Real-time Control Method Research Articles

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Optimal control for helicopter/turboshaft engine system based on hybrid variable speed

In order to address the limitation of fixed-ratio transmission (FRT), which compromises the attainment of both optimal main rotor speed and optimal power turbine speed, an optimal speed control method based on hybrid variable speed (HVS) is proposed. Firstly, based on the integrated performance calculation model of helicopter/turboshaft engine system, the distribution factors of variable speed are applied, and the integrated optimization method of optimal speed is proposed based on the minimum engine fuel flow. Subsequently, an online estimation technique employing a high-order filter is devised and engineered to achieve superior cascaded control of turboshaft engines. Finally, a novel real-time optimal speed control method based on hybrid variable speed is proposed. The simulation results under different operation conditions demonstrate that regardless of whether it is FRT or HVS, the optimal main rotor speed increases with forward velocity. In the case of HVS, turboshaft engine degradations have a significant impact on the optimal power turbine speed rather than optimal main rotor speed. Adopting an estimation method based on high-order filtering for gas turbine rotational acceleration proves more advantageous in mitigating high-frequency oscillation and continuous saltation of estimated values. Moreover, in comparison with the optimal speed control method of FRT, HVS-based approach enables simultaneous attainment of the optimal main rotor speed and power turbine speed, thereby enhancing the overall efficiency of the integrated helicopter/turboshaft engine system and significantly decreasing engine fuel consumption by over 2%. Consequently, there has been a remarkable enhancement in the overall performance of the integrated helicopter/turboshaft engine system.

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Real-time coloration control of gallium-based strips through cold rolling.

Cold rolling has been used as a real-time surface oxidation control method to create colored strips on flexible substrates. By controlling the extrusion rate in real time, a variety of colored strips have been fabricated on Ga-based liquid metal (LM) strips. X-ray photoelectron spectroscopy (XPS) analysis shows that the surfaces of the colored strips, which were obtained through extrusion rate control of LM-Al, consist primarily of metal oxide composites, including Ga2O3, Ga2O, Al2O3, SnO2, and In2O3. The colors of the strip surfaces are directly correlated with the oxide film thickness. Additionally, these cold-rolled colored thin strips demonstrate high conductivity and have significant potential for use as conductive flexible components with indicator functions in the flexible electronics realm.

An efficient and robust real-time longitudinal ventilation control method for unpredictable tunnel fire scenarios

A novel real-time control method, named SF-B control method, was proposed and a comprehensive numerical simulation study was conducted for feasibility analysis. The simulation utilized a combination of Python scripts and FDS. The SF-B control method incorporates automatic detection and localization of the fire source, and the existing ventilation velocity is corrected based on the real-time state of motion of smoke fronts and the back-layering length. The control logic of the SF-B control method was described in detail, and the criterion for determining stabilization was provided. After a comparison with PID control method, scenarios involving random constant HHRs, random fire locations and variable HRR were set up for separate simulations, and the results were compared with previous experimental findings. The outcomes demonstrate that the fluctuation of the SF-B control method is only about 1/10 of that of the PID control method, and the control performance is not significantly influenced by the HRR or fire location. It shows a more powerful control ability on smoke back-layering in scenarios involving variable HRR. This method exhibits excellent compatibility with different fire conditions, showcasing high ventilation control efficiency, robust control capabilities, and delivering reliable control results.

Real-time optimization control of variable rotor speed based on Helicopter/ turboshaft engine on-board composite system

In order to adequately leverage the advantages of coaxial compound helicopters (CCH) to enhance overall performance and efficiency through variable rotor speed, and to improve the fuel economy of the composite propulsion system, a real-time optimization control method of variable rotor speed based on helicopter/turboshaft engine on-board composite system is proposed. Firstly, a steady-state model of the CCH required power and an on-board inverse model of the turboshaft engine based on an improved deep neural network propulsion system model (AdamDNN-PSM) are established. On this basis, an on-board adaptive model based on adaptive square root Kalman filter (ASRUKF) estimator is employed to correct the precision of the on-board inverse model and to track the real engine state. Finally, by adopting the feasible sequence quadratic programming (FSQP) method, an optimal speed rotor (OSR) is obtained through real-time optimization control, which reduces fuel consumption during cruising while considering both the geometric and aerodynamic constraints of the rotor. The simulation results demonstrate that the accuracy and real-time performance of the on-board inverse model have been significantly improved. The real-time optimization control method for OSR effectively reduces fuel consumption during cruising and enhances the CCH performance of the entire flight envelope.

Development of an indirect measurement method for the Contact Tube to Workpiece Distance (CTDW) in the Direct Energy Deposition – Arc (DED-ARC) process for different arc types

During the layer-by-layer build-up in the Direct Energy Deposition (DED) - Arc additive manufacturing (AM) process, the distance between the contact tube and the workpiece, effectively the welded layer, changes. Since the weld paths are predefined by the path planning software, a constant Contact Tube to Workpiece Distance (CTWD) and weld bead height is assumed. However, even small changes in geometry, such as crossovers of weld paths, result in higher weld beads than assumed. Similarly, an incorrectly assumed bead height as input to the path planning will result in a change in the CTWD. The sum of the deviations of the real weld geometries from the assumed ones in the path planning can greatly influence the CTWD. This implies that the dimensional accuracy may be significantly compromised. This research presents an approach for a general indirect measurement method using the welding current to obtain the CTWD during the actual welding process. A real-time process control method is implemented and validated using the mechanically controlled short arc and the pulsed arc process. Varying process parameters are used to validate the general applicability for a specific material. For the mechanically controlled short arc process, the model underestimates the measured CTWD by a mean error of 3.4 mm. The pulse process is overestimated by a mean error of 2.2 mm. The standard deviation for the pulse process with 1.3 mm is slightly smaller than for the short arc process with 1.7 mm.

An Enhanced Voltage Control Method for Fair Active Power Curtailment of Rooftop PV Systems in LV Distribution Networks

Solutions based on active power curtailment (APC) of photovoltaic inverters (PVIs) have been effectively used to alleviate overvoltages in low-voltage distribution networks (LVDNs) due to real power injection supplied by rooftop photovoltaic (PV) systems. However, volt–watt control methods presently available can lead to unfair APC among the PVIs, penalizing financially customers located far away from the distribution transformer of the LVDN. This paper proposes an innovative real-time voltage control method to address the unfair APC issue in LVDNs with rooftop PV systems. The proposed method is based on a centralized control architecture to calculate two fair curtailment factors for all PVIs of the LVDN. Additionally, to explore the extended communication infrastructure brought by centralized control architecture, this paper proposes a novel simplified method to compute the voltage sensitivity matrix (VSM) for applications on unbalanced three-phase LVDNs. Performance tests were conducted on a set of LVDNs, evaluating the effectiveness and fairness of APC quantitatively using Jain’s Fairness Index (JFI) as a metric. The results confirmed the effectiveness of the proposed control method for mitigating the overvoltage issue, ensuring fairness by presenting average JFI values close to one (the maximum allowed value). Furthermore, in comparison to conventional VWC strategies, penalties arising from the local dependence of PVIs on APC were eliminated for both curtailment factors. Finally, an accuracy test was performed on the proposed VSM model, based on voltage estimation through the linearized model of LVDN at a specific operating point. The findings demonstrated outstanding accuracy, with an error of less than 0.2%, compared to voltage estimation via power flow.

Accounting for passenger loss in bus bunching reduction: a robust real-time speed control method

ABSTRACT Bus bunching is a common phenomenon resulting from the inherent instability influenced by factors such as traffic and passenger behaviour. However, the issue of passenger loss caused by extended waiting time, is often overlooked despite being a significant concern for passengers. This paper addresses this problem by modelling passenger arrival and loss as a Markov birth–death process. We present a transit route model that incorporates passenger loss while considering additional factors, such as stochasticity, overtaking, passenger alighting, and bus capacity constraints. The primary objective of our model is to minimise passenger loss. To achieve this, we conduct an analysis about the characteristics of passenger loss. Under mild assumptions, we prove that passenger loss increases as the headways between buses exhibit greater variance. We also develop a robust optimisation (RO) method and corresponding algorithms to minimise passenger loss. To validate the effectiveness of our control method, we conduct numerical experiments using both computer-generated and real-world data. The results demonstrate that our method effectively reduces bus bunching, passenger loss, and waiting time compared to the existing literature.

A Multi-Objective Optimal Control Method for Navigating Connected and Automated Vehicles at Signalized Intersections Based on Reinforcement Learning

The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles’ motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TD3) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories.

Edge AI-Based Smart Intersection and Its Application for Traffic Signal Coordination: A Case Study in Pyeongtaek City, South Korea

Recently, smart intersections have emerged as a novel intelligent transportation system (ITS) solution that integrates traffic monitoring, optimal signal control, and even traffic safety. Although smart intersections have been prevalent in many cities, there are a few drawbacks in their practical operations. First, there are inevitable delays in transmitting and processing the video data. Second, there is still a need to develop a real-time signal control method leveraging the acquired data from smart intersections. Thus, this study aims to construct edge AI-based smart intersections and to provide their application for traffic signal coordination. To this end, we install smart intersections on three consecutive intersections of Route 45 in Pyeongtaek city, South Korea. The real-time traffic data are collected by an edge AI video analysis model which is compressed and optimized for its operation in on-site edge devices. The optimized model maintains a similar level of accuracy (93.64%), even if the size is reduced by 97.8% compared to the original. Next, we utilize the LT2 model to treat the coordination failure problem in nonpeak hours occurring unnecessary delays of the side-streets with relatively high demands. We complement some constraint conditions in order to consider the compatibility with the current legacy system. The experiment is conducted on a virtual environment of which geometry and traffic demand are configured based on the features of the study site. The numerical results conclude that the optimal offsets calculated by the LT2 model effectively manage bandwidths for multidirectional flows based on the real-time traffic demands collected from the edge AI-based smart intersections. This study contributes to serve high-resolution real-time traffic data using edge AI on smart intersections and to provide a case study for signal coordination.

Power screw-assisted reconfigurable reflective metasurface with spatial modulation

Reconfigurable reflective metasurfaces (RRMs) have garnered significant attention due to their capability to control reflection characteristics. While conventional metasurfaces manipulate the phase and amplitude of the reflected beam through the arrangement of sub-wavelength scatterers in a periodic array, achieving dynamic manipulation typically necessitates external stimuli. Alternatively, spatial modulation within a metasurface introduces gaps in the metastrips, enabling a spatial phase shift across the reflected beam and facilitating real-time beam steering and shaping. This paper proposes a novel power screw-assisted reconfigurable reflective metasurface for achieving beam control and spatial modulation. The design integrates a power screw into the metasurface structure, providing controlled displacement and continuous beam scanning capabilities. Experimental results demonstrate that the proposed metasurface can scan from ±32° to ±15° at 28 GHz. Unlike current designs requiring complex external control systems for beam scanning, this approach offers a simpler yet powerful method for real-time control of the reflected beam. The novelty of this work lies in the integration of the power screw mechanism, presenting a promising advancement in the field of reconfigurable reflective metasurfaces with potential applications in communication systems, energy harvesting, and adaptive optics.

Real-Time Line Drop Compensation Control Method of Step Voltage Regulator on Distribution Lines with Distributed Generation

Real-Time Line Drop Compensation Control Method of Step Voltage Regulator on Distribution Lines with Distributed Generation

Development and Evaluation of "The Delta Plus-Minus Even Distribution Check": A Novel Patient-Based Real-Time Quality Control Method for Laboratory Tests.

Laboratory testing of large sample numbers necessitates high-volume rapid processing, and these test results require immediate validation and a high level of quality assurance. Therefore, real-time quality control including delta checking is an important issue. Delta checking is a process of identifying errors in individual patient results by reviewing differences from previous results of the same patient (Δ value). Under stable analytical conditions, Δ values are equally positively and negatively distributed. The previous 20 Δ values from 3 tests (cholesterol, albumin, and urea nitrogen) were analyzed by calculating the R-value: "the positive Δ value ratio minus 0.5." This method of monitoring optimized R-values is referred to as the even-check method (ECM) and was compared with quality control (QC) testing in terms of error detection. Bias was observed on 4 of the 120 days for the 3 analytes measured. When QC detected errors, the ECM captured the same systematic errors and more rapidly. In contrast, the ECM did not generate an alarm for the one random error that occurred in QC. While QC did not detect any errors, the percentage of R-values exceeding the acceptable range was under 2%, the number of days generating alarms was between 16 and 21 days, with short alarm periods, and a median number of samples per alarm period between 7 and 9 samples. The ECM is a practical real-time QC method, controlled by setting R-value conditions, that quickly detects bias values.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Proposal for Real-time Quality Control Method to Enhance Hydrological Application of Radar Rainfall

The primary aim of a rain radar is to observe ground-level rainfall accurately. Effective quality control of rain radar data is necessary to ensure accuracy. In the field of hydrology, various methods have been employed to improve the quality of radar rainfall by correcting or synthesizing it with gauge rainfall. However, it is more important to enhance the quality of radar polarimetric variables in order to improve the quality of radar rainfall. Rain radar systems generate various polarimetric variables through complex radar hardware systems and signal processing algorithms. Measurement errors may arise from equipment abnormalities, inappropriate operating settings, and obstructions in observation during signal processing algorithms and production process of polarimetric variables. Because rain radar is an observation device operating in real time, it entails more frequent and continuous quality control compared to flowmeters that intermittently measure streamflow. This study introduces a series of real-time quality control methods tailored for rain radar, including inspection of radar rainfall accuracy, quality analysis of polarimetric variables (ρ<sub>HV</sub>, Z<sub>DR</sub>, Φ<sub>DP</sub>), error estimation of polarimetric variables, and long-term variability of errors.

Mitigating vulnerability of a multimodal public transit system for sustainable megacities: a real-time operational control method

The multimodal public transit system (MPTS) has been recognized as a primary mobility support system for sustainable megacities. However, it is often vulnerable to various service disruptions, and the most severe circumstance is the interdependent cascading failures interacting among urban rail transit, bus transit, and road transit networks. The vulnerability of an MPTS against this severe failure, associated with extreme performances, limits the building of future resilient megacities. In this paper, a real-time operational control method based on the system emergency capability (SEC) is developed to block the dynamic unfolding paths of this severe failure considering network topology characteristics and dynamic evolution characteristics. This method is immediately available for real-time emergency control, while previous studies on qualitative optimization strategies cannot. Remarkably, a three-stage association design process is conducted to explore the most efficient SEC loading strategy, involved with multiple intertwined influential factors, including the target loading stations, loading time-step intervals, and loading interval length and loading strength. Finally, a case simulation is undertaken to indicate the adaptability of the proposed method. This work can provide critical insights into real-world emergency resource allocation and an underlying simulator with search direction knowledge for future intelligent algorithm-based optimal control.

A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human-computer interactions.

Cooperative control method considering efficiency and tracking performance for unmanned hybrid tractor based on rotary tillage prediction

In order to address the issues of poor trajectory tracking accuracy and low energy utilization efficiency in operation for unmanned hybrid tractors, a cooperative control method was proposed specifically for rotary tillage conditions. This method aims to improve the tractor efficiency and trajectory tracking performance. Firstly, a power take-off (PTO) power prediction method based on the Markov chain is designed to address the PTO of sudden load changes. Secondly, a trajectory tracking method based on model predictive control is designed to improve the accuracy of rotary tillage operation tracking. Next, in order to achieve efficient utilization of overall system energy, a real-time energy-saving control method based on instantaneous optimization is proposed. Finally, integrating the PTO power prediction method, trajectory tracking method, and real-time energy-saving control method with velocity and PTO power as the interactive variables, a cooperative control method based on Markov-trajectory tracking is established. The results show that the proposed control method simultaneously improves the energy utilization efficiency and trajectory tracking accuracy of the tractor under the rotary tillage condition. This study provides a reference for the cooperative control of the drive system and trajectory tracking of unmanned hybrid tractors.

Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller.