A1.3.7 Explain the use of control systems in a range of real-world applications. (HL only)
• Examples may include autonomous vehicles, home thermostats, automatic elevator controllers, automatic washing machines, traffic signal control systems, irrigation control systems, home security systems, automatic doors
The big idea
Control systems give physical things the ability to meet precise goals automatically: keep a room at 21 °C, hold an elevator level with a floor, or guide a car down a lane. They do this by closing a feedback loop—sense → compute → actuate → repeat—so that deviations from the target are continually corrected despite disturbance and uncertainty.
Eight representative applications
| Application | Controlled variable(s) | Typical sensors | Processing / control algorithm | Actuators & output devices | Key engineering constraints |
|---|---|---|---|---|---|
| Autonomous vehicle | Lateral position, speed, yaw rate, following distance | LiDAR, radar, cameras, IMU, wheel encoders, GNSS | Multilayer stack: perception (CNNs), sensor fusion (EKF), trajectory planning (MPC), low-level PID for steering & torque | Electric steering, brake-by-wire, throttle, shift control | <50 ms end-to-end latency, fail-operational safety, ISO 26262 functional safety, cybersecurity, redundancy |
| Home thermostat (HVAC) (helpful video here) | Indoor air temperature (and often humidity) | Thermistor, humidity probe, occupancy PIR | On–off (bang-bang) or discrete PID with dead-band and adaptive gain | Relay or triac driving compressor, fan and heating elements | Energy efficiency, wear minimisation, ±0.5 °C accuracy, user comfort |
| Automatic elevator controller | Car position, velocity, door state | Shaft encoder, limit switches, load cell, door light curtain | State-machine supervisor + closed-loop speed PID; jerk-limited motion profile | VVVF drive on hoist motor, brake solenoid, door motor | Ride comfort (jerk < 1 m/s³), floor-level accuracy ±5 mm, SIL 3 safety, emergency braking |
| Automatic washing machine | Water level, drum speed, temperature | Pressure switch, hall RPM sensor, NTC thermistor, lid switch | Timed sequence with nested PID loops for temperature and spin speed; fuzzy logic for load estimation | Fill valve, heater, pump, inverter-driven motor | Fabric protection, water/energy use, noise, imbalance detection |
| Traffic-signal control system | Phase timing, queue length at junctions | Inductive loops, video analytics, magnetometers, V2X beacons | Fixed-time plan, vehicle-actuated, or adaptive algorithms (SCOOT/SCATS, RL-based MPC) | LED signal heads, pedestrian indicators, networked controllers | Maximise throughput, minimise delay, coordinate corridors, fault tolerance |
| Irrigation control system | Soil moisture, local evapotranspiration | Capacitance probe, rain gauge, ambient temp & RH, flow meter | Rule-based or PID with weather forecast feed-forward | Solenoid valves, pump VFD, fertigation injector | Water conservation, crop-specific constraints, anti-water-hammer, remote telemetry |
| Home security system | State of entry points, motion presence | Magnetic reed switches, PIR, glass-break microphone, camera | Event-driven finite-state machine, sensor fusion, false-alarm filtering | Siren, SMS/Internet alert, smart-lock command | Low power (battery backup), tamper detection, encrypted comms, 24–7 availability |
| Automatic doors | Door position and speed | Infrared presence sensor, encoder, safety light curtain | Speed-profile PID with obstruction detection; supervisory logic for modes (auto, hold-open, night) | Brushless DC motor, brake, lock solenoid | EN16005 safety, <1 s open time, pinch-force limit, reliable in dirt/wind |
Common architectural patterns across the examples
- Layered control
High-level supervisor (state machine or planner) invokes one or more low-level continuous controllers (PID, MPC), each running at its own sampling rate.
Example: The elevator scheduler decides the next destination floor; a separate PID loop shapes the speed–position profile. - Redundant sensing for safety
Life-critical systems (cars, elevators) duplicate sensors of different modalities so that a single failure is detectable and correctable. - Adaptive or learning controllers
Traffic signals and irrigation rigs increasingly apply reinforcement learning or model-predictive control to cope with stochastic demand and weather. - Energy-aware actuation
HVAC and washing machines modulate inverter drives or compressor duty cycles to trade response speed for kilowatt-hours saved.
Why feedback matters
- Disturbance rejection An autonomous car must stay centred even if cross-wind knocks it sideways; a thermostat must hold temperature when a door opens.
- Component variability No two pumps deliver exactly the same flow; closed-loop control hides that variance.
- Predictive behaviour By modelling future plant dynamics (MPC) systems like traffic controllers achieve smoother, globally optimal actions.
Practical considerations for designers
- Latency budgeting Compute and I/O delays must be well below the dominant time constant of the plant (tens of µs for motor commutation, seconds for room heating).
- Sampling theorem Digital controllers sample ≥ 10× the system bandwidth to avoid aliasing and phase-lag induced instability.
- Fault detection Watchdogs, heartbeat links, and plausibility checks (e.g., door speed vs. encoder count) prevent uncontrolled operation.
- Standards compliance IEC 61508, ISO 13849, UL 325, and automotive FuSa standards define evidence required for safety integrity.
Here’s a technically specific collection of YouTube links matched to our eight representative control systems.
| Application | YouTube Link (Technical Focus) | Why It’s Appropriate |
|---|---|---|
| Autonomous vehicle | https://www.youtube.com/watch?v=CkUTJYGE7oY (youtube.com) | Demonstrates research-level autonomous vehicle control: perception, sensor fusion, feedback steering and throttle control. |
| Home thermostat (HVAC) | https://www.youtube.com/watch?v=NNp98ODpBHg (youtube.com) | Walks through an industrial PID temperature controller, showing real feedback control used in thermostat systems. |
| Automatic elevator controller | https://www.youtube.com/watch?v=q2vBVU4nna8 (youtube.com) | Engineering-oriented elevator control system demo with motion and safety logic. |
| Automatic washing machine | https://www.youtube.com/watch?v=HXFU9sq3PqY (youtube.com) | A PID control tutorial where temperature control and speed loops like those in washers are explained. |
| Traffic-signal control system | https://www.youtube.com/watch?v=3jyRp6SVao8 (youtube.com) | Shows an adaptive traffic control algorithm managing cycle timing and vehicle actuation. |
| Irrigation control system | https://www.youtube.com/watch?v=X2fWmf3cnIE (youtube.com) | A practical look at programming an irrigation controller — zones, sensors, and feedback interfaces. |
| Home security system | https://www.youtube.com/watch?v=pqKrPb72H8E (youtube.com) | Builds a security alert system using motion sensors — demonstrating sensing, event logic, and alert output. |
| Automatic doors | https://www.youtube.com/@AutomateddoorsystemsCoUk/videos (youtube.com) | A channel full of real automatic door system videos showing sensors, controllers, and actuators in industrial settings. |
Notes on selection and technical relevance
These videos link to real control behavior rather than hobby demos:
- The autonomous vehicle video isn’t a toy simulator; it shows systems being tested in the field and highlights core control challenges like sensor fusion and closed-loop steering. (youtube.com)
- The thermostat PID controller example uses a solid-state controller, not a home toy thermostat, so students can see how PID parameters influence control behavior in practice. (youtube.com)
- For elevator and traffic control, the chosen videos illustrate real industrial control strategies rather than purely marketing or conceptual content. (youtube.com)
- The irrigation controller video explains control zones and programming, which maps onto the environmental feedback logic in your table. (youtube.com)
These links are solid for students to observe how system architecture (sensors → control logic → actuators) manifests in engineering implementations. You can support each with explanations of how sensors map to control loops, what control algorithm patterns they illustrate (PID vs state machine vs adaptive logic), and what constraints (latency, safety, accuracy) matter in each domain.
Take-away
From the thermostat on a living-room wall to the multilayer control stack of a self-driving car, feedback control turns intent into stable, precise behaviour in the physical world. The specific sensors, algorithms, and actuators vary, but every system follows the same closed-loop blueprint: measure, compare, correct—again and again, fast enough and reliably enough to keep the real world in line with the desired state.