1、外文作者：Deying Gu, Dongmei Yan文献出处：Applied Mechanics and Materials Vols 55-57 （2017） pp 1218-1222 （如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做）英文1911单词，11902字符（字符就是印刷符），中文3081汉字。Elevator Group Control System Based on Fuzzy Single-chipComputerKeywords: Elevator Group Control System, Fuzzy Single-chip Computer, control r
2、ules, Fuzzy inferenceAbstract: What the group control of the elevator traffic system deal with is a complex, random,multi-objective, nonlinear, uncertain decision-making problem. Good effect of controlling elevators cant be achieved with traditional control methods. Elevator group control system bas
3、ed on fuzzy single chip computer is introduced in this paper. Fuzzy inference can be carried out based on control rules that are programmed based experts experience. Then elevators are allocated in the light of inference result in order to achieve ideal control effect.IntroductionElevator group cont
4、rol system is a complex, random, multi-objective, non-linear, uncertain decision-making problem, because there are a lot of uncertainties in elevator group control system, such as numbers of passengers in each floor, the floor each passenger will arrive, calling signal out of elevator and traffic si
5、tuation.Zong Qun etc. proposed a new multi-objective elevator group control algorithm based on fuzzy logic, describes its structure and realization in detail. The algorithm can change the power vector of the multiple objectives automatically .The effectiveness of the algorithm is confirmed by comput
6、ation result 1.Elevator Group Control System Based on Fuzzy Single-chip Computer is introduced in this paper against shortcomings of traditional elevator group control system. It aims at decreasing waiting time and energy loss. It optimizes the elevator group harmoniously 2-6.Fuzzy setThe concept Fu
7、zzy set was first presented by America learner called Zadel. Membership function was introduced to study fuzzy rule. Later, fuzzy set was applied into control field. Then fuzzy control theory was formed. Fuzzy control theory is a new kind of computer control approaches based on mathematical knowledg
8、e including fuzzy set, fuzzy language variant and fuzzy inference. Fuzzy control is mainly implanted with expertise of operators. Structure of fuzzy control system is shown in Fig.l.Fig. 1 structure of fuzzy control systemFuzzy logic starts with the concept of a fuzzy set. A fuzzy set is a set witho
9、ut a clearly defined boundary. It can contain elements with only a partial degree of membership. To understand what a fuzzy set is, first consider what is meant by what we might call a classical set. A classical set is a container that wholly includes or wholly excludes any given element.A membershi
10、p function （MF） is a curve that defines how each point in the input space is mapped to a membership value between 0 and 1. One of the most commonly used examples of a fuzzy set is the temperature. In this case the universe of discourse is temperature, say from 15 to 37 degree Celsius, and the word h
11、igh would correspond to a curve that defines the degree to which temperature is high. If the set of is given the well-defined boundary of a classical set, we might say temperature higher than 30 degree Celsius are considered high.It may make sense to consider the set of all real numbers lower than 3
12、0 degree Celsius because numbers belong on an abstract plane, but when we want to talk about real temperature; it is unreasonable to call the substances temperature （29 degree Celsius） is low and another substances9 temperature （29 degree Celsius） is high, which they differ in one degree Celsius. Me
13、mbership function of Mamdani type is shown in Fig2.Fig2. Membership function of Mamdani typeFuzzy logic lets one describe complex systems using expertise knowledge and experience in simple English-like rules. It does not require any system modeling or complex math equations governing the relationshi
14、p between inputs and outputs. Fuzzy rules are very easy to learn and use. It typically takes only a few rules to describe systems that may require several of lines of conventional software. As a result, Fuzzy Logic significantly simplifies design complexity.Most real life physical systems are actual
15、ly non-linear systems. Conventional design approaches use different approximation methods to handle non-linearity. A linear approximation technique is relatively simple, however it tends to limit control performance and may be costly to implement in certain applications. A piecewise linear technique
16、 works better, although multiple inputs exist; a lookup table may be impractical or very costly to implement due to its large memory requirements.Fuzzy provides an alternative solution to non-linear control because it is closer to the real world. Nonlinearity is handled by rules, membership function
17、s, and the inference process which results in improved performance, simpler implementation, and reduced design costs.By using a more natural rule-based approach which is closer to the real world, Fuzzy control can offer a superior performance and a better trade-off between system robustness and sens
18、itivity, which results into handling non-linear control better than traditional methods.The concepts used in elevators group control system are almost fuzzy, such as passenger waiting time, passengers flow volume, passengers in the carand elevators response time for calling, which cant be defined pr
19、ecisely with quantitative limits and also hardly dealt with common logic rules. Thus, elevators group control system can be manipulated with fuzzy inference that is based on experts experiences.Structure of the SystemFunctions Unit Structure of the system is shown in Fig3.1） Sampling unit: Two kinds
20、 of information can be achieved in sampling unit, which are called calling information relating elevators starting times and passengers location and Information of present elevators state including degree of congestion in elevators, elevators location and traffic direction. All information in sampli
21、ng unit is transmitted to traffic flow pattern recognition unit in order to analyze traffic states.2）Traffic flow Pattern recognition unit: Elevators are operated in light of four modes as follows:Up Peak mode, down Peak mode, normal traffic flow mode and idle mode. It is necessary to allocate more
22、elevators to the ground floor because many passengers require being conveyed form ground floor to other floors in up peak mode.Fig 3 structure of the systemDown peak mode is characterized by which many passengers require being conveyed to ground floor during the interval of being off-duty. Normal pa
23、ssenger flow volume is nearly equal to down passenger flow volume. Calling signals are created continually and elevators load is approximate. Idle mode come about is usually passenger flow volume is small during holiday or night. Idle mode is selected when elevators load is far less tan rated load.T
24、here are different requirements in different mode. For example, waiting time is decisive in up or down peak hour for selecting elevators and energy consumption should be focused on in the idle mode. In moral mode, three objectives must be considered simultaneously. So, weighting of three objectives
25、in the system varies with different modes.3） Fuzzy inference unit: Information （calling information, present elevators state） that is collected in sampling unit will be dealt with in fuzzy single-chip computer in which they are fuzzified and inference is made with fuzzy rule then defuzzified to be t
26、ransmitted to Elevators Allocation unit.4） Elevators Allocation unit:The information being sent form fuzzy single-chip computer, sampling unit and traffic flow Pattern recognition unit are processed with evaluating function in order to create control signals to dispatch elevators reasonably .Fuzzy s
27、ingle-chip computer- NLX230NLX230 with 16 fuzzifiers and 64 rules is an 8-bit microcontroller utilizing fuzzy logic at 30 million rules per second. There are five parts in the NLX230 （diagram is shown in fig4）. Initialization of the chip includes loading bitmap for defining Membership Function, outp
28、ut range and 64 rules at most. NLX230 works differently from general MCU in that it computes and optimizes the output according to fuzzy logic theory-it control output by parallel operation.There are two different operational modes for NLX230. They are independent and dependent manner, which is deci
29、ded by level at one of pin called M/Sn. If level at M/Sn is equal to 1, the chip works under independent mode. When level at M/Sn is equal to 0, the chip works under dependent mode. In independent mode, external EEPROM can be load data automatically, which are written into EEPROM based on debugged m
30、embership and fuzzy rule by development system-AD230. In dependent mode, data is loaded by external logic circuits such as CPU etc.Fuzzy Inference of NLX230 is base on Manidani, but it is particular in structure of membership function and control rules.Fig4 Diagram of NLX230,s structureControl Rules
31、 of NX230Control rules in NLX230 is not easy understand because they are expressed based on data in rules register, function register, initialization register and type of membership register etc. Thus, control rules are introduced by development system of NX230. Development system of NX230 is AD230
32、in which control rules described in text can be changed in to date corresponding to relating registers of NLX230.ArithmeticFuzzy controllers are designed in the system with inputs are waiting time, passengers number and stops. The symbol （Wl, W2, W3） represents weighting of all elevators based on counting result in Traffi
