A Novel Intelligent Elevator Group Control Algorithm Based on Corridor Passenger Detection and Tracking

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.

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.

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

In practice, the intelligent PID control algorithm which is an improved algorithm based on the classical PID control was put forward by many researchers to resolve some control problems, such as time-delay, lag and nonlinear. In this paper, an intelligent PID control algorithm is considered and applied to an internal combustion control system. Then, the semi-physical simulation platform based on PLC (Programmable Logic Controller) is set up, where the internal combustion engine is simulated by using simulation model and the intelligent PID controller is realized by using PLC. The results show that the designed control system is stable, and high accuracy and short response time are obtained. Also, the results show the advantage of the presented intelligent PID control algorithm comparing with the classical PID control algorithm.

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.

For the non-determinacy of transportation characteristics of the elevator group control scheduling process, an elevator group control system model is established, which including the shortest waiting time, the shortest by ladder time and the least energy consumption. Along with a scheduling strategy of elevator group control based on pseudo differential feedback (PDF) control which can content the requirement of an adaptable elevator group control performance is presented. The algorithm will be improved to achieve better dispatching result and simulation experiment effect finally. The simulation results show that this method compared with other algorithms in elevator group control scheduling performance and traffic model's adaptability has greatly improved.

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Dynamic performance is an important index of high-speed on/off valve in the digital hydraulic field. Optimizing the control algorithm is an effective method to improve the dynamic performance of existing high-speed on/off valves. Usually, a high-voltage excitation method is used to realize fast switching of the high-speed on/off valve, but this also leads to a high temperature rise, large energy consumption, and long delay times when switching off. In this article, an intelligent pulse-width modulation control algorithm is proposed to improve dynamic performance while minimizing temperature rise and energy consumption of high-speed on/off valves. A high-frequency voltage source with full positive, low positive, full negative, and zero duty ratios is applied to drive the high-speed on/off valve in opening, maintaining opened, closing and maintaining closed states, respectively. The adaptive switching of these four duty ratios is realized by real-time current feedback which can estimate the operational state of the high-speed on/off valve, so that the current in the loop is always in the optimal state. A mathematical model of the high-speed on/off valve is built, and based on it, the theoretical formulas for the delay time, temperature rise, and energy consumption of the high-speed on/off valve are deduced. Simulation results indicate that the intelligent pulse-width modulation control algorithm has a better performance in dynamic characteristics than normal high-voltage excitation methods. Moreover, experimental results from a test system indicate that the intelligent pulse-width modulation control has the potential to shorten the opening time by 23.6% and closing time by 17.0%, which confirms the simulation results. Results also showed the ability to extend the controllable working duty ratio by 83.3%, to reduce temperature rise by 69.9%, and reduce energy consumption by 88.8% when compared to the matched controller of the tested high-speed on/off valve. This article presents an effective and practical method to improve the performance of high-speed on/off valves.

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility.

Elevator group control system (EGCS) with multi-objective, stochastic and nonlinear characteristics is a complex optimization system. 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 result of simulation shows that this method realizes reasonable elevator dispatching under various passenger traffic conditions and indicates the validity of this method.

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.

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.

In this paper, the intelligent internal model control algorithm was applied to a temperature object. According to experimental results it is shown that control performance of the intelligent internal model control algorithm is superior to one of the internal model control algorithm. It has great robustness and anti-inference, and this algorithm makes a small computation which is easy to use for engineering implementation.

The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID-like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.

Smart and personalized ventilation systems have been demonstrated with high performance in creating a healthy and energy-efficient indoor environment, but they have been rarely comprehensively summarized and explored in previous studies. With the progressive development of various terminal devices and control technologies, personalized ventilation based on intelligent control is potentially a promising way to achieve efficient control and energy savings in human micro-environments. This study comprehensively summarizes and analyzes the recent studies and common utilization forms of smart ventilation and PV systems that are based on CO2 concentration control, to pave path and provide some guidelines for their integration application for reducing energy consumption and improving indoor thermal comfort. Research shows that the combination of personalized ventilation and smart ventilation is an essential development for ventilation systems. Smart ventilation with demand control logic based on CO2 concentration has been mature enough to effectively improve the effectiveness and comfortable performance of personalized ventilation. However, switching from traditional air conditioning systems to personalized ventilation still requires improved sensors and intelligent control algorithms. In addition, this paper also summarizes the exploratory studies and potential application analysis of machine-learning theories to improve intelligent control of personalized ventilation. To this end, this paper identifies future tendencies for advanced theories, integrated systems, and devices in personalized ventilation systems.