智能小车毕业论文中英文资料外文翻译文献(编辑修改稿)内容摘要:

according the rule table anized by experience. In general, fuzzy control input variables are based on system error E and error change EC, which is similar to PD control. Such control might have a good dynamic characteristic, but the static performance is not satisfactory. Combining fuzzy control and PID control, this would make a system have both flexibilityadaptablity of fuzzy control and high accuracy of PID shows the structure diagram of fuzzyPID control system, in which fuzzy controller is responsible for selecting a different PID parameter to improve the local performance thus increasing over all performance. 中 英文翻译 4 Design of fuzzyPID controller Speed drive motor controller design is similar to the following example for steering gear controller design. Fuzzy controller consists of fuzzification, fuzzyinference and defuzzification, which are based on the knowledge base.[6] Controller input error and error change, output the parameters Kp,Ki,Kd. Suppose the fuzzy set for E is{NB,NM,NS,NO,PO,PS, PM,PB}。 the fuzzy set for EC、 Kp、 Ki and Kd is{NB,NM,NS,ZO,PS,PM,PB}. The linguistic meanings are: NB = negative big, NM = negative middle, NS = negative small, NO = negative zero, ZO = zero, PO = positive zero, PS = positive small, PM = positive middle, PB = positive big. So the membership function curves of fuzzy variables E、 EC、 Kp、 Ki and Kd are shown in the : 中 英文翻译 中 英文翻译 It’s necessary to establish rule table after finishing fuzzification. According the description of rule table, 56 fuzzy conditional statements can be summed, which look like If (E is PB) and (EC is PB) then (Kp is PB) (Ki is ZO) (Kd is PB). See details in . Then, the last step is defuzzification and making a lookup table. During fuzzy control, 中 英文翻译 the lookup table would be embed into the program. Suppose input value is fixed, the corresponding output value would be found in the table. Actually, this would save much puting time, and the control would bee simply. 中 英文翻译 5 Analysis of experimental results 中 英文翻译 Experiment used the steering gear model provides by reference[7]. The simulation circuit were shown in . The usual PID and fuzzy PID algorithm were all simulinked in the Matlab. Responding curves obtained were shown in and . The experimental result show that pared with the usual PID, the responding time of fuzzyPID algorithm is shorter without over swing. The system dynamic performance is improved significantly. 6 Conclusion and outlook This paper provided a design scheme for controlling a smart car, which is proved practically and superlatively though experiments. Unmanned smart car is due to the development of puter technology, pattern recognition and intelligent control technique. Many countries and research groups are doing research in the area. But it’s a plicated system, which involves a number of technologies. So the development of each technology is important, for it would bee the bottleneck of the development of smart car. Device Overview S12XS Family Introduction The new S12XS family of 16bit micro controllers is a patible, reduced version of the S12XE family. These families provide an easy approach to develop mon platforms from lowend to highend applications, minimizing the redesign of software and hardware. Targeted at generic automotive applications and CAN nodes, some typical examples of 中 英文翻译 these applications are: Body Controllers, Occupant Detection, Door Modules, RKE Receivers, Smart Actuators, Lighting Modules and Smart Junction Boxes amongst many S12XS family retains many of the features of the S12XE family including Error Correction Code(ECC) on Flash memory, a separate DataFlash Module for code or data storage, a Frequency Modulated Locked Loop (IPLL) that improves the EMC performance and a fast ATD family delivers 32bit performance with all the advantages and efficiencies of a 16bit MCU while retaining the low cost, power consumption, EMC and codesize efficiency advantages currently enjoyed by users of Freescale’s existing 16bit S12 and S12X MCU families. Like members of other S12X Fam。
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