The PID controller consists of three parameters: a constant of proportionality (Kp), a constant of integration (Ki) and a constant of differentiation (Kd). The values of these three parameters can be tested and adjusted to optimize the performance of the system.
The basic principle of PID control is that when the feedback signal differs from the desired output, the controller generates a control quantity to correct the output.
Specifically, the constant of proportionality (Kp) controls the proportionality between the control quantity and the feedback signal, i.e., when the feedback signal increases, the control quantity also increases. The integration constant (Ki) controls the proportional relationship between the control quantity and the integration error, i.e., when the error persists, the control quantity will gradually increase. The differential constant (Kd) controls the proportionality between the amount of control and the differential error, i.e., when the rate of change of the error increases, the amount of control also increases.
Usually, the output of a PID controller is composed of three parts: the proportional part (proportional), the integral part (integral), and the differential part (der continuation). Specifically, the output of a PID controller can be expressed as:
Output=Kp*error+Ki*integral(error)+Kd*derivative(error)
Wherein, error denotes the difference between the feedback signal and the desired output, integral(error) denotes the integral value of error, and derivative(error) denotes the derivative value of error.
The response speed, stability and accuracy of the PID controller can be adjusted by adjusting the values of the three parameters Kp, Ki and Kd. For example, when the value of Kp increases, the response speed of the controller will increase, but at the same time will increase the risk of oscillation; when the value of Ki increases, the stability of the controller will be enhanced, but at the same time will increase the risk of integration; when the value of Kd increases, the accuracy of the controller will be enhanced, but at the same time will increase the response speed.
Common PID regulation methods are manual regulation method, ZN method (Ziegler-Nichols method) and fuzzy PID method. Manual regulation method is the simplest method, but the efficiency is low; ZN method is a classical PID regulation method, but its regulation results are greatly affected by the system; Fuzzy PID method is a fuzzy logic-based PID regulation method, which has strong adaptability and robustness.
In general, PID control is a classical and effective automatic control algorithm, which is widely used in many industrial applications. For example, PID control can be used for temperature control, carbon dioxide concentration control, flow control, pressure control, speed control, and so on.
In order to design an effective PID control system, it is usually necessary to consider the following factors:
Dynamic characteristics of the system: the parameters of the PID controller should be adjusted according to the dynamic characteristics of the system, in order to achieve a faster response speed and higher accuracy under the premise of ensuring stability.
Quality of the feedback signal: PID controller needs to obtain sufficiently accurate feedback signal in order to accurately regulate the control quantity. Therefore, the quality of the feedback signal has a great impact on the performance of the PID controller.
Conditions of the environment: The parameters of the PID controller should also be adjusted according to the conditions of the environment in order to ensure the stability of the system in a complex environment.
Accuracy requirements of the system: The accuracy requirements of the PID controller may vary depending on the application scenario. For example, in medical equipment, for temperature control, higher accuracy may be required, so the PID controller needs to be adjusted accordingly.