Fuzzy Parameter Self-tuning PID Control Based on LabVIEW and MATLAB

1 Introduction

The traditional PID controller has a simple structure, good stability, high reliability, and mature manufacturing technology. It has been widely used in the control of industrial production processes. However, it is mainly suitable for controlling linear processes with exact models, and cannot achieve ideal control effects for systems with nonlinearity, large lags, and time-varying uncertainties. The rise and rapid development of artificial intelligence has provided a new method for the control field. Fuzzy control is an important branch of artificial intelligence control. It uses the basic theories and methods of fuzzy mathematics to represent the conditions and operations of rules with fuzzy sets, and stores these fuzzy control rules and related information as knowledge in the computer knowledge base. Then, the computer uses fuzzy reasoning to determine the size of the system control quantity based on the actual response of the control system. Combining fuzzy theory with PID control strategy can realize online adaptive adjustment of PID parameters, so that the system has the advantages of flexibility and strong adaptability of fuzzy control and the characteristics of high precision of PID control. Fuzzy controller is one of the current research and development hotspots in the field of control, and its research and development methods are different. This paper describes the use of LabVIEW and MATLAB hybrid programming technology to integrate the fuzzy logic toolbox (FIS Toolbox) in MATLAB with the LabVIEW virtual instrument development software, develop a fuzzy parameter self-tuning PID virtual controller, and realize real-time measurement and control of nonlinear systems.

2 Fuzzy-PID control strategy

The fuzzy parameter self-tuning PID controller takes the system deviation E and the deviation change EC as input, which can meet the requirements of E and EC for PID parameter self-tuning at different times. By modifying the PID parameters online using fuzzy control rules, a fuzzy parameter self-tuning PID control system (hereinafter referred to as Fuzzy-PID control system) can be constructed, and its structure is shown in Figure 1:

3 Controller Design

The core of fuzzy control design is to summarize the technical knowledge and practical experience of engineering designers, establish a suitable fuzzy rule table, and form an inference structure to implement fuzzy rules.

3.1 Establishment of fuzzy rule table

3.2 Editing of FIS Reasoning Structure

In MATLAB, there are two ways to edit FIS structures, one is direct programming, and the other is using the FIS editor. This article uses the method of combining the FIS editor with Simulink.

Enter Fuzzy in the MATLAB command window to open the basic FIS editor. The editing steps are as follows:

3.4 Design of Fuzzy Controller

LabVIEW and MATLAB have different focuses in application fields, and each has its own advantages. Therefore, the hybrid programming advantages of the two are complementary in engineering, which has a huge driving force for the development of more powerful intelligent virtual instruments. This paper uses the MATLAB Script Node integrated in LabVIEW to realize the hybrid programming of the two and complete the design of fuzzy parameter self-tuning PID. The design program of the controller is shown in Figure 5.

The flowchart of Fuzzy-PID consists of a while loop, in which functions such as manual-automatic switching, deviation processing, parameter adjustment, controller initialization, and calling model dynamic link library files are implemented. This while loop is executed only once each time it is called. The purpose of using the loop here is to use the shift register of the while loop to save data. The shift register can be used to pass the value of the previous loop to the next loop, so the running status and intermediate results of the program can be recorded with the help of the shift register, which will be used when the program is called again. The first shift register in the above figure is used as a manual output holder to keep the manual output value, and the second register records the running status of the controller. The two are combined to realize the manual-automatic disturbance-free switching of the controller.

4 Controller Application

In order to verify the actual control effect of the Fuzzy-PID controller, this paper takes the nonlinear liquid level process in the Beijing Huasheng A3000 advanced process control experimental system as the controlled object, and uses the ADAM4000 I/O module produced by Advantech to connect the designed controller with the controlled object, forming a control system with fuzzy self-tuning PID parameters, and compares the control quality of the system when subjected to interference with the control quality of the conventional PID control system. The control effects of the two systems are shown in Figure 6.

As shown in Figure 6, when the system setting value or disturbance undergoes a large step change, the anti-interference ability, tracking effect, and control quality of the Fuzzy-PID control system are significantly better than those of conventional PID control.

5 Conclusion

In the MATLAB environment, the Fuzzy Logic Toolbox (FIS) is used to build the Fuzzy-PID algorithm of the virtual controller, and LabVIEW is used to design other functions required in engineering applications such as human-machine interface, signal acquisition, data processing and storage. By using the "MATLAB Script Node" integrated in LabVIEW, LabVIEW and MATLAB are mixed for programming to achieve the integration of the above two parts of the design and complete the design of the fuzzy self-tuning PID parameter controller. The actual control results of the Fuzzy-PID control system show that the designed virtual controller has strong adaptability and robustness for the control of the controlled object with more serious nonlinear characteristics, and its control quality is better than that of the conventional PID controller.