1 Introduction Since fuzzy control does not depend on the precise model of the system, it has certain adaptability and good robustness, so fuzzy controllers have a wide range of applications in industrial control systems. Traditional fuzzy controllers rely heavily on the experience of specific experts, so the performance optimization of fuzzy controllers has always been a concern. Among them, the parameter self-adjusting fuzzy controller is an improved fuzzy controller that automatically adjusts the quantization factors of the input and output of the fuzzy controller. There are many successful implementation schemes for fuzzy controllers, such as those based on PCs or microcontrollers. In recent years, with the development of electronic integrated manufacturing processes and computer-aided design technology, FPGA development technology based on EDA engineering [1] has emerged. Using the rich hardware resources of FPGA and convenient auxiliary development tools, some new types of automated controllers can be developed. This paper presents a parameter self-adjusting fuzzy controller with certain adaptability and gives a relevant implementation design scheme based on FPGA. 2 Parameter Self-Adjusting Fuzzy Controller Principle and Design 2.1 Fuzzy Controller Principle Ordinary fuzzy controllers include five main parts: fuzzy quantization, fuzzy rules, fuzzy decision, and fuzzy judgment. Among them, fuzzy quantization completes the conversion of the precise input quantity into the fuzzy input quantity. Fuzzy rules are a series of control rules, usually a set of IF-THEN structure rules. Fuzzy decision-making is to obtain relevant fuzzy decision values ​​based on the input fuzzy quantities and fuzzy control rules. Fuzzy judgment refers to determining a precise clear quantity from the fuzzy set of decision values. Then, the relevant control quantity is applied to the controlled object. Usually, in the process of fuzzy quantization, the input quantity is multiplied by a scaling factor to quantize it into the fuzzy interval. Similarly, the decision fuzzy quantity output by the fuzzy judgment is also multiplied by a scaling factor to quantize it into the precise interval of the control quantity. [align=center] Figure 1 Schematic diagram of the principle of a common fuzzy system[/align] Figure 1 is a schematic diagram of the principle of a common fuzzy system. In the figure, r, y, e, and ec are the system's input signal, output signal, system error signal, and system input error differential signal, respectively. E and EC are the corresponding quantized values ​​of the system input error signal and the corresponding error differential signal, quantized into the fuzzy interval, respectively. U is the decision fuzzy quantity of fuzzy judgment control, and u is the corresponding output control quantity converted from the control quantized value to the control precise interval. G1, G2, and G3 are the scaling factors for input quantization and control quantization, respectively. The proportional factors G1, G2, and G3 have a significant impact on the system response. A change in G1 alters the corresponding horizontal coverage of the control decision table, changing the horizontal region where the value of 'e' lies, and consequently, potentially changing the output control quantity. Practice shows that in the rising segment of the system response curve, changes in G1 affect the system response speed; a smaller G1 results in a smaller dead zone and a faster response, but also a larger overshoot. In the steady segment, when both 'E' and 'EC' are at their respective zero positions, the system's steady-state error is inversely proportional to the magnitude of the quantization factor G1. Similarly, a change in G2 will also affect the horizontal region where 'ec' lies, potentially changing the output control quantity. Practice shows that changes in G2 are particularly sensitive near the setpoint; a smaller G2 increases control sensitivity but also increases convergence difficulty and loses some control rules; while a larger G2 makes the controller sluggish and prone to oscillations. G3 directly affects the output of the fuzzy controller. In the initial stage, if the controller outputs in absolute terms, G3 has little effect on the control; but when the controller outputs in incremental terms, if it is working in the positive value region, the increase of G3 will increase the output accordingly, the rise will be faster, and the dead zone will be smaller; but in the convergence stage, the controller is working in the negative value region, and the increase of G3 will lead to a significant reduction in the output, causing the system to slowly approach the set value, especially in the steady stage, where a large G3 will cause oscillation [2]. From the influence of G1, G2, and G3 on the system response, it can be seen that segmenting the values ​​of the relevant proportional factors at different stages can obviously improve the control effect of fuzzy control. Therefore, in this improved fuzzy controller, the strategy of adjusting the relevant proportional factors is adopted, which is the so-called parameter self-adjusting fuzzy controller. 2.2 Self-adjusting fuzzy controller The self-adjusting fuzzy controller proposed in the literature [3] adopts the dynamic change of the relevant proportional factors according to the different magnitudes of the input error in the control. This improves the relevant control effect. 3 Design of parameter self-adjusting fuzzy controller based on FPGA After the fuzzy control theory was applied in practice, the hardware implementation of fuzzy controller has achieved great results. Generally, it is implemented using a microcontroller, PC or DSP chip and corresponding control circuit. However, the increasingly sophisticated FPGA has a great advantage over microcontrollers and DSPs. Users can reconfigure the logic modules and I/O modules inside the FPGA to implement the user's logic. It also has the characteristics of static reprogrammability and dynamic in-system reconfiguration, which makes the hardware function can be modified by programming like software. Engineers can design an FPGA-based digital system by themselves using the traditional schematic input method or hardware description language. Through software simulation, we can verify the correctness of the design in advance. After the PCB is completed, the online modification capability of the FPGA can be used to modify the design at any time without changing the hardware circuit. Using FPGA to develop digital circuits can greatly shorten the design time, reduce the PCB area, and improve the reliability of the system. However, in the early days, due to the limitations of development tools and processes, the chip implementation of many fuzzy control systems was completed by researchers in microelectronics or related fields [5]. However, due to the differences in professions, there are not many fuzzy control chips based on FPGA in many specific fields. However, in recent years, with the development of computer-aided tools, EDA engineering has emerged [1]. FPGA design and development tools have been widely adopted, and as a result, more and more FPGA-based controllers have emerged in the field of automation [6]. However, in the implementation of schemes based solely on FPGA, many data operation processes are complex. However, due to the inherent parallel processing structure of FPGA, its performance far exceeds that of traditional DSP processors when performing complex calculations. Therefore, if the design is reasonable, the calculation speed of dedicated modules developed using FPGA is generally faster than that of some traditional DSPs and other microprocessors [7]. If an FPGA with built-in DSP hardware resources is selected, the design structure will be more reasonable. However, in general, the cost of developing FPGAs with DSP resources is very high, both in terms of chips and development tools. However, some low-cost FPGAs with built-in DSPs have emerged. For the two major FPGA manufacturers, Xilinx and Altera, they provide low-cost chips that complement the advantages of DSP and FPGA, currently namely Spartan-3 and Cyclone III. These chips are currently widely used in the field of communication, but not widely used in the implementation of industrial automation systems. Therefore, the implementation of parameter self-adjusting fuzzy controllers can use such low-cost FPGA chips with DSPs as the hardware basis. Figure 4 is a schematic diagram of the fuzzy controller implementation structure. The A/D controller section controls the A/D conversion controller. The fuzzy quantization factor adjustment module selects the appropriate scaling factor based on the system error. The fuzzy decision module performs fuzzy inference based on fuzzy rules and the input fuzzy quantity to obtain the fuzzified control quantization value. The fuzzy decision module converts the fuzzy decision control quantization value into a specific control quantity, including the fuzzy output quantization factor. The main control module coordinates the work between the modules, specifically providing clock signals, reset signals, and start/stop signals to each module. 4. Conclusion The parameter self-adjusting fuzzy controller improves its performance by dynamically adjusting the scaling factor during fuzzy quantization. The control approach is clear and easy to implement. Furthermore, the analyzed parameter self-adjusting fuzzy controller has a certain adaptability to control systems in different environments. Finally, an implementation scheme for this fuzzy controller is presented, which provides valuable reference for the implementation of similar control systems. 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