Through analysis and research on the current status of the range hood industry, this paper focuses on the high energy consumption, high noise, low energy utilization of the range hood, and carries out research on the new energy-saving and environmental protection range hood control module. At present, there are no molded products for range hoods with chip-level control modules in China. Western countries have not yet developed products in this area due to different dietary structures.
The hood control module studied in this paper includes two parts: multivariable intelligent fuzzy control module and SVPWM vector drive module. The fuzzy control module is based on Alter's EP2C35 series FPGA. It can comprehensively analyze the kitchen cooking environment according to the three variables of humidity and soot volume, establish fuzzy math rules of multivariable system, determine the output speed value, and finally realize the fan speed. Real-time stepless frequency conversion speed regulation, while reducing the noise of the range hood, achieving the purpose of environmental protection and noise reduction.
Because there are many factors affecting the real-time variable frequency speed regulation of the range hood, including the temperature of the stove, the concentration of soot discharged and the concentration of cooking water vapor, etc., and cross-coupling with each other, the characteristics of the variables have complex nonlinear relationships, random Variable variables with large shocks are randomly added to the variables, with complex time-varying characteristics and large dynamic mutations. This requires fuzzy systems to have rapid identification decisions and resilience. Therefore, ensuring the rapidity and real-time control is the starting point of our hardware design and control algorithm. After investigation, we use hardware to implement fuzzy control chip method, design FPGA-based fuzzy chip, which can achieve high speed, fast inference, good real-time, easy to modify fuzzy rules and membership function.
Today, in the 1st century, energy conservation and high efficiency have become the most basic requirements for products based on the market. Only by continuously promoting product technology upgrades and breaking through international green technology barriers can we accelerate the process of internationalization of kitchen appliances in China.
1 Multi-variable intelligent fuzzy controller model design The reasoning process is to fuzzify the smoke and humidity deviation and its variation obtained by the lower sensor sampling, and convert the precise input into fuzzy value; derive the conclusion by fuzzy logic inference rule, by condition The degree of satisfaction is introduced to the size of the fuzzy output to realize the anthropomorphic decision; finally, the fuzzy output is converted into an accurate output by defuzzification.
Determination of input and output According to the characteristics of this control system, the fuzzy controller adopts the form of two-input and single-output. That is, the humidity S generated by the stove is collected by the humidity sensor, and the smoke concentration C is collected by the smoke sensor as the input of the fuzzy controller, as shown below: after being blurred, it is converted into a fuzzy set described by the fuzzy control language to establish an input. And the fuzzy control rule between the output, and then according to the control rules, the fuzzy control table is calculated offline, stored in the RAM of the FPGA, and the complex inference operation is simplified to the table look-up operation in the real-time control, which greatly improves the system. responding speed.
The system takes five fuzzy subsets to describe the input humidity S concentration C, where the humidity varies from 0% to 100%, the smoke concentration ranges from 0% to 100%, and the five fuzzy subsets are: NB ( Negative large), NS (negative small), 0 (zero), PS (positive small), PB (positive large). And their domain is divided into 7 levels (-3, -2, -1, 0, 1, 2, 3), and the membership function of the fuzzy subset is chosen as a triangle, in the domain (-3, -2, The membership function distribution of the fuzzy set on -1,0,1,2,3) is as shown.
Similarly, seven fuzzy subsets are used to describe the control output u, and its membership functions such as fuzzy control rules determine the control rules of the fuzzy controller based on a large number of experimental data. According to experience, the following control rules are obtained. Table 1: Table 1 fuzzy rule table. It represents the fuzzy relationship of the fuzzy system: according to the correlation between the input precision quantity and the output precision quantity given by the fuzzy control table, the 3D view in MATLAB is as follows : Defuzzification Because motor control can only be achieved with a certain voltage value, the blur vector must be sharpened (anti-fuzzification).
According to the fuzzy control rule, the center of gravity method is used to derive the fuzzy control table. The center of gravity method is also called the weighted average decision method. The most important thing is to choose the appropriate weighting coefficient. We take the membership function as the weighting coefficient, which is expressed as follows: each time the control quantity given by the fuzzy control algorithm is used, it must be converted into the basic domain that the control object can receive. The scale factor of the output quantity is determined by the following formula. The actual range of variation is that since the basic domain of the control quantity is a continuous real number field, the transformation from the fuzzy set theory domain of the control quantity to the basic theory domain is calculated by the following formula: (m, the control quantity The exact control quantity obtained by any element in the fuzzy set theory domain or the fuzzy set of the control quantity, Yu1 is an exact quantity in the basic quantity of the control quantity, and KD is the scale factor).
1) Reflects the adjustment of the amplitude of the controller output. If the KD is too large, the amplitude of the control signal is increased, so that the response time is shortened, but it is easy to cause oscillation; if the KD is too small, the response speed is slow, and the dynamic response time of the system is long. .
2 Fuzzy controller chip design Fuzzy controller adopts EP2C35 series FPGA as the core. This FPGA has a system clock of up to 15 5.98MHZ, and the integration scale is more than one million gates. Based on FPGA structure, fuzzy logic is realized by hardware method. It can achieve high-accuracy control of the speed, meet the complex time-varying characteristics of the smoke and water vapor random shock in the kitchen environment and the abrupt change of large dynamics to achieve higher system performance.
The four functional modules of the fuzzy control chip in this design are respectively described: fuzzy variable module, fuzzy module, fuzzy inference module and defuzzification module. The advantage of partitioning modules independently in RAM is that it facilitates the comprehensive utilization of the logical structure of the memory area and the type of resources used, as well as the automatic analogy of memory cells to the hardware primitives of the specified device.
The hardware design of this paper adopts Alter's DE2: the smoke concentration and humidity information is input to the fuzzy controller after A/D conversion, and the output voltage is obtained after fuzzy processing to realize the motor speed control, and then the variable is sent as a parameter to the PWM program. In order to output the corresponding voltage value to control the motor speed.
4 Summary This paper uses hardware to implement the fuzzy control chip method, design the fuzzy chip based on FPGA, and also design the four-part function module of the fuzzy control chip, verify, test, compare and comprehensively consider each functional module and hardware structure. For each performance index, choose a reasonable module partitioning method and hardware structure model to achieve the best performance of multiple performance indicators. Using the two variables of humidity and soot to comprehensively analyze the kitchen cooking environment, determine the fan frequency value according to the fuzzy output, improve the frequency conversion control accuracy, realize the real-time stepless frequency conversion speed regulation of the fan speed, and truly achieve the goal of high efficiency, low carbon and environmental protection. .
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