.3 3. Control Systems Decision Guidance

Estimated Time: 30 minutes | Difficulty: Intermediate

Practical decision frameworks:

Open-Loop vs. Closed-Loop Matrix - When to use each
PID Term Selection Guide - P, PI, PID, or PD?
Distributed Architecture Decision - Local vs. cloud vs. hybrid
Quick-Start Tuning - Real-world PID parameter selection
When NOT to Use PID - Alternative control strategies

Key Resources: - Decision matrices for common scenarios - Typical PID values for temperature control - Knowledge check questions - Further reading and references

226.4 Prerequisites

Required Chapters: - Processes and Systems - Process control basics - Sensor Fundamentals - Sensors - Actuators - Actuators

Technical Background: - Control loop concepts - PID controllers - System response characteristics

Control System Elements:

Element	Function	Example
Sensor	Measurement	Temperature probe
Controller	Decision	PID algorithm
Actuator	Action	Valve, motor
Process	System	HVAC, tank

Lab Requirements: - Arduino/ESP32 board - Temperature sensor - LED or motor for actuation

Total Estimated Time: 2.75 hours (all sub-chapters)

226.5 Learning Path

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flowchart LR
    A[Prerequisites<br/>Processes & Systems] --> B[PID Simulation Lab<br/>1.5 hours]
    B --> C[Understanding Checks<br/>45 min]
    C --> D[Decision Guidance<br/>30 min]
    D --> E[Next: Multi-Hop Networks]

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    style B fill:#16A085,stroke:#2C3E50,color:#fff
    style C fill:#E67E22,stroke:#2C3E50,color:#fff
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    style E fill:#7F8C8D,stroke:#2C3E50,color:#fff

Recommended Approach

For hands-on learners: Start with the PID Simulation Lab to build intuition through experimentation.

For conceptual learners: Begin with Understanding Checks to see how concepts apply to real scenarios.

For decision-makers: Jump to Decision Guidance for practical frameworks.

226.6 Quick Reference

226.6.1 Key Concepts Summary

Concept	Definition	IoT Application
Process	Algorithm transforming inputs to outputs	PID control logic
System	Physical + software components	ESP32 + sensor + actuator
Open-Loop	No feedback from output	Timer-based irrigation
Closed-Loop	Feedback enables self-correction	Temperature control
P-term	Proportional to current error	Fast response
I-term	Integral of accumulated error	Zero steady-state error
D-term	Derivative of error rate	Overshoot reduction

226.6.2 Decision Quick-Reference

Use Open-Loop when: - Environment is predictable - Accuracy is not critical (±20%) - Cost < $50/unit - No safety implications

Use Closed-Loop when: - Disturbances are expected - Precision required (±2%) - Life-safety critical - Value > $500 per failure

PID Term Selection: - P-only: LED dimming, non-critical positioning - PI: HVAC, industrial process control (most common) - PID: Ovens, motors, precision robotics - Skip D: High-noise environments

226.7 Cross-Hub Connections

Learning Resources

Interactive Tools: - Simulations Hub - PID controller simulators - Tool Discovery Hub - All interactive tools

Assessment: - Quizzes Hub - Control system quizzes - Knowledge Gaps Hub - Address misconceptions

Exploration: - Videos Hub - PID tuning demonstrations - Knowledge Map - Concept relationships

226.8 What’s Next?

Start with the first sub-chapter:

Begin PID Simulation Lab →

Or navigate directly to: - Understanding Checks - Decision Guidance

After completing all sub-chapters, continue to: - Multi Hop Ad Hoc Networks