Research of Dispatching Method in Elevator Group Control System Based on Traffic Mode Identify

Elevator group control system (EGCS) with multi-objective, stochastic and nonlinear characteristics is a complex optimization system. It is hard to describe EGCS with exact mathematic model and to increase the capability of the system with traditional control method. After analyzing characteristic of typical traffic mode of elevator. This paper proposed a new simulation platform of an elevator group control system implemented in C# using the fuzzy-neural network technology. The comprehensive evaluation function of traffic signal is established and the right heavy of every evaluation factor (waiting time, riding time, energy consume, crowd degree) is studied by the neural network, so the elevator is dispatched optimally. The result of simulation shows that this method realizes reasonable elevator dispatching under various passenger traffic conditions and indicates the validity of this method.KeywordsEGCSFuzzy Neural Network

From the commercial point of view, in tall buildings with multiple lifts or elevators, owners face problems like waste of space, more managing time and money. People travelling by lifts face problem of high waiting time. Elevator Group Control System (EGCS) manages several elevators in tall buildings for efficient carrying of passengers. Waiting time of people and car are major factors affecting the performance of EGCS. EGCS organizes elevators to reduce the performance evaluation measures. But, it is hard to achieve all measures. Hence, here we deal with the improvement of performance by reducing waiting time of the passengers. The proposed system introduces dynamic scheduling algorithm to schedule elevators dynamically. The proposed system uses fuzzy logic to schedule and dispatch the elevators. The main motive of the research work is to diminish the passenger’s waiting time and car allocation time.

An elevator system consists of an elevator group control system (EGCS) and a micro-processed sub-system that implements a local control system (LCS) for each elevator. This paper describes the validation of five dispatching algorithms for an elevator system that was implemented on Spartan 3 FGPA based boards in an integrated approach. The focus was on area reduction and performance improvement. The overall system is composed of several LCS, which implement the dispatching algorithms, an RS485 based network and a virtual environment called virtual elevator interface system (VEI). The latter includes a simulator/monitoring system and an EGCS based on fuzzy logic (FEGCS). The FEGCS runs on a PC and, under different traffic situations, determines the best algorithm to be run in each LCS reducing the user waiting time and the power consumption. The novelty of this approach is that the LCSs are capable to run several dispatching algorithms that are suitable for different passenger traffic situations, while the FEGCS only must determine the best algorithm to be run in each LCS. The VEI allows the designer to test and validate in a flexible way the algorithm performance for different traffic situations.

Aiming at the problem of uncertainty for information collection in traditional elevator group control system (EGCS), this study proposes a method about dispatching approach optimization for a new type of EGCS with destination floor guidance. The characteristic of destination floor guidance is information admixture of hall call registration and destination selection. EGCS with destination floor guidance can attain nearly complete and reliable information aggregation in advance to guide the passenger flow effectively. In order to realize the accurate prediction of input parameters, all of the hall call registrations are classified to four groups. Based on it, the optimized prediction algorithms of evaluation items are established, including the average waiting time of passengers (AWT), the average riding time of passengers (ART), rate of waiting long time of passengers (LWR), and riding power consumption (RPC). Especially, Fuzzy Neural Network (FNN) is applied to optimize multiple objectives for realizing reasonable dispatching approach. The simulated results show that the algorithm applied in the paper improves the evaluation items by 10% at least as compared with the traditional algorithm and it indicates an excellent application to enhance the holistic capability of EGCS.

This paper presents a new group controller's adaptation mechanism with fuzzy logic for elevator group control system (EGCS) applications. Instead of depending heavily on the predicted passenger traffic pattern for adaptation, the fuzzy logic group controller (FLGC) adjusts itself to suit the system's environment through a self-tuning scheme. The average-waiting-time data that reflect the measured performance results of the EGCS are exploited to tune the membership functions of the input parameters and to modify the fuzzy rule sets employed by the FLGC, thus leading to better controller's performance and improved EGCS service quality. Computer simulation shows satisfactory improvements made in passenger waiting time and passenger riding time as compared to the performance of the conventional group controllers in three traffic peaks. The hardware implementation of the self-tuning FLGC on a TMS320C6416T digital signal processor further demonstrates its practicality.

The essential problem of elevator group control is how to manage the operation of the elevators properly and then to reach the top efficiency; a high efficiency means a shorter waiting time and running time, less crowding, and less power consumption. A good algorithm should be able to select a proper control policy according to the traffic pattern. This paper proposes a new multiobjective elevator group control algorithm based on fuzzy logic, and describes its structure and realization in detail. The algorithm can change the power vector of the multiobjectives automatically. The effectiveness of the algorithm is confirmed by computation result.

The conventional elevator group control system is based on the button as a response and where to dispatch an elevator car. There is one big problem that when system dispatches the elevator to that button responding floor, but no passenger there and causing a waste of time and energy. In this paper, we present a novel intelligent elevator group control algorithm based on corridor passenger detection and tracking. In the above proposed system, after the corridor passengers are detected using binocular-cameras, Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. At same time, a novel intelligent elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation condition.

Group elevator scheduling (GES) problem is an optimization problem. In this work, a robust optimization scheduling method is proposed to solve elevator group control system problem with passenger traffic flow uncertainty. An uncertain optimization model of elevator group scheduling is set up based on considering both energy consumption and waiting time. When considering the uncertainty of passenger traffic flow, the model is an uncertain optimization problem. It is tuned into its robust counterpart (RC) based on robust optimization theory. The simulation experiment presents the proposed scheduling method's scheduling performance.

Genetic programming network (GNP), a graph-based evolutionary method, has been proposed as an extension of genetic algorithm (GA) and genetic programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with ant colony optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to elevator group supervisory control systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

A liquid metal cooled reactor coupled by mixed gas of helium and xenon closed Brayton cycle is designed for space exploration purpose. The design and dynamics of the space reactor differ from those of traditional reactors. Therefore, it is necessary to develop a control system design and simulation platform to investigate its dynamic behavior, design and evaluate the control system. Two system codes are adopted and coupled to develop the platform. The reactor core system code is used to calculate the reactor kinetics and thermal-hydraulics of the coolant. The energy conversion system code is to compute the Brayton cycle and the heat exchanger between the primary and secondary loops. 3KeyMaster simulation platform is used to load the two system codes by an interface program through dynamic link library. The data exchange and time synchronization between 3KeyMaster simulation platform and two system code are performed to realize the coupling of two system code. By introducing typical disturbances, the calculation results are compared with those from the system codes to verify the interface program. The input and output parameters can be visualized by model components in 3KeyMaster simulation platform to analyze the dynamic behaviors, which is useful for the subsequent control system design.

An Elevator Group Control System (EGCS) assigns an elevator of a group to each passenger transportation request by solving a snapshot optimization problem, the Elevator Dispatching Problem (EDP). In the destination control, passengers register their destination floors in the elevator lobbies, after which the EGCS completes the assignment at once and is not allowed to change it later. Therefore, the EDP is formulated as a stochastic optimal control problem, where uncertain future passenger arrivals are modeled by a Poisson and a geometric Poisson process. The EDP is considered as a certainty equivalent controller in which the uncertain quantities are replaced by their expected values, and as a robust controller in which they take multiple values according to risk scenarios. Numerical experiments show that the expectations do not accurately predict EDP variables. The modeling with the geometric Poisson process results in better forecasting accuracy than with the Poisson process and many scenarios that closely match the realizations of the variables. Hence, the scenarios can be used as a basis for a robust EDP which simultaneously minimizes a passenger service quality criterion and its variation due to uncertain demand.

We have developed a high-performance elevator group control system EJ-1000FN with a performance tuning function, which employs a fuzzy neural network as a performance forecasting model of the elevator system. The performance tuning function utilizes the forecasting model in order to search the optimal control parameters which give the best system performance in the present traffic situation. >

Elevator traffic flow is fundamental in elevator group control systems. Accurate elevator traffic flow model is crucial to the elevator system configuration and the dispatching of elevator group control systems, especially to the elevator system configuration in yet-to-be-commissioned new building. Based on dynamic passenger distribution of building, importing the overloading modulus of the passenger distribution capability of building, a new elevator traffic flow model is found and has a new form of origin vector and origin-destination matrix. The traffic flow data which based on the new traffic flow model shows that the new model is practical and useful. Simulation test also shows that the elevator traffic flow based on new model is more reasonable than the traditional elevator traffic flow model.

Elevator Group Control System (EGCS) has a vital role in the vertical transport operations in tall buildings. In this research work, a hybrid approach for the dynamic-scheduling of elevators(lifts) in EGCS to handle the down-peak and up-peak traffic conditions is proposed. The principal motive of this research is to minimize the factors viz. the passengers traveling time, average waiting time, and the passenger's inconvenience. The administrator of EGCS can set timeslots (which may be overlapped) both day-wise and date-wise in the elevator-control-information, which will be used by the EGCS The EGCS takes into account the administrator's elevator-control-information regarding various timeslots, both day-wise and date-wise, and estimates the amount of traffic timewise. The administrator can also indicate which elevators are in-service and out of service for proactive and reactive risk management. The anticipated hybrid approach can tackle several scenarios or problems while minimizing the factors mentioned above.

Scheduling of Elevator Group Control System (EGCS) is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and ...