Hierarchical Control of Ride Height System for Electronically Controlled Air Suspension Based on Variable Structure and Fuzzy Control Theory

The current research of air suspension mainly focuses on the characteristics and design of the air spring. In fact, electronically controlled air suspension （ECAS） has excellent performance in flexible height adjustment during different driving conditions. However, the nonlinearity of the ride height adjusting system and the uneven distribution of payload affect the control accuracy of ride height and the body attitude. Firstly, the three-point measurement system of three height sensors is used to establish the mathematical model of the ride height adjusting system. The decentralized control of ride height and the centralized control of body attitude are presented to design the ride height control system for ECAS. The exact feedback linearization method is adopted for the nonlinear mathematical model of the ride height system. Secondly, according to the hierarchical control theory, the variable structure control （VSC） technique is used to design a controller that is able to adjust the ride height for the quarter-vehicle anywhere, and each quarter-vehicle height control system is independent. Meanwhile, the three-point height signals obtained by three height sensors are tracked to calculate the body pitch and roll attitude over time, and then by calculating the deviation of pitch and roll and its rates, the height control correction is reassigned based on the fuzzy algorithm. Finally, to verify the effectiveness and performance of the proposed combined control strategy, a validating test of ride height control system with and without road disturbance is carried out. Testing results show that the height adjusting time of both lifting and lowering is over 5 s, and the pitch angle and the roll angle of body attitude are less than 0.15°. This research proposes a hierarchical control method that can guarantee the attitude stability, as well as satisfy the ride height tracking system.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

Thermodynamic Modeling and Simulation of Air System Control Device

This paper aims to obtain the thermodynamic characteristics of the air system control device sealing part in different compressor bleed air and ambient temperature.On the basis of considering the main factors affecting the heat exchange process and simplifying the physical model of the air system control device,the thermodynamic model of air system control device is established based on the basic theory of laminar flow heat transfer and heat conduction theory.Then the piston motion characteristics and the thermodynamic characteristics of the air system control device seal are simulated.The simulation results show that the valve actuation dynamic time of piston is about 0.13 s in the actual working conditions,and the temperature effect on the dynamic response of the piston rod is only 5 ms when the inlet air temperature at 300 ℃ and 370 ℃.The maximum temperature of the air system control device sealing part is not more than 290 ℃ under long time working condition of compressor air entraining.The highest temperature of the sealing part can reach up to 340 ℃ when the air flow temperature reaches the limit temperature of 370 ℃,and the longest duration working temperature limit is not more than 14 s.Therefore,the selection of control device sealing material should consider the work characteristic of instantaneous temperature limit.

Intelligent Virtual Assistant System(IVAS) for Air Traffic Controllers

Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.

Type selection and maintenance of the control system of large-scale air separation units

Based on the practice of Baosteel＇ s 60000 m3/h air separation unit （ASU） ,which is the first domestically- integrated unit of such a scale, this paper studies the principles of type selection of the distribution control system （DCS）. It discusses the design of the unit＇s control system,which involves a compressor system,a purification system （molecular sieving）, a turbo expansion system and an air separation system. The final part of the paper discusses the maintenance and future development of the ASU control system at Baosteel.

Adaptive inverse control of air supply flow for proton exchange membrane fuel cell systems

To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.

An Energy-Saving Control Strategy on Central Air-Conditioning System

An energy-saving control strategy based on predictive control for central air-conditioning systems is proposed in this paper. The cold load model is developed to describe the dynamic characteristics of temperature control systems, and then parameters in the cold load model and in the central air-conditioning system model are estimated. Generalized predictive control (GPC) is used to establish an optimization model to minimize the consumption of energy and the control error of temperature. The simulated annealing (SA) algorithm, combined with quadratic programming, is adopted to solve the optimal problem. Contrasted with the simulation of traditional PID control, the results prove the effectiveness of this proposed strategy.

Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System

The stable operation of the central air conditioning water system always is a major difficulty for the control profession. Paper focus on the water system with multi variable, strong coupling, nonlinear, large time delay characteristics, presented use feed forward coupling compensation method, to eliminate the coupling effect between temperature and pressure. In this paper, the Elman neural network controller is designed for the first time, and the simulation results show that the response time of Elman neural network controller is shorter, the system is more stable and the overshoot is small.

Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era

China’s past economic growth has substantially relied on fossil fuels,causing serious air pollution issues.Decoupling economic growth and pollution has become the focus in developing ecological civilization in China.We have analyzed the three-decade progress of air pollution controls in China,highlighting a strategic transformation from emission control toward air quality management.Emission control of sulfur dioxide(SO2)resolved the deteriorating acid rain issue in China in 2007.Since 2013,control actions on multiple precursors and sectors have targeted the reduction of the concentration of fine particulate matter(PM2.5),marking a transition to an air-quality-oriented strategy.Increasing ozone(O3)pollution further requires O3 and PM2.5 integrated control strategies with an emphasis on their complex photochemical interactions.Fundamental improvement of air quality in China,as a key indicator for the success of ecological civilization construction,demands the deep de-carbonization of China’s energy system as well as more synergistic pathways to address air pollution and global climate change simultaneously.

Evaluation of mercury speciation and removal through air pollution control devices of a 190 MW boiler

Air pollution control devices （APCDs） are installed at coal-fired power plants for air pollutant regulation. Selective catalytic reduction （SCR） and wet flue gas desulftLrization （FGD） systems have the co-benefits of air pollutant and mercury removal. Configuration and operational conditions of APCDs and mercury speciation affect mercury removal efficiently at coal-fired utilities. The Ontario Hydro Method （OHM） recommended by the U.S. Environmental Protection Agency （EPA） was used to determine mercury speciation simultaneously at five sampling locations through SCR-ESP-FGD at a 190 MW unit. Chlorine in coal had been suggested as a factor affecting the mercury speciation in flue gas; and low-chlorine coal was purported to produce less oxidized mercury （Hg^2＋） and more elemental mercury （Hg^0） at the SCR inlet compared to higher chlorine coal. SCR could oxidize elemental mercury into oxidized mercury when SCR was in service, and oxidation efficiency reached 71.0%. Therefore, oxidized mercury removal efficiency was enhanced through a wet FGD system. In the non-ozone season, about 89.5%-96.8% of oxidized mercury was controlled, but only 54.9%-68.8% of the total mercury was captured through wet FGD. Oxidized mercury removal efficiency was 95.9%-98.0%, and there was a big difference in the total mercury removal efficiencies from 78.0% to 90.2% in the ozone season. Mercury mass balance was evaluated to validate reliability of OHM testing data, and the ratio of mercury input in the coal to mercury output at the stack was from 0.84 to 1.08.

Leaching behavior of Pb and Zn in air pollution control residues and their modeling prediction

Increasing attention has been paid to air pollution control （APC） residues in China recently due to the rising proportion of waste incineration and the hazardous characteristics of the residues, among which heavy metal leaching toxicity plays an important role. Leaching behavior and potential risk of Pb and Zn in the APC residues from a Shanghai municipal solid waste （MSW） incinerator was studied, based on the leaching tests under different conditions and theoretical calculation using a geochemical thermodynamic equilibrium model MINTEQA2. Results showed that, extractant species and liquid to solid （L/S） ratio predominantly controlled the leaching toxicity of Pb and Zn, while ionic strength, vibration method and leaching time had less effect on the metals release. Leachate/final pH determined the metal leaching behavior, which changed the speciation of heavy metals in the extraction system. The equilibrium aqueous speciation, precipitation-dissolution of Pb and Zn was investigated according to the model computation, which was well in agreement with the experimental results.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Stiffness-damping matching method of an ECAS system based on LQG control

A novel method of matching stiffness and continuous variable damping of an ECAS(electronically controlled air suspension) based on LQG(linear quadratic Gaussian) control was proposed to simultaneously improve the road-friendliness and ride comfort of a two-axle school bus.Taking account of the suspension nonlinearities and target-height-dependent variation in suspension characteristics,a stiffness model of the ECAS mounted on the drive axle of the bus was developed based on thermodynamics and the key parameters were obtained through field tests.By determining the proper range of the target height for the ECAS of the fully-loaded bus based on the design requirements of vehicle body bounce frequency,the control algorithm of the target suspension height(i.e.,stiffness) was derived according to driving speed and road roughness.Taking account of the nonlinearities of a continuous variable semi-active damper,the damping force was obtained through the subtraction of the air spring force from the optimum integrated suspension force,which was calculated based on LQG control.Finally,a GA(genetic algorithm)-based matching method between stepped variable damping and stiffness was employed as a benchmark to evaluate the effectiveness of the LQG-based matching method.Simulation results indicate that compared with the GA-based matching method,both dynamic tire force and vehicle body vertical acceleration responses are markedly reduced around the vehicle body bounce frequency employing the LQG-based matching method,with peak values of the dynamic tire force PSD(power spectral density) decreased by 73.6%,60.8% and 71.9% in the three cases,and corresponding reduction are 71.3%,59.4% and 68.2% for the vehicle body vertical acceleration.A strong robustness to variation of driving speed and road roughness is also observed for the LQG-based matching method.

En-route Sector Complexity Control Strategies in Air Traffic Management

Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.

Empirical Exploration of Air Traffic Control Behaviour at Terminal Maneuvering Area:From an Air Traffic Flow Aspect

In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway refers to the time interval between successive flying aircraft in air traffic flow,which is one of the most important characteristics of air traffic flow.The variation in aircraft headway reveals the air traffic control behaviour.In this paper,we study the characteristics of air traffic control behaviours by analyzing radar tracks in a terminal maneuvering area.The headway in arrival traffic flow is measured after the determination of aircraft trailing relationships.The headway evolutionary characteristics for different control decisions and the headway evolutionary characteristics in different phase-states are discussed,and some interesting findings are gotten.This work may be helpful for scholars and managers in understanding the intrinsic nature of air traffic flow and in the development of intelligent assistant decision systems for air traffic management.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Effect of Working Experience on Air Traffic Controller Eye Movement

Eye movement is an important indicator of information-seeking behavior and provides insight into cognitive strategies which are vital for decision-making.Various measures based on eye movements have been proposed to capture humans’ability to process information in a complex environment.The effectiveness of these measures has not yet been fully explored in the field of air traffic management.This paper presents a comparative study on eye-movement measures in air traffic controllers with different levels of working experience.Two commonly investigated oculomotor behaviors,fixation and saccades,together with gaze entropy,are examined.By comparing the statistical properties of the relevant metrics,it is shown that working experience has a notable effect on eye-movement patterns.Both fixation and saccades differ between qualified and novice controllers,with the former type of controller employing more efficient searching strategies.These findings are useful in enhancing the quality of controller training and contributing to an understanding of the information-seeking mechanisms humans use when executing complex tasks.

Integrated Evaluation of Air Traffic Controller Workload Based on Matter-Element Analysis

A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.

Air flow control based on optimal oxygen excess ratio in fuel cells for vehicles

Air flow control is one of the most important control methods for maintaining the stability and reliability of a fuel cell system, which can avoid oxygen starvation or oxygen saturation. The oxygen excess ratio （OER） is often used to indicate the air flow condition. Based on a fuel cell system model for vehicles, OER performance was analyzed for different stack currents and temperatures in this paper, and the results show that the optimal OER was affected weakly by the stack temperature. In order to ensure the system working in optimal OER, a control scheme that includes an optimal OER regulator and a fuzzy control was proposed. According to the stack current, a reference value of air flow rate was obtained with the optimal OER regulator and then the air compressor motor voltage was controlled with the fuzzy controller to adjust the air flow rate provided by the air compressor. Simulation results show that the control method has good dynamic and static characteristics.