Describe control technologies (smart homes, traffic lights, autonomous vehicles)

3 Monitoring and Control

Objective

Describe the control technologies used in:

• Smart homes
• Traffic‑light systems
• Autonomous vehicles

1. Introduction to Control Systems

A control system continuously monitors a process, compares the measured output with a desired reference, and applies a corrective action. The basic feedback loop is expressed as:

$$e(t)=r(t)-y(t)$$

• e(t) – error signal (difference between reference and output)
• r(t) – reference (desired) value
• y(t) – measured output

2. Sensor Families – Characteristics, Calibration & Pros/Cons

Calibration Checklist (All Sensor Families)

3. Actuator Families – Types, Movements & Case‑Study Mapping

4. Smart‑Home Control Technologies

• Sensors: temperature, humidity, light/UV, motion (PIR), door/window contacts, CO₂/gas, sound, IR proximity.
• Actuators: smart thermostat (rotary valve), motorised blinds (linear), LED drivers (electrical), electronic door locks (solenoid), smart plugs (relay).
• Communication protocols: Wi‑Fi, Zigbee, Z‑Wave, Thread, Bluetooth Low Energy (BLE).
• Control platforms: cloud‑based assistants (Amazon Alexa, Google Assistant), local hubs (Home Assistant, Samsung SmartThings).
• Control logic:
  • Rule‑based schedules (e.g., lights on at sunset).
  • Occupancy‑based optimisation using motion sensors.
  • AI‑driven energy‑saving algorithms that learn daily habits.
• Safety & security features: fire‑alarm integration, door‑contact alerts, remote lock control, tamper detection.

Performance & Latency Box – Smart Home

Typical latency requirement: seconds to minutes (e.g., thermostat response < 5 s, lighting < 200 ms).
Bandwidth: < 1 Mbps per node (most devices send small status packets).
Reliability target: 99.9 % uptime for security‑related devices.

Sample Pseudocode – Smart‑Home Heating Control

IF (Time = 06:00) THEN
    SetTargetTemp(21°C)
END IF

READ currentTemp FROM TempSensor
error = targetTemp - currentTemp

IF (error > 0.5) THEN
    ACTIVATE heatingValve (open proportionally to error)   // PWM to rotary actuator
ELSE IF (error < -0.5) THEN
    DEACTIVATE heatingValve
END IF

// Feedback loop repeats every 5 seconds

5. Traffic‑Light Control Systems

• Detection methods: inductive‑loop detectors, video image processing, radar, infrared, acoustic sensors.
• Actuators / Outputs: electromechanical signal heads (red, amber, green), pedestrian crossing displays, audible beepers.
• Control logic levels:
  1. Fixed‑time control – pre‑programmed cycle lengths.
  2. Actuated control – phase extensions based on real‑time detector data.
  3. Adaptive control – network‑wide optimisation (e.g., SCOOT, SCATS, InSync).
• Communication: Dedicated Short‑Range Communications (DSRC), cellular IoT (4G/5G), fiber‑optic links to central traffic‑management centres.
• Safety extensions: emergency‑vehicle pre‑emption, pedestrian push‑buttons, flashing/amber‑caution modes.

Performance & Latency Box – Traffic Lights

Latency requirement: ≤ 1 s for detector‑to‑signal change; ≤ 3 s for adaptive network re‑optimisation.
Bandwidth: 10‑100 kbps per intersection (mostly status & control messages).
Reliability target: 99.99 % (failure leads to fail‑safe flashing mode).

Sample Flowchart – Actuated Traffic‑Light Cycle

6. Autonomous Vehicle Control Technologies

• Sensing suite: LiDAR, radar, stereo/mono cameras, ultrasonic sensors, GNSS (GPS/GLONASS) + Inertial Navigation System (INS), infrared night‑vision.
• Perception algorithms: sensor fusion, object detection, lane‑keeping detection, semantic segmentation, localisation (SLAM).
• Decision‑making layers:
  1. Strategic layer – route planning using digital maps.
  2. Tactical layer – manoeuvre selection (lane change, overtaking, intersection handling).
  3. Operational layer – low‑level control of steering, throttle, braking, signalling (typically via CAN‑bus).
• Control logic: hybrid of rule‑based traffic‑law compliance and machine‑learning models for prediction and risk assessment.
• Actuators: electric power‑steering motor (rotary), drive‑by‑wire throttle (linear/electric), electronic braking system (hydraulic‑electric), indicator lights (electrical).
• Communication: Vehicle‑to‑Vehicle (V2V), Vehicle‑to‑Infrastructure (V2I), Vehicle‑to‑Network (V2N) via 5G/Cellular‑V2X, plus internal CAN/LIN buses.
• Safety‑critical aspects: redundancy, fail‑safe states, real‑time latency constraints (≤ 50 ms for emergency braking, ≤ 100 ms for steering decisions).

Performance & Latency Box – Autonomous Vehicles

Latency requirement: ≤ 50 ms for braking, ≤ 100 ms for steering, ≤ 200 ms for lane‑keep assistance.
Bandwidth: 10‑100 Mbps per vehicle (high‑definition camera streams, LiDAR point clouds).
Reliability target: 99.999 % for safety‑critical functions (ISO 26262 ASIL D).

Sample Pseudocode – Operational Layer (Emergency Braking)

loop every 20 ms
    sensorData = fuse(LiDAR, radar, camera)
    obstacles = detectObjects(sensorData)
    IF (obstacle.distance < 2 m AND obstacle.relativeSpeed > 5 m/s) THEN
        brakeCommand = computeBrakingForce(obstacle)
        SEND brakeCommand TO electronicBrakeActuator   // CAN‑bus message
    END IF
end loop

7. Comparative Overview

8. Summary

Control technologies follow the hierarchy sensors → processing → actuators → feedback. Mastery of the full sensor families, their calibration, and the mapping of actuator families to real‑world examples enables students to design reliable monitoring and control solutions for personal (smart home), public‑infrastructure (traffic lights) and advanced autonomous (vehicles) domains.

9. Suggested Classroom Activities

1. Create a detailed flowchart for a smart‑home heating scenario (temperature sensor → PID controller → motorised valve → room‑temperature feedback).
2. Visit a local road intersection, identify the detector types present, and design an adaptive‑control algorithm that could raise vehicle throughput by ≥ 10 %.
3. Research a current autonomous‑vehicle platform (Waymo, Tesla, Cruise) and produce a diagram linking each sensor to the strategic, tactical and operational decision layers.
4. Design an experiment to calibrate a temperature sensor using the ice‑water (0 °C) and boiling‑water (100 °C) method; analyse how a ±0.5 °C calibration error would affect a smart‑home thermostat’s energy consumption.
5. Calculate the maximum allowable delay for safe operation in each technology (fire‑alarm activation, traffic‑light phase change, emergency‑brake in a vehicle) and compare the resulting latency budgets.