Idle control essentially changes the cylinder charge by controlling the opening of the idle bypass valve in the throttle body when the throttle is fully closed, thereby stabilizing the idle speed at the target value, by the intake manifold, throttle, stepper Motor idle bypass valve, electronic control unit ECU and sensor. The relationship between the opening of the idle bypass valve and the output of the stepper motor of the actuator is approximated as a straight line, so its mathematical model can be approximated by a proportional link [5], ie G(s)=100/128(%/step)3 fuzzy controller Designing fuzzy control is a computer numerical control technology based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning. This paper uses a two-dimensional fuzzy controller [6], as shown. The controller uses the speed deviation e and the rate of change of the speed deviation as the control input, and uses the step of the idle bypass valve stepper motor as the system output control amount. Fuzzy controller structure block 3.1 Input and output variable fuzzification According to the idle speed control test observation result, define the input control quantity e and the output control quantity u to take 7 fuzzy subsets: NL (negative large), NM (negative medium), NS (negative small), ZR (zero), PS (positive small), PM (middle), and PL (positive); Defining the input control quantity de takes eight. Fuzzy subsets: NL (negatively large), NM (negative), NS (negative small), NZ (zero positive), PZ (zero negative), PS (positive small), PM (middle), and PL (positive). At the same time, the change range of e is set to [-250,250] r/min, and the change range of de is set to [-100, 100] r/min. Exceeding is treated as a boundary value. For the control variables e, de, and u, the domain of jurisdiction is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}. Degree functions are as shown. The principle of establishing the fuzzy control law of the fuzzy control rule is to ensure that the input of the fuzzy controller can make the dynamic and static characteristics of the system output response reach the optimal value. For example, when the speed deviation is negative and the rate of change of the speed deviation is negative, there is a tendency for the engine speed to increase further, and then the change of the controller should take a negative value to quickly reduce its speed. Because the input control quantity e takes 7 fuzzy subsets and de takes 8 fuzzy subsets, the fuzzy control rule base contains 7*8=56 rules. It is a fuzzy control rule established based on actual operation experience and expert knowledge. Fuzzy inference Fuzzy inference is the core of fuzzy control. Based on the fuzzy input control variables e, de and fuzzy control rules, it is inferred according to a well-defined reasoning method, so as to obtain fuzzy rules for fuzzy output u. If e is Ai ( i=1,2,...,n), and de is Bi, then u is Ci, and the degree of membership of inferred result C′ is uC′(ui)=∨ni=1[uAi(e)∧uBi(de) ) ∧uCi(ui)] where the method of taking ∧(and) is min and the method of taking ∨(or) is max. The function of clearness and clarity is to transform the output (fuzzy quantity) obtained by fuzzy reasoning into a clear quantity that is actually used for control. The clearness of the blur is calculated using the area center-of-gravity method. The calculation formula is u=∑ni=1uiuC′(ui)/∑ni=1uC′(ui). The above fuzzy inference uses Mamdani's MIX-MAX-center-of-gravity method, which is essentially a weighted average. Method, calculation is done automatically by Simulink. The establishment of fuzzy PID control system The fuzzy PID control system is realized by MATLAB/Simulink tools. The simulation frame is as shown. Fuzzy inference uses the FuzzyLog-icToolbox module. FISEditor and ruleEditor are used to establish the system input and output variable membership functions and fuzzy control law library. Then the FIS file is called by the S-Function module. The digital PID controller is directly implemented using the DiscretePIDController module. According to the engineering setting method, the best parameters of the module near the system operating point are obtained. Conclusion The simulation results show that the fuzzy PID controller enhances the system's ability to adjust the idle speed regulation, has a small overshoot, and has a fast response speed, greatly improving the dynamic and static performance of the system, and has obvious advantages in modern natural gas engine idle control. The car seat has the best quality of carbon fiber infrared heating system, which means that it offers super-fast heating to keep the seat warm. Second, to this, the unit allows for constant adjustment of the temperature to suit your needs. Heated Seat Covers,Seat Heated In Car,Carbon Fiber Heating Pads,Car Cushion Seat Heating JiLin Province Debang Auto Electric Co.,Ltd. , https://www.debangcarseatheating.com
