In the field of modern industrial automation, process control technology is one of the key factors to ensure production efficiency and product quality. The PID (Proportional-integral-Derivative) controller, as the most common feedback control mechanism, is widely used in the precise regulation of parameters such as temperature, pressure and flow rate. With the advancement of Industry 4.0 and the development of technology, modern control systems not only require high efficiency and stability, but also higher flexibility and programmability. As a globally leading provider of industrial automation solutions, Siemens’ SIMATIChttps://heiautomation.com/product/ series of PLCS (Programmable Logic Controllers) are widely used in various industrial scenarios. SCL (Structured Control Language) is a high-level programming language based on the IEC 61131-3 standard, providing rich data types, structured programming methods and powerful mathematical computing capabilities. This article will detail how to implement PID control in Siemens PLC using SCL, and explore its application cases and optimization strategies.
Principle of PID Control
A PIDhttps://www.youtube.com/@%E6%9B%BE%E6%85%A7%E5%A9%B7-z6j/shorts controller is a linear controller, and its control signal consists of three parts: the proportion (P) of the error, the integral (I), and the differential (D). Specifically:
Proportional control: The output is directly adjusted based on the current error value. The proportional coefficient Kp determines the intensity of the control effect.
Integral control: Accumulate all past error values, eliminatehttps://heiautomation.com/about/ steady-state errors through the integral term, and the integral time constant Ti affects the speed of the integral action.
Differential control: Predict the changing trend of future errors, improve the response speed of the system through differential terms, and the differential time constant Td controls the sensitivity of the differential action.
The output u(t) of the PID controller can be expressed as:
Picture
Among them, e(t) is the error between the set value and the actual value.
The basics of the SCL language
SCLhttps://www.youtube.com/@%E6%9B%BE%E6%85%A7%E5%A9%B7-z6j is a high-level programming language similar to Pascal and has the following characteristics:
Structured programming: Supports the definition of functions, procedures, data types, etc., making the program structure clear and easy to understand.
- Rich data types: In addition to basic data types, it also supports composite data types such as arrays and structures.
- Powerful mathematical computing capabilities: Built-in multiple mathematical functions for convenient complex calculations.
- Modular design: Supports modular programming, facilitating code reuse and maintenance.
PID control is implemented using SCL
Define data types and variables
First of all, it is necessary to define the data types and variables required by the PID controller. For example, a structure can be defined to store the status information of the PID https://www.facebook.com/profile.php?id=100087291657130controller:“`scl
TYPE PID_DATA :
STRUCT
Kp : REAL; (* Proportionality coefficient *)
Ki : REAL; (* Integral coefficient *)
Kd : REAL; (* Differential coefficient *)
SetPoint : REAL; (* Set value *
ProcessVar : REAL; (* Actual value *)
Error : REAL; (* Error *)
Integral : REAL; (* Integral term *)
Derivative : REAL; (* Differential term *)
Output : REAL; (* Output value *)
END_STRUCT
END_TYPE
`Initialize the PID controller
Before the program starts running, the PID controller needs to be initialized and the initial parameters set:“`scl
VAR
pid : PID_DATA;
END_VAR
pid.Kp := 1.0;
pid.Ki := 0.1;
pid.Kd := 0.01;
pid.SetPoint := 100.0;
pid.ProcessVar := 0.0;
Pi.error := 0.0;
pid.Integral := 0.0;
pid.Derivative := 0.0;
pid.Output := 0.0;
`Write the PID control algorithm
Next, write the core part of the PID control algorithm. Here, a periodic https://heiautomation.com/contact-us/interrupt program block (such as OB35) is used to call the PID control function:“`scl
FUNCTION_BLOCK PID_CONTROL
VAR_INPUT
Kp : REAL;
Ki : REAL;
Kd : REAL;
SetPoint : REAL;
ProcessVar : REAL;
END_VAR
VAR_OUTPUT
Output : REAL;
END_VAR
VAR
LastError : REAL;
LastTime : TIME;
END_VAR
VAR_TEMP
CurrentTime : TIME;
Error : REAL;
DeltaTime : TIME;
END_VAR
(* Calculate the current time and time difference *)
CurrentTime := T#0ms;
DeltaTime := CurrentTime – LastTime;
(* Calculation error *)
Error := SetPoint – ProcessVar;
(* Proportion item *)
Output := Kp * Error;
(* Integral term *)
pid.Integral := pid.Integral + (Ki * Error * DeltaTime);
(* Differential term *)
pid.Derivative := Kd * ((Error – LastError) / DeltaTime);
(* Update the output value *)
Output := Output + pid.Integral + pid.Derivative;
(* Update the error and time of the last time *)
LastError := Error;
LastTime := CurrentTime;
END_FUNCTION_BLOCK
`Call the PID control function
Call the PID control function in the main program or interrupt program https://studio.youtube.com/channel/UC5jh-_J8N51BACPDqMnnT6Q/videos/short?filter=%5B%5D&sort=%7B%22columnType%22%3A%22date%22%2C%22sortOrder%22%3A%22DESCENDING%22%7Dand update the actual value:“`scl
VAR
pid : PID_DATA;
pidControl : PID_CONTROL;
END_VAR
(* Update the actual value *)
pid.ProcessVar := ReadSensorValue();
(* Call the PID control function *)
pidControl(Kp := pid.Kp, Ki := pid.Ki, Kd := pid.Kd, SetPoint := pid.SetPoint, ProcessVar := pid.ProcessVar, Output => pid.Output);
(* Send the PID output value to the actuator *)
WriteActuatorValue(pid.Output);
`Application case
Temperature control
In a temperature control application, a PID controller is used to maintain a constant furnace temperature. Suppose there is an electric furnace with the target temperature set at 100° C. The sensor reads the current temperature, and the PID controller calculates the appropriate heating power, which is adjusted through anhttps://heiautomation.com/ actuator (such as an electric heating wire). By adjusting the PID parameters, rapid response and stable temperature control can be achieved.
Instance data:
- Set value: 100°C
- Initial actual value: 80°C
- Proportional coefficient (K_p ) : 1.0
Integral coefficient (K_i ) : 0.1
- Differential coefficient (K_d ) : 0.01
Through the above parameter Settings, the system can raise the temperature from 80°C to 100°C within 10 minutes and keep it stable.
Flow control
In liquid flow control, the PID controller is used to maintain the stability of the flow within the pipeline. Suppose there is a liquid delivery system with the target flow rate set at 100 L/min. The flowmeter https://www.facebook.com/profile.php?id=100087291657130measures the current flow rate, and the PID controller calculates the change in the valve opening. The flow rate is controlled by adjusting the valve opening. PID control can effectively deal with flow fluctuations and ensure the stability of the production process.
Instance data:
- Set value: 100 L/min
- Initial actual value: 90 L/min
- Proportional coefficient (K_p ) : 0.5
- Integral coefficient (K_i ) : 0.05
- Differential coefficient (K_d ) : 0.005
Through the above parameter Settings, the system can adjust the flow rate from 90 L/min to 100 L/min within 5 minutes and maintain stability.
Optimization strategy
Parameter tuning
The performance of a PID controller largely depends on the selection of parameters. Common parameter tuning methods include:
Ziegler-Nichols method: Gradually increase the proportional gain until the system oscillates, and then calculate the PID parameters based on the oscillation period and amplitude. For example, for a temperature control system, the following parameters can be obtained through the Ziegler-Nichols method:
Critical gain (K_u ) : 2.0
Critical period (T_u ) : 10 seconds
- Recommended parameters:
(K_p = 0.6 times K_u = 1.2)
(T_i = 0.5 \times T_u = 5 ) seconds
(T_d = 0.125 times T_u = 1.25) seconds
Trial-and-error method: By constantly adjusting the PID parameters, observing the system response, and gradually optimizing the parameters. This method is applicable to systems without a fixed model.
Anti-integral saturation
The integral term is prone to cause the integral saturation https://www.facebook.com/profile.php?id=100087291657130problem, that is, the accumulation of the integral term is too large, resulting in system overtuning. Integral saturation can be avoided by setting integral limits or introducing integral separation techniques. For example, the maximum value of the integral term can be set to 1000 and the minimum value to -1000 to prevent integral saturation.
Feedforward control
For some systems with obvious interference, feedforward control can be introduced to compensate for the interference in advance and improve the robustness and response speed of the system. For instance, in a flow control system, if the upstream flow changes significantly, the valve opening can be adjusted in advance through feedforward control to reduce the system’s response time.
Conclusion
The implementation of PID control in Siemens PLChttps://www.youtube.com/watch?v=vjpjyv7nYGQ through SCL language can not only improve the accuracy and stability of the control system, but also enhance the flexibility and scalability of the system. This article elaborates on the basic principles of PID control, the fundamental knowledge of the SCL language, and the specific steps on how to implement PID control using SCL. Through the discussion of practical application cases and optimization strategies, it is hoped that readers can better understand and apply PID control technology to enhance the level of industrial automation.