Future Control Systems Research Articles

Safety Concepts for Future Electromechanical Brake Central Control Systems

<div>Electromechanical brakes (EMB) are currently coming into focus in the automotive industry. This trend was confirmed in 2022, when a first automotive supplier [<span>1</span>] announced the series production of EMB systems. One major driver is safety, especially if EMB systems are implemented with smart actuators that install redundant electronic control units (ECU) and distributed software [<span>1</span>]. Earlier, the authors have addressed safety mechanisms in EMB actuators [<span>2</span>]. In this article the authors extend their investigation to address safety mechanisms in future EMB central control systems (CCS). Impact of different brake system topologies (X-, H-, centralized) vis-à-vis potential safety mechanisms within communication buses and ECUs is analyzed.</div>

Surrogate model of turbulent transport in fusion plasmas using machine learning

Abstract The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the construction of models that effectively streamline the simulation process. While previous first-principles simulations have provided physics-based transport information, they have been inadequate fast for real-time applications or plasma control. In order to address this challenge, we introduce SExFC, a surrogate model based on the Gyro-Landau Extended Fluid Code (ExFC). An approach of physics-based database construction is detailed, as well the validity is illustrated. Through harnessing the power of ML, SExFC offers the capability to deliver rapid and precise predictions, facilitating real-time applications and enhancing plasma control. The proposed model integrates the recurrent neural network (RNN) algorithm, specifically leveraging the Gated Recurrent Unit (GRU) for iterative prediction of flux evolutions based on radial profiles. Therefore, the SExFC model has the potential to enable rapid and physics-based predictions that can be seamlessly integrated into future real-time plasma control systems.

A traffic incident detection method of highway based on spatial-temporal characteristics

In this study, we propose a method for detecting traffic incidents by leveraging tensor decomposition in conjunction with spatial-temporal constraints. The method comprises three main stages. Firstly, we develop an initialization and noise reduction procedure for the traffic data pre-processing model, which includes Tucker decomposition and truncating higher-order Singular Value Decomposition (SVD). Subsequently, we construct spatial and temporal coefficient matrices based on the Pearson test and introduce a Laplace penalty term to quantify the disparity between predicted and actual data. Next, we validate the method using traffic loop detector data and incident records from I90 in Seattle, USA, in 2015, comparing its performance with seven other traffic prediction methods. The results demonstrate the superior prediction accuracy, correct detection rate, and low false alarm rate of our traffic incident detection method. Specifically, the mean square error of speed prediction is 5.22 km / h , the average relative error is 6.88 % , and the detection rate reaches 98.92 % . Our method effectively evaluates the spatial-temporal characteristics of traffic data, enabling accurate prediction and detection of traffic incidents. Its technical applicability holds promise for enhancing the capacity and efficiency of future traffic control systems.

Thrust online fusion estimation of high-flow dual variable cycle engine

For the aeroengines, thrust is a crucial performance parameter which is closely related to the mission accessibility. Accurate thrust control is conducive to excavate the potential of the variable cycle engine (VCE) in different operation modes, which meets the requirements of future engine control system. However, the thrust is unmeasured in flight. Besides, the traditional thrust estimation method is difficult to meet the wide-area thrust control requirements due to the strong nonlinearity and large flight envelope for a neoconfiguration VCE of the high-flow triple-bypass, named high-flow dual variable cycle engine (HDVCE). This paper proposes a novel online fusion thrust estimation method mainly under single operation mode for the HDVCE. The problem of individual engine differences is also focused, and the combination of model and data-driven is to generate the thrust estimation with fusion strategy. At first, a multi-combustion chamber coupled dynamic model is established for the HDVCE, and an adaptive network is used for model optimization to improve the model accuracy. On this basis, an EKF-based thrust estimator is built. Secondly, a data-driven thrust estimator is pre-trained by a stacked autoencoder (SAE) and then tuned by back propagation (BP) neural network. Subsequently, the thrust integration is incorporated based on estimation results from the EKF-based and data-driven one using Euclidean distance fusion strategy. Simulation results show that the fusion estimation method proposed in this paper, on the one hand, reduces the effect of model instability on EKF. On the other hand, it ensures the estimation accuracy of SAE-BP far from the training point. The comparisons also reveal that the fusion one produces more than 97% in thrust estimation accuracy and is better than the remaining ones, which is promising for thrust control applications.

Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory

This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design.

The application of Internet of Things technology in intelligent fire protection

The uncertainty of fire and the difficulty of accurate prevention greatly increase the difficulty of fire prevention and control. Therefore, it is necessary to actively introduce modern science and technology, adopt fire Internet of Things (IoT) technology, realize remote dynamic monitoring, and accurately obtain fire prevention and control data information. This paper carries out an in-depth analysis on fire prevention and control, focusing on the application of fire IoT in the field of fire prevention and control. In addition, it specifically introduces the key technologies of intelligent fire protection, Radio Frequency ID, and the construction of fire safety models. At the same time, it describes the problems faced by the IoT in intelligent fire protection, that is, the quality of transmitted information is not strong, the speed of message transmission needs to be improved, and the application platform that is more conducive to the transmission of information needs to be built. By overcoming these issues, the future intelligent fire control system will be more intelligent and bring more convenience to people.

Measurement of conductive fabrics electrical resistance by combining of image processing and convolutional neural network methods

This study proposes the use of a CNN model to predict the resistance of conductive fabrics by utilizing the brightness information from their images, aiming to address the limitations of traditional contact-based measurement methods and explore the feasibility of non-contact resistance measurement. Conductive fabrics were produced using environmentally friendly cellulose fiber as a base material, with a dip-coating and padding process involving water-based single-walled carbon nanotube (SWCNT). After scanning the produced conductive fabrics and meticulously preprocessing the images, a dataset for CNN training was constructed, comprising label values corresponding to the sheet resistance of each image. ANOVA analysis confirmed a statistically significant relationship ( p-value = 8.04145e^-18) between the brightness of conductive fabric images and their sheet resistance. By leveraging the relationship between the brightness of fabric images and sheet resistance, training of the CNN model yielded an RMSE of 0.0558 and an R-squared value of 0.9557, validating the effectiveness of the designed CNN model for image-based resistance prediction. This research is expected to contribute to the development of future real-time monitoring and control systems, providing a crucial foundation for the advancement of data-driven measurement and control systems based on computer vision and machine learning techniques. Furthermore, it is anticipated to unveil new possibilities for various applications of conductive fabrics.

Force modeling of a reluctance motion system with multi-axial asymmetrical air gaps

Semiconductor photolithography equipment requires an actuation system that can satisfy high acceleration and precision demands on the nanoscale in order to operate with high throughput and efficiency. Reluctance actuators, which offer greater acceleration and force output than conventional Lorentz actuators, are one option. The photolithography technique uses a floating stage with air bearings to reduce friction; nonetheless, vibration transfer is not completely prevented, potentially causing misalignment and asymmetries between the actuator components. The output force can be significantly impacted by unequal offsets between the mover and stator. In the paper that follows, a technique for calculating the force of various asymmetrical instances for the C-core reluctance actuator is demonstrated. This paper looks into the effect of rotational offsets in all rotational degrees of freedom occurring simultaneously. Previously, only each degree of freedom was analyzed. Analytical models are developed and compared to Comsol Multiphysics (COMSOL) simulations, and experimental findings show accuracy to be within almost 10% for the ranges examined. The findings presented can allow for future control systems to be designed to counteract multi-axial asymmetric issues.

AN Optimization control Techniques for Aircraft Yaw Control Lateral Dynamics

Presently, advanced control theory plays a major role in the aircraft system for the control of Yaw, Roll and Pitch angles, which was very much necessary for the stabilization of aircraft system. Following the works of the control system community for aircraft applications, this paper concentrates on the control of Yaw angle by designing various optimal Linear Quadratic Regulator (LQR) controllers using standard existing tools. The optimal controllers are designed for the aircraft, tested and validated using MATLAB/SIMULINK environment, and the results are compared with different system conditions to select the best optimal LQR feedback controllers in future aircraft control systems.

State-space model construction and dynamic analysis for a space thermionic nuclear reactor

The space nuclear reactor (SNR) is an important energy pattern for spacecrafts due to its high power density, long life and stabilization. A linear dynamic model is necessary to analyze the dynamics and design the appropriate control system analytically. A simplified nonlinear model based on conservation equations including point reactor kinetics model, thermal-hydraulic model, coolant model and heat pipe and radiator model was constructed. The state-space linear model was derived using perturbation theory. To verify the linear model, comprehensive comparisons were made in both time domain and frequency domain. The dynamic characteristics with respect to reactor power and reactivity were obtained in frequency and time domains. It is concluded that the reactor is an unstable system with a large time delay and strong nonlinearity at different power levels. This study provides solid foundation for future control system design.

Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton's principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

Simulation of refrigerant-lubricant two-phase flow characteristics and performance test in space compressor

The vapor-compression heat pump allows for lightweight heat-control systems and efficient heat emissions. It is considered to be the best choice for future space thermal control systems. To solve the problem of separation and compression deterioration of conventional positive-displacement refrigeration compressors in a microgravity environment, an innovative microgravity solution, mixing refrigerant and lubricant without oil separation, is proposed. A flow model in the compression chamber was developed and simulations were performed to investigate the effects of gravity and lubricant volume fraction on the flow characteristics of the refrigerant-oil mixture in a rolling rotor compressor. A modified compressor performance test bench was built and the effect of compressor speed on parameters such as power and heat dissipation was investigated experimentally. The results showed a maximum difference between the simulated and experimental values of compressor discharge temperatures of 9.16 %. Under microgravity conditions, a lubricant-refrigerant mixture with a volume fraction of less than 5 % can serve as the system working medium. The lubricant volume fraction at the outlet of a compressor cycle in normal gravity was 4.9 %, while it was 3.34 % in microgravity. As the compressor speed increased from 1500 to 5000 rpm, the compressor power increased from 15.5 to 64.5 W and the system heat discharge rate increased from 115.2 to 164.1 W. The system refrigeration coefficient of performance (COPc) decreased from 6.4 to 1.5, while that of heat dissipation, COPh, decreased from 7.4 to 2.5.

Modelling and forecasting of relative humidity in Indian region

The forecasting of relative humidity (RH) is very important in planning various industrial activities and in designing future climate control systems. However, research on forecasting of RH is very few and far. In this study, a novel technique is proposed for forecasting one-day ahead RH using artificial neural network (ANN) and multiple linear regression (MLR) techniques by reducing the number of variables in input space gradually for an India region. The results show that both ANN and MLR models forecasted one-day ahead RH equally well. The ANN and MLR models which even use only lagged RH values performed equally well with nearly similar values of R (0.969 and 0.966), and RMSE (0.055 and 0.057), but MLR model has an advantage of being simpler and hence the present study recommends the use of MLR technique for RH forecasting. Also, the lagged RH values are sufficient for forecasting one-day ahead RH.

Stretchable flexible sensors for smart tires based on laser-induced graphene technology

Continuous feedback on a tire is an essential means to ensure tire safety. Smart tires are an important part of the future vehicle control system, which affects the safety and comfort of vehicles by combining sensors with traditional tires to achieve continuous monitoring of real-time dynamic parameters. A stretchable and flexible sensor made of laser-induced graphene (LIG) and PDMS, designed for use in smart tires, is presented in this work. The sensor is known as a LIG-PDMS sensor. Using transfer printing, LIG is formed on a commercial polyimide film under the scribing of a laser beam following the predesigned route before being transferred to a PDMS film. This technology is used to successfully prepare flexible sensors for measuring the tire road interaction at different driving speeds due to its flexibility and shape-following characteristics. The real-time monitoring of the wheel speed and the shape of the tire grounding mark during the driving process is realized by embedding multiple LIG sensors in the tire to monitor the strain information of the tire grounding. Results show that the tire deformation can be accurately feedbacked with the LIG sensors, demonstrating our method's capability for designing and manufacturing intelligent tires.

Exploring the formation mechanism of off-flavor of irradiated yak meat based on metabolomics.

Irradiation's effects on quality, volatile compounds, and differential metabolites of yak meat were studied. Irradiation dose at 3kGy had no effect on yak meat quality, however irradiation dose at 5kGy resulted in yak meat quality deterioration as well as considerable irradiated off-flavors. And the level of the off-odor was strongly associated with the irradiation dose, and allyl methyl sulfide, octanal, nonanal, benzaldehyde, and 4-methylthiazole were all significant producers of off-odor. Meanwhile, with the increased of radiation dose, the amounts of cysteine, methionine, proline, linoleic acid, stearic acid changed obviously. The main generation pathway of irradiated off-flavors in yak meat were thought to be cysteine and methionine metabolism, and linoleic acid metabolism. The oxidative decomposition of sulfur-containing amino acids and unsaturated fatty acids may cause the off-flavor of irradiation yak meat. This research established a theoretical foundation for future control systems to prevent flavor quality alterations during irradiation preservation.

Novel Off-Design Operation Maps Showing Functionality Limitations of Organic Rankine Cycle Validated by Experiments

In this study, we developed a new methodology to analyze the off-design performance of the organic Rankine cycle. The methodology enabled us to predict the performance based on four-dimensional decision variables and a set of constraints. A corresponding formulation and algorithm with general applicability were constructed. The reliability and feasibility of this methodology were validated by a test rig of the cycle with R245fa as the working fluid and three experimental schemes. Under specific working conditions, the theoretical results illustrated the operation maps, functionality limitations, and their variation laws, considering the interactive characteristics among the variables. The functionality limitations predicted a maximum thermal efficiency of 9.42%, corresponding to a net power output of 697.1 W, whereas the maximum net power output was 2251.5 W, corresponding to a thermal efficiency of 8.04%. The experimental results indicated that when the R245fa mass flow rate was 0.120 kg/s, the experimental efficiency and power output were 6.94% and 1873.4 W, respectively; when the R245fa mass flow rate was 0.049 kg/s, they were 3.54% and 274.1 W, respectively. These findings were in good agreement with the theoretical results, with relative errors below 7.64%. This work is expected to be applied in future dynamic control systems to update the setting points and manipulated variables in real time.

Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems

Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed.

Lighting Cultures in Northern and Southern Europe

In order to create awareness of different qualities of light in indoor living spaces, two different lighting cultures are being investigated; Northern and Southern European. We therefore investigate the relation between natural and artificial light, based on geographical position as well as social/cultural habits of different countries. The aim is that this approach will inform the user on how different lighting scenarios can improve the use of the space. The investigations are based on end user’s preferences and illustrate how awareness of different lighting cultures can be used in different lighting design scenarios. The findings from this specific project are meant to support the development of scenarios for future lighting fixtures and control systems based on a deeper understanding of cultural and geographical parameters.

Dynamic model construction of a space thermionic nuclear reactor

• A dynamic model of a space thermionic nuclear reactor is derived and established. • The dynamic model is verified with design parameters, steady and transient results. • Steady and dynamic characteristics of the reactor are simulated and analyzed. • The model and dynamic analysis supply solid foundation for the control studies. Space nuclear reactors, a mobile power supply equipment that converts nuclear energy to electric energy, have become significant for space exploration power supply, due to its long life and high power density. A detailed and accurate dynamic model is necessary to study the dynamic characteristics and design an appropriate control system. A dynamic model, including a reactor core, heat dissipation, and thermoelectric conversion models, was derived based on the conservation equations. Detailed development process is presented. MATLAB and Simulink were used to implement the model. To verify the developed model, simulations, including the steady state and startup process, were performed. The calculation results agreed well with the design parameters and the results obtained using other transient code. The dynamic characteristics were obtained by introducing the reactor power and reactivity disturbances. The space nuclear reactor can be concluded to have positive reactivity feedback, strong nonlinearity, and a large time delay. In this study, we provide research foundation for future control system design.

Improved Plasma Vertical Position Control on TCV Using Model-Based Optimized Controller Synthesis

Elongated plasmas lead to improved performance in tokamaks but make the plasma prone to vertical instability, which requires active feedback control, a critical issue for future fusion reactors. Vertical control was optimized for the TCV tokamak by applying modern control theory to electromagnetic models for the plasma-vessel-coils dynamics. Two different optimal combinations of poloidal field coils for vertical control actuation are derived from linear plasma response models and used on different timescales for controlling the plasma vertical position. On fast timescales, the priority is input minimization, while on long timescales position control is designed to be compatible with shape control. A structured H-infinity design extending classical H-infinity to fixed-structure control systems was subsequently applied to obtain an optimized controller using all available coils for position control. Closed-loop performance improvement was demonstrated in dedicated TCV experiments, showing a reduction of input requirement for stabilizing the same plasma, thus reducing the risk of power supply saturation and consequent loss of vertical control. This novel algorithm is adaptable to different plasma equilibria as it is designed for model-based automated coil selection and controller tuning, thus avoiding extensive experimental gain scans when performing plasma discharges in TCV. The presented technique is general and can be applied to any present tokamak with independent coils or for the design of future tokamak magnetic control systems.