Research on elevator group control system based on multi-sensor

1 Problem Statement One of the research directions of elevator group control system (EGCS) is how to use optimized control strategies to coordinate the operation of multiple elevators and improve the operation efficiency and service quality of elevators. The improvement of operation efficiency also means reducing the waiting time of passengers and energy consumption, which directly affects the quality of service. This will inevitably consider the issue of ride comfort. Studies have found that there are many factors that affect ride comfort: acceleration and its changes during start/stop, vibration of the car, waiting time, running time, crowding in the car, the number of stops and starts during the arrival at the destination floor, and the lighting and decoration in the car, etc. The waiting time, running time, crowding and the number of stops during operation are determined by the group control scheduling algorithm. Therefore, how to improve ride comfort in the elevator group control scheduling strategy is a major issue of this study. Since Mitsubishi first applied fuzzy logic to elevator systems, intelligent control methods have become the most studied scheduling algorithms in elevator group control systems: for example, the fuzzy neural network elevator scheduling method can make full use of the learning ability and information processing ability of neural networks, and continuously adjust network parameters through learning to achieve optimal control; the scheduling method based on genetic algorithms determines the control target according to customer requirements and uses genetic algorithms to optimize the parameters of the scheduling evaluation function based on the predicted call generation and distribution data [2-3]. However, these methods only consider technical factors and ignore riding comfort. In addition, due to the lack of understanding of the number of passengers, when the number of people waiting for the elevator on a certain floor is so large that one elevator cannot bear it, the next elevator will be allocated only after the elevator has transported some passengers away, which prolongs the waiting time and causes multiple elevators to be idle, while passengers cannot get enough elevators. To solve the above problems, the design is carried out from the following two aspects: by configuring multiple sensors to obtain more passenger information, and starting from riding comfort, a task allocation algorithm based on fuzzy reasoning is adopted to reduce congestion, shorten waiting time, and reduce the number of stops. 2 Structural design of elevator control system The structure of the improved elevator control system is shown in Figure 1. It includes five parts: elevator dispatch controller, main controller, car controller, command board and call board. The main controller mainly realizes the functions of elevator operation control, door opening and closing control and command processing. First, in the learning state, the main controller obtains the height information of the shaft and the position information of the elevator by recording the pulses input by the rotary encoder and the sensors on each floor. In the running state after the learning is completed, the main controller refers to the recorded data to send a control signal to the inverter to control the operation of the elevator and respond to the user's instructions and calls. The car controller receives the instructions from the command board and the signals sent by the sensors in the car, and controls the equipment in the car according to the instructions of the main controller and the user. The elevator dispatch controller is used to receive the elevator call signal and passenger information sent by the call board. The command board and call board are used to receive the user's instructions, and at the same time, feedback the elevator status information to the user to realize human-computer dialogue.

In the system, both communication signals and control signals are implemented in digital form, which improves control accuracy and simplifies software design. CAN bus is used for multi-master communication between controllers, which is fast, stable and has a simple interface.

3 Sensors of elevator group control system

The outbound call button in the lobby and the inbound call button in the car can be regarded as two sensors, which are used to receive the elevator call request from the passengers in the lobby and the destination instruction from the passengers in the car, and then send these two parts of information to the elevator dispatch controller as two input signals of the dispatch unit.

The image sensor located in the lobby of each floor is used to measure the number of people waiting to use the elevator on that floor. The image sensor consists of a camera and a microprocessor connected to it. In order to reduce the amount of calculation and reduce the interference of useless information, the image sensor starts working and calculates the number of people waiting for the elevator only when the call button in the lobby of that floor is pressed. When only the up or down button is pressed, the number of people waiting for the elevator calculated by the image sensor is the number of passengers going up or down; if both buttons are pressed, the number of passengers going up and down is estimated based on the position of the floor relative to the entire building, that is: assuming that the total number of floors in a building is N, and both the up and down call buttons in the lobby of the Mth floor are pressed, the image sensor on this floor calculates that the number of people waiting for the elevator is X, then it can be assumed that the number of passengers going up is

Where floor (x) is the largest integer not greater than x.

By adding the setting of this sensor, the weight sensor located in the shaft determines the number of passengers in the car by weighing, taking the average weight of the passengers and retaining a certain margin of 70 d/person. If the total weight of the passengers in the car is W, the number of passengers in the car is

n = W/70 ,

and the number of passengers that the car can still bear can be calculated from this, which serves as the basis for whether the elevator responds to the passenger's call.

The speed sensor measures the current running speed of each elevator, because the speed command curve of the elevator is often designed based on the distance principle when decelerating to the leveling floor to ensure the comfort of the elevator, the leveling accuracy and facilitate on-site adjustment. At the same time, the position of the elevator car is determined by the position sensor by calculating the moving pulses generated by the pulse encoder, and the synchronization floor is calculated according to the floor height stored in the memory. Therefore, the elevator dispatch controller determines the nearest call floor that the elevator can respond to based on this speed and position. Figure 2 shows the sensor configuration of the elevator group control system.

4 Task allocation algorithm design The following control parameters are selected based on the data obtained by each sensor:

① Waiting time (WT): the time from when the passenger presses the call button to when he enters the elevator;
② Riding time (RT): the time from when the passenger enters the elevator to when he reaches the destination floor;
③ Number of passengers (RN): the number of passengers in the car, that is, the degree of crowding;
④ Number of passengers waiting for the elevator (WN): the number of passengers waiting for the elevator in the lobby;
⑤ Number of stops (SN): the number of stops from when the passenger enters the elevator to when he reaches the destination floor;
⑥ Relative distance (RD): the number of floors that the elevator passes from the current floor to the destination floor.

Then, the elevator dispatch controller calculates the control parameters for each elevator to respond to a certain call. Because the response to the elevator call request is a multi-input-single-output process. Considering the difficulty of multi-input-single-output processing and the many uncertainties in the system, the author adopts a multi-rule weighted fuzzy algorithm to process it. For each input and output of the system, the fuzzy reasoning rules are established in a one-dimensional space. The final output is obtained by taking the weighted sum of all rule outputs. This form can well establish and modify fuzzy rules, and has greater flexibility than traditional fuzzy reasoning methods.

For a multi-input-single-output system, the model description is as follows:

Taking passenger comfort as the control target, the weights of each parameter are selected as follows:

The membership of the above parameters is represented by trapezoidal membership function and triangular membership function. Taking the number of passengers and output function as an example, its membership function is shown in Figure 3.

For each single-input-single-output system, calculate the sum of its weighted values, and then obtain the control parameters of the elevator, which can be expressed as follows

For n elevators, there are n output values ​​Y1, Y2, ..., Yn, which are the control parameters of each elevator. Select max (Yi) {i = 1, 2, ..., n} as the elevator that responds to the call.

5 Conclusion

The multi-sensor elevator group control system proposed in this paper obtains passenger information on each floor and in the car by configuring multiple sensors, and predicts the purpose of the waiting passengers, providing a basis for control decisions. At the same time, the system takes passenger comfort as the control target, adopts a multi-rule weighted fuzzy algorithm, reasonably allocates elevators, shortens passenger waiting time, reduces the congestion in the car, and reduces the number of stops, making the elevator system more humane, meeting people's higher requirements for the elevator system, and has a good application prospect.