An Elevator Characterized Group Supervisory Control System. T.Tobita*, A.Fujino*, H.Inaba, K.Yoneda** and T.Ueshima**

Transcription

1 An Elevator Characterized Group Supervisory Control System T.Tobita*, A.Fujino*, H.Inaba, K.Yoneda** and T.Ueshima** 'Hitachi Reserch Laboratory, Hitachi, Ltd Ichige, Katsuta-shi, Ibaraki-ken, 312 Japan **Mito Works Hitachi, Ltd Ichige, katsuta-shi, Ibaraki-ken, 312 Japan Abstract: An elevator group supervisory control system is used to supervise multiple elevators, ensuring that they are operated efficiently. A conventional system aims at reducing average waiting time, although recently requests by elevator users have focused not only on reducing average waiting time, but also on reducing the riding time, the number of passengers, etc. Therefore the authors developed a new elevator group super-visory control system which improves plural control objects according to users' requests. The system effectiveness is confirmed by Introduction An elevator group supervisory control system is used to supervise multiple elevators, ensuring that they are operated efficiently. In a conventional system which adopts the objective of improving running efficiency of elevators and service to passengers, occurrence of hall calls is supervised and a call is assigned to the optimum elevator in consideration of the total service condition, i.e. all calls, thereby reducing the average waiting time. For example, popular hall assignment method were studied in which the predicted waiting times for each floor are estimated by microcomputer, and assignments made to a service elevator at each hall call occurrence.' The hall assignment methods use a minimizing average waiting time method or, minimizing longest waiting time method: incorporating psychological waiting time method? When assigning service elevators by those methods, the supervisory control system does not follow exchanges of traffic flow in building.' The system which improves parameters of the assignment evaluation formula is possible by learning results of the traffic flow, and there are ather systems which apply fuzzy control or knowledge base system: etc. For the above, the control object of the elevator supervisory control system is only a reduction in average waiting time and energy saving. Recently, however, requests by elevator users have focused not only on reducing average waiting time, but also on reducing riding time, number of passengers, etc. Consequently, the authors have developed a new supervisory control system which improves several control objects, according to the users' requests! In this system the method for receiving users' requests is important, and the present system has support equipment for receiving users' requests. 1. Multi-objective control method and structure Usually, an elevator group supervisory control system handles 4-8 elevators. Fig.1 shows the configuration of an elevator group supervisory system. Hall call signals from each hall button are collected by the group supervisory controller, after which the supervisory control system deades the assignment and service elevator used on the hall call signals and car positions. Then the supervisory system transmits the assignment signal and the elevator speed controllers move the elevator car the according to assignment signal. I elevator group supervisocy controller elevator hall 1 car Fig.1 Configuration of an elevator group supervisory control system The assignment evaluation formula for reducing average waiting time is the following: (2) CH2976-9/91/ $ IEEE 1972 I ECON '91

2 where, ti; predicted waiting time of no.i elevator at no.j floor; CDi :estimated value of no.i elevator; h(x): estimation function; CD minimum value of estimated value for each elevator. For the minimum waiting time method, expression (1) becomes: O,=max(t,i) (3) In the conventional system, other term are added to expression (11, for example probability of being a full load, probability of fault of prediction, and a parameter for distributing elevators. The conventional system improves average waiting time by means of preventing an increase of waiting time due to an elevation passing with a full load or fault of prediction. But, nowadays, the elevator supervisory control system has to meet other requests besides reduced waiting time. Fig.2 shows example call assignments complying with requests of owners, and it shows hall call assignments for reductions in waiting time, riding time or number of passengers. Fig.2(a) shows an assignment for reduced waiting time. In this case, elevator (A) is assigned since it is nearest and will be the first to arrive after the hall call. If priority is given to riding time, however, the supervisory controller assigns an elevator (B). Elevator (B) has the least stops from its present position to the first floor. If priority is given to crowding, the supervisory controller assigns elevator (C). However, it is not possible to use expression (1) for assignment of (B) or (C). So a new expression is needed like (4). CD i=h(t,&iifw.ii) (4) where tfi; predicted riding time of no.i elevator at no.j floor; wfi; predicted riding ratio (number of riding passengers / capaaty). A crowding ratio is defined as follows t number of passengers number of starts at 50% of capaaty crowding ratio = number of passengers total number of starts (5) Expression (4) must be optimized according to the users' requests. But it is difficult to solve the problem of assignment analytically, so expression (4) needs to sum the function of each control object, as in expression (6), which determine the optimum estimation expression by CDi=k, - f,(l>+k, * f,(t,i)+k*, * fwr(w,j (6) where k,: waiting time coefficient; k,: riding time coefficient; kwr: riding ratio coefficient; f,: waiting time estimation function; f,: waiting time estimation function, fwr: riding ratio estimation function. A supervisory controller performs different assignments for each building by means of changing the coefficients k, k, k, and estimation functions ftw, f, fw,. Fig.3 shows the effect of crowding parameter on the riding ratio estimation function. The crowding ratios at lunch time and normally are plotted. According to the graph, the effects of coefficients and estimation functions in expression (6) depend on traffic flow, so determining them is difficult. Therefore, in order to determine coefficients and estimate functions for individual supervisory control for each customer and each building, 5 persons 2.persons 4 persons L ( a )Waiting time minimum ( b )Ridig time mimimum (c)number of passengers minimum Fig.2 Example call assignments complying with requests of owners or managers (0: indicates a call stop by a car call or a previous assingment;v: indicates a service elevator) I ECON '

3 a support system is prepared to determine coefficients and estimation functions t o meet users' requests. normal time crowding parameter Fig.3 Effect of crowding parameter 2. Support equipment and its reasoning method Fig.4 show the configuration of a characterized group supervisory control system, which is composed of support equipment and a group supervisory controller. request input ( - I support equipment 1 request input seclion control method decider buildii and elevator Soedliilii i building and elevator specs. input unit fuzzy reasoning The support equipment receives the users' requests and determines the control method (assignment method, assignment evaluation formula, etc). The group supervisory controller assigns the best elevator for each hall call by means of the control method which is determined by the support equipment. The support equipment consists of the request input unit, control method deader and building and elevator specifica-tions input unit. The building and elevator specifications input unit accepts specifications bf the building, and elevators and traffic. The request input unit includes the request input section and fuzzy reasoning section. The former calculates an importance rate by a unity-referenced ratio comparison and an eigen vector method. Target values are determined from building use and time zone. The latter section infers transform coefficients of each request from the building and elevators specifications and traffic. Users' requests are described numerically in expression (7) as a weighted norm: 1,= ZW, I fi -f*& I (7) where 1,: weighted norm; wi: weight coefficient; f,: simulation results of control; fi,; goal value of control object. The weight coefficient is described as expression (8): w.=q i * Pi (8) where a,: importance value; fi,: transform coefficients. Fig.5 describes the request input method of the request input section. Users compare control objects' importance and set cursors to their preference. The request input section uses a matrix (8) form for the cursors positions, and calculates importance values as eigen values. highly weighted equally weighted hqhiy weighted waiting 1 imea- t riding time waiting 1 imea- t crowding ratio 1-1 riding time crowding ratio Fig.5 Example of input method of owners' or managers' requests Fig.6 desaibes fuzzy reasoning for an example case in an office building during lunch time, where the riding ratio from the lobby is 20% and the traffic is 15 persons/(5min. in each elevator). The fuzzy rules are the following. Fig.4 Configuration of an elevator characterized group supervisory control system Rule 1 IF riding ratio from lobby is small THEN a transform coefficient of number of passengers is small medium I ECON '91

4 Rule 2 IF traffic is medium THEN a transform coefficient of number of passengers is small. For rule 1, the grade is calculated from the fact the "riding ratio from lobby is 20%" and the assumed the membership function (Fig.6(a)). Then the conclusion part of the membership function (Figd(b1) is transformed by the grade. Rule 2 (Figd(d)) is transformed in the same manner. Then the result's membership function (Fig.6(e)) is calculated from the fuzzy logic OR (Fig.6(b) and Fig.6(d)). Next the center of gravity of Fig.6(e) is computed, and the horizontal axis value of the center of the gravity value is taken as the result. In this case, the result is 0.3. $h Rule 1 riing ratio from lranslorm coeflidenl,.o kbby is small... a riding ratio from kbby (%) (a) Rule 2. traffic is medium The control method deduction unit is an expert system which uses a production system. This unit infer control methods which seem to suit users' requests from values of the request table and data of the environment/traffic data base. The simulator unit simulates elevator movements and calculates simulation results for each control method. Next, the multi-target decision making unit calculates weighted norms (7) for evaluation of the control method from the simulation results and the users' request table, and selects the best control method which has a minimum weighted norm. Then this best control method is shown on the display along with its simulation results. The elevator operator looks at this display and decides whether it is satisfactory or not. If unsatisfactory, the above procedure is repeated. If good, this control method is sent to the elevator supervisory controller. Accordingly the operator deades the favored control method using the support equipment. Fig3 describes simulation results for the support equipment. 0.0.C i:ni (person/(5 min * each elevator)) (C) 0.0 L transform coeffiient (d) Fig.6 An example of fuzzy reasoning The request input unit make a users' request table with importance rates, transform coefficients and target value as described above. The control method deader decides the control method from the request table and environment traffic data-base by using an expert system and elevator simulator. Fig.7 shows the knowledge base structure of deciding knowledge base. fl/ rules of control method depend on traflic flow etc. NI= d mntrd method depend on \I users' requesl traffic flow.: GT model IF waiting time is important THEN riding coefficient=0.2 U other rules traffic flow: IT model IF waiting time is important THEN riding coefficient=o.b... (b) A case attaching importance to number of passengers Fig.7 Knowledge base structure of control method deduction Fig. 8 Simulation results for support equipment I ECON '

5 The supervisory controller operates each elevator by the chosen control method. Traffic flow in a building sometimes changes, so control parameters deviate from their best values. Then the supervisory controller learns the traffic flow, and simulates the actual flow using various control parameter combinations. The supervisory controller must carry out a long simulation sequence and it can follow traffic flow changes for 1 or 2 day periods $1 4 E12 E1 0 L ; 8 a E Simulation results Figs.9 and 10 show results of characterized control for the simulation conditions in table 1. Fig.9 shows waiting time distributions. Fig.S(a) uses the criterion of a satisfactory waiting time (like a conventional control method), and Fig.S(b) uses a satisfactory number of passengers. Accordingly to the plots, the former is 3 seconds better than the latter. Table 1 Simulation conditions simulation conditions I I buildinguse I oftice building number of sevice floors 1 15 floors elevator speed capacity traffic 150 mlmin 140 pemw5min I 50r 3 ~&$qim~~s)loo 120 ' waiting time (s) (a) A case attaching imponanto waiting time to number-of passengers Fig.9 Waiting time distributions I ' number of starts (a) A case attaching importance to waiting time E 16 p14 12 El0 0 & 8 a s6 = 4 2 n " number of starts (b) A case attaching importance to number of passengers Fig.10 Change in number of passengers Conclusion A new elevator supervisory control system was developed which improves plural control parameters, according to users' requests. A method was devised which uses from users' requests for the control method employing fuzzy theory and expert system. The effectiveness of the control system was confirmed by References [ 1lT.Yuminaka and T.Iwasaka, "Forecasting Control (b) A case attaching impoflance System for Elevators - Development of m/ic System", The Hitachi Hyoron, vo1.54, no.12, pp67-73,1972 [2]K.Hirasawa et al, "Hall Call Assignment in Elevator Supervisory Control",The Trans. of The Institute of Fig-10 shows change in number of passengers. Electrical Engineers of Japan, vo1.99-c, no.2, ppl-6,1979 Fig.lO(a) uses the criterion of a waiting time, and [3]T.Sato et al, "Solid State Control System for Higher fig.lo(b) uses a number of passengers. In these Plots Grade Elevator",Mitubishi Gihou, vo1.53, no.3,1979 the horizontal is the number of elevator start times, [4]K.kurosawa et al,"intelligent and Supervisory and the vertical axis is the number of passengers. From Control for Elevator Group (Part 11: Leaning Logic), Fig.lO(a), the elevator starts 23 times with more than 10 The Trans. of IPSJ, vo1.28,n0.3, pp ,1987 persons (half the loading capacity), and it is completely [S]S.Hikita and K.Komaya, "A New Elevator filled 3 times. On the other hand, in Fig.lO(b), the elevator GroupSupervisory Control System Using Fuzzy starts 12 times with over half of the loading capacity, and Rule-Base", The Trans. of The Society of Instrument there are no starts when it is full. These simulations demonstrate it is possible to operate plural elevators and Control Engineers, vo1.25, no.1, pp99-104,1989 [6]Y.Sakai et a1,"multi objective Elevator Supervisory according to the users' requests. Group Control System with Artificial Intelligence", The Hitachi Hyoron, vo1.71, no.5, pp , I ECON '91