222  PID: Open-Loop and Closed-Loop Systems

222.1 Learning Objectives

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

  • Distinguish Control Types: Compare open-loop and closed-loop control strategies and their applications
  • Identify System Characteristics: Recognize advantages and disadvantages of each control approach
  • Apply Decision Frameworks: Use systematic criteria to select appropriate control architecture
  • Design IoT Control Systems: Choose between local edge control and cloud-based control loops
TipMVU: Minimum Viable Understanding

Core concept: Open-loop systems execute predetermined actions blindly (like a timer), while closed-loop systems continuously monitor output and adjust (like a thermostat). Why it matters: Closed-loop systems can self-correct errors and adapt to disturbances, but require sensors and more complexity. Key takeaway: Most IoT control applications benefit from closed-loop feedback, but simple monitoring-only devices may operate open-loop at the device level while the overall system implements feedback through cloud coordination.

222.2 Prerequisites

Before diving into this chapter, you should be familiar with:

222.3 Closed-Loop Feedback Systems

Closed-loop feedback system block diagram
Figure 222.1: Closed-loop feedback system diagram showing the continuous cycle of sensing, comparing to setpoint, and adjusting output to maintain desired state

In a closed-loop system, a portion of the output is fed back to the input and either added to (positive feedback) or subtracted from (negative feedback) the input signal. This creates a self-regulating system that continuously updates based on current output conditions.

Graph diagram

Graph diagram
Figure 222.2: Block diagram of closed-loop feedback system showing setpoint input, comparator computing error signal (setpoint minus measured), controller processing error, process/plant producing output, and feedback sensor creating continuous regulation loop.

Closed-Loop Feedback System Block Diagram: Set point is compared with measured output, generating error signal. Controller processes error and adjusts system input. Feedback sensor creates continuous regulation loop.

Key Components:

  1. Set Point (SP): The desired target value
  2. Error Signal: Difference between set point and measured output
  3. Controller: Processes error and determines corrective action
  4. Process/Plant: The system being controlled
  5. Feedback Sensor: Measures actual output
  6. Comparator: Computes error = SP - measured value
Smart home heating system closed-loop example
Figure 222.3: Smart home heating system as a closed-loop feedback example, showing thermostat sensor, controller, heater actuator, and room temperature feedback 

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flowchart LR
    SP["Target:<br/>22 deg C"] --> Comp["Compare"]
    Sensor["Temperature<br/>Sensor:<br/>21 deg C"] --> Comp
    Comp -->|Error: +1 deg C<br/>Need Heat| MCU["Smart<br/>Thermostat<br/>Controller"]
    MCU -->|Activate| Heater["Heating<br/>System"]
    Heater -->|Warm Air| Room["Room<br/>Environment"]
    Room -.->|Measure<br/>Temperature| Sensor

    style SP fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style Comp fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style MCU fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Heater fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style Sensor fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Room fill:#7F8C8D,stroke:#16A085,stroke-width:2px,color:#fff

Figure 222.4: Smart Home Heating Control Loop with Temperature Feedback

Smart Home Heating Control Loop: Thermostat compares target (22C) with measured temperature (21C), calculates error (+1C), activates heater, and continuously monitors room temperature to maintain setpoint.

Operation:

  1. User sets desired temperature: 22C
  2. Temperature sensor measures actual temperature: 21C
  3. Error = 22C - 21C = +1C
  4. Controller activates heating system
  5. Room temperature rises toward 22C
  6. As error decreases, heating output adjusts
  7. System maintains temperature within tolerance

222.4 Open-Loop Control Systems

Open-loop control system block diagram
Figure 222.5: Open-loop control system diagram showing controller and process without feedback - system operates based on predetermined inputs without sensing output

An open-loop system does not monitor or measure its output. It executes a predetermined action based solely on the input, without feedback. This is also called a non-feedback system.

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graph LR
    Input["Input<br/>(Set Timer<br/>60 min)"] --> Controller["Controller<br/>(Timer)"]
    Controller -->|Run for<br/>60 minutes| Process["Process<br/>(Dryer)"]
    Process -->|Unknown State| Output["Output<br/>(Clothes)"]

    style Input fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style Controller fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Process fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style Output fill:#7F8C8D,stroke:#16A085,stroke-width:2px,color:#fff

Figure 222.6: Open-loop system block diagram with timer-based control without feedback

Open-Loop System Block Diagram: Input (timer setting) determines controller action without measuring output state. System executes predetermined sequence with no knowledge of actual results.

Characteristics:

  • No feedback path from output to input
  • Cannot self-correct for disturbances or errors
  • Simpler and less expensive to implement
  • Suitable when output is predictable and disturbances are minimal
Automatic clothes dryer open-loop example
Figure 222.7: Automatic clothes dryer as an open-loop system example - timer-based operation without moisture sensing feedback 

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flowchart LR
    User["User Sets<br/>Timer: 60 min"] --> Timer["Timer<br/>Controller"]
    Timer -->|Start Heating<br/>& Drum Rotation| Dryer["Dryer<br/>Process"]
    Dryer -->|Heat + Tumble<br/>for 60 min| Clothes["Clothes<br/>(Unknown<br/>Dryness)"]
    Timer -.->|60 min Elapsed| Stop["Stop<br/>Dryer"]

    style User fill:#7F8C8D,stroke:#2C3E50,stroke-width:2px,color:#fff
    style Timer fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Dryer fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style Clothes fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style Stop fill:#E74C3C,stroke:#2C3E50,stroke-width:2px,color:#fff

Figure 222.8: Open-Loop System: Clothes Dryer Timer-Based Operation Without Feedback

Clothes Dryer Open-Loop Operation: Timer runs for preset duration without measuring moisture content. Clothes may be over-dried (energy waste, fabric damage) or under-dried (ineffective) with no adaptive adjustment.

Operation:

  1. User sets timer for 60 minutes
  2. Dryer runs heating element and drum for 60 minutes
  3. Timer expires, dryer stops
  4. No measurement of whether clothes are actually dry

Problem: Clothes might be over-dried (wasted energy, fabric damage) or under-dried (ineffective), but the system has no way to know or adjust.

Modern IoT Enhancement: Adding a humidity sensor creates a closed-loop system that stops when clothes are dry, regardless of time elapsed.

222.5 Open-Loop in IoT Sensing Applications

Open-loop architectures are increasingly common in IoT data collection scenarios where:

  • Device only senses and transmits data
  • No local actuation required
  • Analysis and decision-making occur remotely
  • Feedback loop exists at system level, but not device level

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flowchart TB
    Sensor["Sensor Node<br/>(Open-Loop)"] -->|Periodic Data<br/>Transmission| Cloud["Cloud<br/>Platform"]
    Sensor -->|Temp: 24 deg C<br/>Humidity: 65%| Cloud
    Cloud -->|Store & Analyze| DB["Data<br/>Storage"]
    Cloud -->|Dashboard| User["Human<br/>Operator"]

    style Sensor fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Cloud fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style DB fill:#7F8C8D,stroke:#16A085,stroke-width:2px,color:#fff
    style User fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff

Figure 222.9: IoT sensor node open-loop data collection to cloud platform

IoT Sensor Node Open-Loop Data Collection: Device only senses and transmits data periodically without local actuation. No device-level feedback loop, but human operators or cloud systems may take action based on reported data.

However, at the system level, there may be feedback:

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graph TB
    subgraph Edge ["Edge Devices (Open-Loop Individually)"]
        S["Sensor Node:<br/>Measure Soil<br/>Moisture 25%"]
        A["Actuator Node:<br/>Irrigation Valve"]
    end

    subgraph Cloud ["Cloud System (Closed-Loop Coordination)"]
        P["IoT Platform"]
        R["Rule: IF moisture<br/>&lt; 30% THEN<br/>Activate Irrigation"]
        D["Database &<br/>Analytics"]
    end

    S -->|Transmit:<br/>Moisture = 25%| P
    P --> R
    R -->|Evaluate| D
    R -->|Command:<br/>Open Valve| A
    A -->|Water Soil| Field["Field"]
    Field -.->|Moisture Increases<br/>to 60%| S

    style S fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style A fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style P fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style R fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style Field fill:#7F8C8D,stroke:#16A085,stroke-width:2px,color:#fff

Figure 222.10: System-level closed-loop irrigation with cloud-based rule coordination

System-Level Closed-Loop with Device-Level Open-Loop: Individual sensor and actuator nodes operate open-loop (no local feedback), but cloud platform creates system-level feedback by coordinating remote sensing and actuation based on rules.

This architecture demonstrates that while individual devices operate open-loop, the overall IoT system implements closed-loop control through cloud-based coordination.

222.6 Comparing Open and Closed Loop Systems

Understanding the trade-offs between open-loop and closed-loop systems is crucial for IoT system design.

222.6.1 Advantages and Disadvantages

ImportantOpen-Loop Systems

Advantages:

  • Simple design and implementation
  • Lower cost (no feedback sensors needed)
  • Faster response (no feedback processing delay)
  • No stability issues or oscillations
  • Lower power consumption

Disadvantages:

  • Cannot self-correct errors - critical limitation
  • No knowledge of output condition
  • Sensitive to disturbances and variations
  • Cannot adapt to changing conditions
  • Accuracy depends entirely on calibration
  • Output may drift over time

Best Used When:

  • Output is highly predictable
  • Disturbances are minimal or absent
  • Cost is primary constraint
  • Simple data collection (sensing only)
  • Speed is critical and accuracy is not
ImportantClosed-Loop Systems

Advantages:

  • Automatic error correction - key benefit
  • Maintains desired output despite disturbances
  • Reduced sensitivity to component variations
  • Can use inexpensive, less accurate components
  • Adapts to changing conditions
  • Improved accuracy and stability

Disadvantages:

  • More complex design
  • Higher cost (feedback sensors and processing)
  • Potential stability problems if poorly designed
  • Can oscillate around set point
  • Higher power consumption
  • Slower response due to feedback processing

Best Used When:

  • Precision control required
  • Environment is unpredictable
  • Disturbances are likely
  • Safety is critical
  • Long-term stability needed
  • Self-regulation is valuable

222.6.2 Decision Matrix

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graph TD
    Start{{"Control System<br/>Design Decision"}} --> Q1{"Precision<br/>Control<br/>Required?"}
    Q1 -->|Yes| Q2{"Environment<br/>Predictable?"}
    Q1 -->|No| Q3{"Cost<br/>Constraint?"}

    Q2 -->|No| CL["Use<br/>Closed-Loop<br/>(Feedback)"]
    Q2 -->|Yes| Q4{"Disturbances<br/>Present?"}

    Q3 -->|High| OL["Use<br/>Open-Loop<br/>(No Feedback)"]
    Q3 -->|Low| Q2

    Q4 -->|Yes| CL
    Q4 -->|No| Q5{"Safety<br/>Critical?"}

    Q5 -->|Yes| CL
    Q5 -->|No| OL

    style Start fill:#16A085,stroke:#2C3E50,stroke-width:3px,color:#fff
    style CL fill:#2C3E50,stroke:#16A085,stroke-width:3px,color:#fff
    style OL fill:#E67E22,stroke:#16A085,stroke-width:3px,color:#fff

Figure 222.11: Decision tree for selecting open-loop vs closed-loop control architecture

Open-Loop vs Closed-Loop Decision Tree: Precision requirements, environmental predictability, disturbances, cost constraints, and safety considerations determine appropriate control architecture.

This variant presents a decision framework for architects choosing between control approaches based on system requirements.

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graph TB
    subgraph Decision["CONTROL ARCHITECTURE SELECTION"]
        direction TB
        Q1{{"Response Time<br/>Requirement?"}}
        Q2{{"Network<br/>Available?"}}
        Q3{{"Safety<br/>Critical?"}}
        Q4{{"Power<br/>Constrained?"}}
    end

    subgraph OpenLoop["OPEN-LOOP<br/>(No Feedback)"]
        OL1["βœ“ Lowest cost<br/>βœ“ Lowest power<br/>βœ“ Simplest design"]
        OL2["βœ— No self-correction<br/>βœ— Drift over time<br/>βœ— Cannot adapt"]
        OL3["USE FOR:<br/>β€’ Data collection only<br/>β€’ Simple timers<br/>β€’ Low-precision needs"]
    end

    subgraph EdgeClosed["EDGE CLOSED-LOOP<br/>(Local Feedback)"]
        EC1["βœ“ Fast response (ms)<br/>βœ“ Works offline<br/>βœ“ Autonomous safety"]
        EC2["βœ— Higher device cost<br/>βœ— Local processing load<br/>βœ— Limited analytics"]
        EC3["USE FOR:<br/>β€’ Motor control<br/>β€’ Safety systems<br/>β€’ Real-time PID"]
    end

    subgraph CloudClosed["CLOUD CLOSED-LOOP<br/>(Remote Feedback)"]
        CC1["βœ“ Centralized logic<br/>βœ“ Advanced analytics<br/>βœ“ Multi-device coordination"]
        CC2["βœ— Network latency<br/>βœ— Requires connectivity<br/>βœ— Failure vulnerability"]
        CC3["USE FOR:<br/>β€’ Building HVAC<br/>β€’ Fleet management<br/>β€’ Non-critical automation"]
    end

    Q1 -->|"< 100ms"| EdgeClosed
    Q1 -->|"> 1s OK"| Q2
    Q2 -->|"Unreliable"| EdgeClosed
    Q2 -->|"Reliable"| Q3
    Q3 -->|"Yes"| EdgeClosed
    Q3 -->|"No"| Q4
    Q4 -->|"Very Low"| OpenLoop
    Q4 -->|"Adequate"| CloudClosed

    style Decision fill:#2C3E50,color:#fff
    style OpenLoop fill:#7F8C8D,color:#fff
    style EdgeClosed fill:#16A085,color:#fff
    style CloudClosed fill:#E67E22,color:#fff

Figure 222.12: Alternative view: This decision matrix helps IoT architects select appropriate control architecture based on system constraints.

222.7 Practical Example: Water Quality Monitoring

TipExample: Water Quality Monitoring with Feedback

Consider an IoT water quality monitoring system for an aquarium:

Open-Loop Approach (Inadequate):

  • Oxygen sensor detects low O2 level
  • Data sent to cloud
  • No local action taken
  • Fish may die before human intervention

Closed-Loop Approach (Recommended):

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flowchart LR
    SP["Setpoint:<br/>O2 = 7 mg per L"] --> Comp["Compare"]
    Sensor["O2 Sensor:<br/>Measures<br/>6.2 mg per L"] --> Comp
    Comp -->|Error: +0.8 mg per L<br/>Need More O2| MCU["Microcontroller<br/>PID Controller"]
    MCU -->|Increase<br/>Pump Speed| Pump["Aerator<br/>Pump"]
    Pump -->|Add Oxygen| Tank["Aquarium<br/>Water"]
    Tank -.->|Feedback:<br/>Measure O2| Sensor
    Tank -->|Also Send Data| Cloud["Cloud<br/>Monitoring"]

    style SP fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style Comp fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style MCU fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Pump fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style Sensor fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style Tank fill:#7F8C8D,stroke:#16A085,stroke-width:2px,color:#fff

Figure 222.13: Aquarium dissolved oxygen closed-loop control with aerator pump adjustment

Aquarium O2 Control Closed-Loop System: Local microcontroller continuously monitors dissolved oxygen, compares to setpoint (7 mg/L), and adjusts aerator pump speed to maintain safe levels.

Operation:

  1. Set point: O2 = 7 mg/L
  2. Sensor measures: O2 = 6.2 mg/L
  3. Error: +0.8 mg/L (need more oxygen)
  4. Controller increases aerator pump speed
  5. O2 level rises toward target
  6. Controller reduces pump speed as error decreases
  7. System maintains stable O2 level
  8. Cloud also receives data for long-term monitoring

Error Signal Calculation:

Error = Set Point - Measured Value
Error = 7.0 mg/L - 6.2 mg/L = +0.8 mg/L

Positive error β†’ Increase aerator output
Negative error β†’ Decrease aerator output
Zero error β†’ Maintain current output

222.8 Design Considerations: Edge vs Cloud Control

WarningTradeoff: Local Edge Control vs Cloud-Based Control Loop

Option A: Local Edge Control - PID controller runs on edge device (microcontroller, gateway) with sensor and actuator. Control loop latency 1-10ms, operates independently of network.

Option B: Cloud-Based Control - Sensor data sent to cloud, PID algorithm runs in cloud, commands sent back to actuator. Enables advanced analytics but adds 100-500ms network latency.

Decision Factors:

  • Choose Local Edge when: Control loop requires <50ms response time (motor speed, safety shutoffs), network connectivity is unreliable or intermittent, bandwidth costs are significant (cellular IoT), or system must operate autonomously during outages.

  • Choose Cloud-Based when: Control decisions benefit from cross-device coordination (building HVAC optimizing across 100 zones), advanced ML models improve control quality, historical data analysis drives setpoint adjustments, or remote monitoring and tuning are required.

  • Latency comparison: Local edge achieves 1-10ms control loop. Cloud-based adds 50-200ms (Wi-Fi to internet) or 100-500ms (cellular) round-trip, making it unsuitable for systems with >10Hz disturbance frequencies.

Hybrid approach: Run fast local PID for stability, use cloud for setpoint optimization and supervisory control.

222.9 Knowledge Check

Question: Comparing open-loop versus closed-loop irrigation control over 30 days in summer. Open-loop waters 10 minutes daily. Closed-loop uses soil moisture sensor (waters when < 30%, stops at 70%). Which statement is most accurate?

Explanation: Open-loop waste: Waters daily regardless of conditions - after rain, over-waters; during heat, under-waters. Closed-loop efficiency: Rainy days - no watering needed. Hot days - waters more. Normal days - optimized amount. Water savings: 40-60% typical in real deployments with closed-loop feedback. ROI: Sensor cost ($50) recovered in water savings within one season.

222.10 Summary

This chapter compared open-loop and closed-loop control systems:

  • Closed-Loop Systems: Continuously monitor output and self-correct errors using feedback sensors
  • Open-Loop Systems: Execute predetermined actions without measuring results
  • Decision Factors: Precision requirements, environmental predictability, disturbances, cost, and safety
  • IoT Architecture: Device-level open-loop with system-level closed-loop is common in distributed IoT
  • Edge vs Cloud: Local edge control for fast response, cloud control for analytics and coordination

222.11 What’s Next

The next chapter explores PID Control Theory, covering the mathematics and behavior of Proportional, Integral, and Derivative control terms.