**Introduction to PID Control**
In today's industrial landscape, the level of automation has become a key indicator of modernization across various sectors. The evolution of control theory has gone through three major stages: classical control theory, modern control theory, and intelligent control theory. An example of intelligent control is a fully automatic washing machine that uses fuzzy logic. A typical automatic control system consists of a controller, sensor, transmitter, actuator, and input/output interfaces. The controller’s output is sent to the controlled system via the actuator and output interface, while the controlled variable is fed back to the controller through the input interface using the sensor and transmitter.
Different systems use different sensors, transmitters, and actuators. For instance, pressure control systems use pressure sensors, while temperature control systems use thermocouples or RTDs. Today, there are numerous PID controllers and intelligent PID regulators available in the market, which have found widespread application in engineering. These include pressure, temperature, flow, and level controllers, as well as programmable logic controllers (PLCs) and PC-based systems that can implement PID control. PLCs often use built-in closed-loop modules for PID control, and some models, such as Rockwell’s PLC-5 and Logix series, can be connected directly to ControlNet for remote control.
**Parameter Tuning of PID Controller**
Tuning the parameters of a PID controller is a critical part of control system design. It involves determining the proportional gain, integral time, and derivative time based on the characteristics of the process being controlled. There are two main approaches to tuning: theoretical calculation and engineering practice. Theoretical methods rely on mathematical models, but the results often require adjustment through real-world testing. Engineering methods, on the other hand, depend on experience and are commonly used in practical applications.
The most widely used engineering methods for PID tuning include the critical ratio method, reaction curve method, and decay method. Each method involves conducting tests and adjusting the controller parameters based on empirical formulas. Although these methods differ in their approach, they all require fine-tuning during actual operation. Among them, the critical ratio method is the most commonly used today.
The steps for tuning PID parameters using the critical ratio method are as follows:
1. First, select a short sampling period that allows the system to respond quickly.
2. Apply only the proportional control until the system reaches a critical oscillation in its step response. Record the proportional gain and the critical oscillation period at this point.
3. Calculate the PID parameters using a specific formula based on the desired level of control performance.
Empirical values for PID parameters in different control systems are as follows:
- Temperature (T): P = 20~60%, Ti = 180~600s, Td = 3~180s
- Pressure (P): P = 30~70%, Ti = 24~180s
- Liquid Level (L): P = 20~80%, Ti = 60~300s
- Flow Rate (L): P = 40~100%, Ti = 6~60s
**PID Common Rules:**
Tuning PID parameters is an art that requires both experience and understanding. The general rule is to start with small values and gradually increase them. Begin with the proportional term, then add the integral, and finally introduce the derivative. If the system oscillates frequently, increase the proportional band. If the curve fluctuates around the setpoint, reduce the proportional band. If the response is slow, decrease the integral time; if the oscillations are long, increase the integral time. Fast oscillations suggest reducing the derivative, while large momentum and slow fluctuations call for increasing the derivative time. An ideal response shows two peaks, with the second peak about one-fourth the height of the first. Through careful observation and multiple adjustments, the control quality can be significantly improved.
**PID Regulation Experience**
Kp (Proportional coefficient): This determines how strongly the controller reacts to the current error. A higher Kp leads to faster responses but may cause overshoot.
Ti (Integral Time): This affects how quickly the controller eliminates steady-state error. A smaller Ti means more aggressive integral action.
Td (Derivative Time): This predicts future errors based on the rate of change of the error, helping to improve stability.
For example:
- Temperature (T): P = 20~60%, Ti = 180~600s, Td = 3~180s
- Pressure (P): P = 30~70%, Ti = 24~180s
- Liquid Level (L): P = 20~80%, Ti = 60~300s
- Flow Rate (L): P = 40~100%, Ti = 6~60s
When tuning, observe the system behavior. If the curve oscillates too much, increase the proportional band. If the deviation is large and the system doesn’t return quickly, reduce the integral time. If the system is unstable, consider reducing the derivative action. If the system becomes too sluggish, increase the derivative time. However, if the integral time is too long or the derivative time is too large, it may lead to instability that cannot be corrected by adjusting the proportional band alone.
**PID Debugging Steps**
PID control is one of the most widely used and enduring control algorithms. Many modern regulators are based on or derived from PID principles. It is considered the foundation of many advanced control strategies. The reason for its widespread use lies in its ability to ensure system stability, speed, and accuracy. By adjusting the parameters, the system can achieve good load capacity and anti-interference capability without compromising stability.
To debug a PID controller, follow these steps:
1. **Negative Feedback**
Ensure that the system uses negative feedback. For example, in a motor speed control system, the feedback signal should oppose the input signal. If the motor is supposed to rotate forward, the feedback should be negative relative to the input.
2. **General Principles of PID Debugging**
- Increase the proportional gain when the output is stable and not oscillating.
- Reduce the integral time when the system is stable.
- Increase the derivative time when the system is stable and responsive.
3. **General Procedure**
a. Start by setting the proportional gain (P). Disable the integral and derivative terms initially. Gradually increase P until oscillation occurs, then reduce it slightly to eliminate oscillation. Set P to approximately 60%–70% of the maximum value before oscillation.
b. Next, adjust the integral time (Ti). Start with a large Ti and gradually decrease it until oscillation occurs. Then increase Ti slightly to stabilize the system. Set Ti to 150%–180% of the adjusted value.
c. Finally, adjust the derivative time (Td). Typically, Td is not needed unless the system is very sensitive. If used, set it to about 30% of the oscillation period.
d. Test the system under both loaded and unloaded conditions, and fine-tune the parameters until the desired performance is achieved.
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