Control systems are an essential part of our modern-day life, from the temperature control of our homes to the flight control systems of aircraft. These systems are used to regulate and stabilize processes to meet desired objectives. Different control techniques are used depending on the application and the system’s requirements. In this article, we will discuss some of the most commonly used control techniques and their applications.
- On-Off Control
1.1 Hysteresis Control: temperature control of a room using a thermostat with a hysteresis band.
1.2 Time-Proportional Control: controlling the temperature of a furnace by cycling it on and off with a variable duty cycle. - Proportional Control
2.1 Two-Position Control: controlling the level of a liquid in a tank by turning a pump on and off.
2.2 Proportional Band Control: controlling the temperature of a chemical reactor by varying the power input to a heater. - Integral Control
3.1 Reset Windup Prevention: controlling the speed of a motor using a PID controller with integral action to prevent overshoot and windup. - Derivative Control
4.1 Rate-of-Change Limiting: controlling the position of a robotic arm by limiting the rate of change of the velocity. - Proportional-Integral Control (PI Control)
5.1 Dead Time Compensation: controlling the temperature of a furnace with a PI controller that compensates for time delays in the heating process.
5.2 Anti-Windup Prevention: controlling the position of an aircraft using a PI controller with anti-windup to prevent saturation of the actuator. - Proportional-Derivative Control (PD Control)
6.1 High-Frequency Noise Filtering: controlling the pressure of a pneumatic system using a PD controller with a high-pass filter to filter out high-frequency noise. - Proportional-Integral-Derivative Control (PID Control)
7.1 Manual Tuning: controlling the speed of a conveyor belt using a PID controller that is manually tuned by an operator.
7.2 Ziegler-Nichols Tuning: controlling the temperature of a chemical reactor using a PID controller that is tuned using the Ziegler-Nichols method.
7.3 Cohen-Coon Tuning: controlling the level of a tank using a PID controller that is tuned using the Cohen-Coon method. - Feedforward Control
8.1 Static Feedforward Control: controlling the position of a robot arm using a feedforward controller that compensates for gravity and friction.
8.2 Dynamic Feedforward Control: controlling the position of a satellite using a feedforward controller that compensates for disturbances in the orbit. - Model Predictive Control (MPC)
9.1 Dynamic Matrix Control (DMC): controlling the temperature of a furnace using a model predictive controller that uses a dynamic matrix model of the system.
9.2 Model Reference Control (MRC): controlling the position of a robot using a model predictive controller that uses a reference model of the system.
9.3 Model Predictive Control with Constraints (MPC-C): controlling the speed of a car using a model predictive controller that takes into account safety constraints.
9.4 Linear Quadratic Gaussian (LQG) Control: controlling the pitch and roll of an aircraft using a model predictive controller that uses a linear-quadratic-Gaussian model of the system. - Sliding Mode Control (SMC)
10.1 Backstepping Control: controlling the position of a helicopter using a sliding mode controller with a backstepping algorithm.
10.2 Passivity-Based Control: controlling the position of a robot arm using a sliding mode controller with a passivity-based algorithm.
10.3 Adaptive Backstepping Control: controlling the speed of a car using a sliding mode controller with an adaptive backstepping algorithm. - Adaptive Control
11.1 Model Reference Adaptive Control (MRAC): Used in aircraft control systems, robotics, and industrial processes.
11.2 Self-Tuning Control: Used in chemical processes, aerospace control systems, and robotics. - Fuzzy Logic Control (FLC): Used in air conditioning systems, washing machines, and other consumer electronics.
- Robust Control
13.1 H-infinity Control: Used in aerospace control systems, automotive control systems, and industrial processes.
13.2 Mu Synthesis Control: Used in aerospace control systems, automotive control systems, and industrial processes.
13.3 Structured Singular Value (SSV) Control: Used in aerospace control systems, automotive control systems, and industrial processes. - Kalman Filter Control
14.1 Extended Kalman Filter Control: Used in aerospace control systems, automotive control systems, and robotics.
14.2 Unscented Kalman Filter Control: Used in robotics, autonomous vehicles, and aerospace control systems.
14.3 Particle Filter Control: Used in autonomous vehicles, robotics, and aerospace control systems. - Other Control Techniques
15.1 Gain Scheduling Control: Used in aircraft control systems, automotive control systems, and industrial processes.
15.2 Smith Predictor Control: Used in process control systems and robotics.
15.3 Cascade Control: Used in process control systems, automotive control systems, and robotics.
15.4 Decoupling Control: Used in process control systems and robotics.
15.5 State-Space Control: Used in aerospace control systems, automotive control systems, and industrial processes.
15.6 Output Feedback Control: Used in aerospace control systems, automotive control systems, and industrial processes.
15.7 Disturbance Observer (DOB) Control: Used in industrial processes and robotics.
15.8 Repetitive Control: Used in robotics, machine tools, and other industrial processes.
15.9 Fractional Order Control: Used in control systems with fractional dynamics, such as electrochemical processes and biomedical systems.
15.10 Time Delay Control: Used in process control systems, robotics, and aerospace control systems.
15.11 Adaptive Sliding Mode Control: Used in aerospace control systems, automotive control systems, and robotics.
15.12 Artificial Neural Network (ANN) Control: Used in process control systems and robotics.
15.13 Hybrid Control: Used in complex systems that require multiple control techniques, such as automotive control systems and robotics.
15.14 Quantum Control: Used in quantum systems and quantum computing.
Classification according to open-loop and closed-loop
Some of the techniques are common because they can be implemented in that way.
| Open-loop control techniques | closed-loop control techniques |
| 1. On-Off Control | 1. Proportional Control |
| 1.1 Hysteresis Control | 1.1 Two-Position Control |
| 1.2 Time-Proportional Control | 1.2 Proportional Band Control |
| 2. Proportional Control | 2. Integral Control |
| 2.1 Two-Position Control | 2.1 Reset Windup Prevention |
| 2.2 Proportional Band Control | 3. Derivative Control |
| 3. Integral Control | 3.1 Rate-of-Change Limiting |
| 3.1 Reset Windup Prevention | 4. Proportional-Integral Control (PI Control) |
| 4. Derivative Control | 4.1 Dead Time Compensation |
| 4.1 Rate-of-Change Limiting | 4.2 Anti-Windup Prevention |
| 5. Proportional-Integral Control (PI Control) | 5. Proportional-Derivative Control (PD Control) |
| 5.1 Dead Time Compensation | 5.1 High-Frequency Noise Filtering |
| 5.2 Anti-Windup Prevention | 6. Proportional-Integral-Derivative Control (PID Control) |
| 6. Proportional-Derivative Control (PD Control) | 6.1 Manual Tuning |
| 6.1 High-Frequency Noise Filtering | 6.2 Ziegler-Nichols Tuning |
| 7. Proportional-Integral-Derivative Control (PID Control) | 6.3 Cohen-Coon Tuning |
| 7.1 Manual Tuning | 7. Adaptive Control |
| 7.2 Ziegler-Nichols Tuning | 7.1 Model Reference Adaptive Control (MRAC) |
| 7.3 Cohen-Coon Tuning | 7.2 Self-Tuning Control |
| 8. Feedforward Control | 8. Fuzzy Logic Control (FLC) |
| 8.1 Static Feedforward Control | 9. Model Predictive Control (MPC) |
| 8.2 Dynamic Feedforward Control | 10. Sliding Mode Control (SMC) |
| 9. Gain Scheduling Control | 11. Backstepping Control |
| 9.1 Linear Gain Scheduling Control | 12. Linear Quadratic Regulator (LQR) Control |
| 9.2 Nonlinear Gain Scheduling Control | 13. Optimal Control |
| 10. Model Predictive Control (MPC) | 13.1 Model Predictive Control with Constraints (MPC-C) |
| 10.1 Dynamic Matrix Control (DMC) | 13.2 Linear Quadratic Gaussian (LQG) Control |
| 10.2 Model Reference Control (MRC) | 14. Nonlinear Control |
| 11. Artificial Neural Network (ANN) Control | 14.1 Feedback Linearization |
| 11.1 Feedforward Neural Network Control | 14.2 Passivity-Based Control |
| 11.2 Feedback Neural Network Control | 14.3 Adaptive Backstepping Control |
| 12. Adaptive Control | 15. Robust Control |
| 12.1 Model Reference Adaptive Control (MRAC) | 15.1 H-infinity Control |
| 12.2 Self-Tuning Control | 15.2 Mu Synthesis Control |
| 13. Fuzzy Logic Control (FLC) | 15.3 Structured Singular Value (SSV) Control |
| 14. Hybrid Control | 16. Kalman Filter Control |
| 14.1 Event-Triggered Control | 17. Extended Kalman Filter Control |
| 14.2 Time-Triggered Control | 18. Unscented Kalman Filter Control |
| 15. Quantum Control | 19. Particle Filter Control |
| 20. Gain Scheduling Control | |
| 21. Smith Predictor Control | |
| 22. Cascade Control | |
| 23. Feedforward Control | |
| 24. Decoupling Control | |
| 25. State-Space Control | |
| 26. Output Feedback Control | |
| 27. Disturbance Observer (DOB) Control | |
| 28. Repetitive Control | |
| 29. Fractional Order Control | |
| 30. Time Delay Control | |
| 31. Adaptive Sliding Mode Control | |
| 32. Artificial Neural Network (ANN) Control | |
| 33. Hybrid Control | |
| 34. Quantum Control |
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