Control Engineering Practice最新文献

{"title":"Integrated spatiotemporal guidance and decision-making design via distributed optimization strategy","authors":"Ang Huang ,&nbsp;Heng Li ,&nbsp;Qing Wang ,&nbsp;Jianglong Yu ,&nbsp;Xiwang Dong","doi":"10.1016/j.conengprac.2025.106469","DOIUrl":"10.1016/j.conengprac.2025.106469","url":null,"abstract":"<div><div>Integrated guidance and decision-making (IGDM) problems are investigated. Unlike previous works, the expected angles are autonomously determined. Firstly, a fundamental angle-constrained optimal guidance law is proposed, and the properties of the fundamental guidance law are analyzed. Then, a spatiotemporal-constrained distributed prescribed time guidance law is derived, which ensures zero acceleration component at the prescribed time. Furthermore, the pay-off function favorable for rapid arrival is presented. The distributed optimization-based expected angle decision-making strategy is then developed, ultimately leading to the IGDM law and algorithm. Finally, the effectiveness of the theoretical results is validated through numerical simulation and experimental results.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106469"},"PeriodicalIF":5.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and experimental validation of an asynchronous observer for CDC semi-active suspensions","authors":"Qing Yuan ,&nbsp;Hongliang Zhou ,&nbsp;Songlin Chen ,&nbsp;Weiwei Miao ,&nbsp;Zhen Yu","doi":"10.1016/j.conengprac.2025.106471","DOIUrl":"10.1016/j.conengprac.2025.106471","url":null,"abstract":"<div><div>This paper presents an asynchronous observer for estimating the vibration states in continuous damping control (CDC) semi-active suspension systems, using acceleration data from both sprung and unsprung masses. Load transfer exerted on the suspension due to variations in vehicle speed is treated as an external input to the system, compensating for inaccuracies in the quarter-car suspension model under variable speed conditions. Using deflection velocity as a switching signal, a piecewise affine (PWA) model of the CDC suspension system is developed to describe the nonlinear behavior of the damper. Given that the deflection velocity signal is typically not directly measurable in engineering applications and can only be obtained inaccurately through signal processing of existing sensor data, an asynchronous observer is designed based on the given system model. This observer ensures both stability and precision of estimation, particularly when the observer and the system reside in different partitions due to discrepancies in acquiring deflection velocity signal. Experimental results indicate that the proposed observer significantly enhances the estimation accuracy, with maximum root mean square error of 0.00592 m for suspension deflection, 0.11889 m/s and 0.10646 m/s for sprung and unsprung mass velocities, and 0.00168 m for tire deflection.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106471"},"PeriodicalIF":5.4,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated path planning and control for autonomous vehicle platooning","authors":"Kiyun Gil,&nbsp;Jinsoo Yuk,&nbsp;Jongho Shin","doi":"10.1016/j.conengprac.2025.106470","DOIUrl":"10.1016/j.conengprac.2025.106470","url":null,"abstract":"<div><div>Platooning provides various benefits, such as improving traffic system efficiency and reducing fuel consumption. Existing research on platooning has primarily concentrated on longitudinal control in highways or dedicated road environments. However, these approaches can significantly reduce platooning performance during scenarios requiring lateral maneuvers, such as cornering or obstacle avoidance. To address these limitations, this study proposes a comprehensive platooning system that considers both longitudinal and lateral dynamics. The proposed platooning approach comprises model predictive control (MPC)-based path planning incorporating the constant time gap (CTG) strategy, and integral control-based path tracking. The MPC-based path planning is formulated as an optimal control problem aimed at minimizing the total cost, which includes the candidate path cost based on CTG policy-generated velocity commands and environmental costs. The optimal control input is obtained using particle swarm optimization (PSO), resulting in the generation of an optimal path. For path tracking, an integral error for yaw rate is defined, and an integral control-based method is employed, considering the differential equation of the integral error and the vehicle’s dynamics model. Numerical simulations and indoor experiments are conducted to validate the feasibility of the proposed approach, with an analysis of the results. A validation video is available at: <span><span>https://youtu.be/-F4xney2Yjc</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106470"},"PeriodicalIF":5.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of space-filling excitation signals and dynamic models for nonlinear system identification of dynamic processes of a Diesel engine","authors":"Volker Smits ,&nbsp;Christopher Illg ,&nbsp;Hermann Klein ,&nbsp;Oliver Nelles","doi":"10.1016/j.conengprac.2025.106454","DOIUrl":"10.1016/j.conengprac.2025.106454","url":null,"abstract":"<div><div>The paper investigates the influence of design of experiments (DoE) methods in combination with various mathematical architectures for the goal of an accurate Diesel engine identification. Three step-based excitation signals – amplitude-modulated pseudo random binary signal (APRBS), optimized nonlinear input signal (OMNIPUS), and the stacked global optimized amplitude time signal (sGOATS) – are analyzed regarding their space-filling property, a good excitation of all frequencies, and achievable model quality. Six different model architectures are used: a finite impulse response multilayer perceptron (MLP-NFIR), a long short-term memory (LSTM) network, a gated recurrent unit (GRU) network, a local model network with regularized finite impulse response models (LMN-NRFIR), a local model network with auto-regressive with exogenous inputs models (LMN-NARX), and a local model state space network (LMSSN). The model architectures are designed to be suitable for low-performance microcontrollers. As a process, a real-world system (Diesel engine) with high system complexity due to multiple feedback paths, strong nonlinearities, and infeasible regions is chosen, which allows a proper investigation of the data-based methods. The sGOATS achieves the best space-filling property, an even excitation of all frequencies, and provides good model qualities. It is shown that it is worth to optimize the excitation signals in order to get better models. Regarding the model architectures, the LMN-NARX and LMN-NRFIR perform worst, whereas the LSTM, GRU, and LMSSN slightly surpass the MLP-NFIR.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106454"},"PeriodicalIF":5.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safe and time-efficient exploration in Reinforcement Learning-based control of a vehicle thermal systems","authors":"Prasoon Garg ,&nbsp;Emilia Silvas ,&nbsp;Frank Willems","doi":"10.1016/j.conengprac.2025.106458","DOIUrl":"10.1016/j.conengprac.2025.106458","url":null,"abstract":"<div><div>Reinforcement Learning has achieved huge success with various applications in controlled environments. However, limited application is seen in real-world applications due to challenges in guaranteeing safe system operation, required experiment time, and required a-priori system knowledge and models in existing methods. In this work, we propose a novel exploration method, which addresses simultaneously the challenges associated with safe and time-efficient exploration while dealing with system uncertainty. This method integrates a reciprocal Control Barrier Function and an on-line learned Gaussian Process Regression model. For safe system operation, we leverage the information from the reciprocal Control Barrier Function to limit the step size of the agent’s actions, when approaching the safety boundary. To make this exploration process time-efficient, we use the information gain metrics that are calculated using the estimation of the action-values by an on-line learned Gaussian Process Regression model to determine the direction of the agent’s actions. We demonstrate the potential of our exploration method in simulation and on a vehicle test-bench for efficiency-optimal calibration of a thermal management system for battery electric vehicles. To quantify the benefits in terms of safety, optimality, and time efficiency, we benchmark our exploration method with random and uncertainty-driven exploration methods in a simulation environment. For the studied test case, the proposed exploration method satisfies the safety constraint and it converges to within 1.25% of the true optimal action while requiring 28% and 18% lower experiment time compared to the random and uncertainty-driven exploration methods, respectively. For the proposed method, its performance is also demonstrated on a vehicle test bench. Experimental results show that the maximal thermal system efficiency is realized within 2% of the true optimum, while effectively dealing with the safety constraints.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106458"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Holistic adaptive energy-efficient MPC architecture for multi-objective control in over-actuated autonomous vehicles","authors":"Fadel Tarhini ,&nbsp;Reine Talj ,&nbsp;Moustapha Doumiati","doi":"10.1016/j.conengprac.2025.106464","DOIUrl":"10.1016/j.conengprac.2025.106464","url":null,"abstract":"<div><div>This paper presents two novel MPC architectures for autonomous over-actuated in-wheel vehicles, targeting enhanced energy efficiency, stability, and control performance. The first architecture introduces a hierarchical centralized MPC framework that utilizes a minimal-order model to integrate path-tracking, speed control, and stability control objectives. The second architecture extends the hierarchical framework into a holistic MPC design, incorporating direct energy-efficient torque allocation and a tire stability criterion. Energy efficiency is significantly improved by minimizing total power consumption and enforcing operational constraints to maximize motor efficiency. Central to both architectures is a novel multi-criteria adaptive weighting mechanism that dynamically reconciles conflicting objectives by adjusting control priorities based on real-time error magnitudes and driving conditions. This mechanism not only resolves potential conflicts between objectives but also enhances robustness to modeling inaccuracies, uncertainties, disturbances, and variations in road adhesion, while significantly improving control performance. Validation is conducted through joint simulations in Simulink/Matlab and the SCANeR Studio vehicle dynamics simulator. The findings demonstrate that both architectures achieve substantial energy savings while maintaining computational efficiency, with improved stability, comfort, and precision in path-tracking and speed control under challenging conditions, including high-speed, high-curvature, and low-adhesion scenarios.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106464"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust torque-observed control with safe input–output constraints for hydraulic in-wheel drive systems in mobile robots","authors":"Mehdi Heydari Shahna,&nbsp;Pauli Mustalahti,&nbsp;Jouni Mattila","doi":"10.1016/j.conengprac.2025.106459","DOIUrl":"10.1016/j.conengprac.2025.106459","url":null,"abstract":"<div><div>Hydraulic-powered in-wheel drive (IWD) mechanisms enhance the maneuverability, traction, and maintenance efficiency of heavy-duty wheeled mobile robots (HWMRs) by enabling independent operation of each wheel. Sufficient motion in such HWMR systems relies on a multi-stage power transmission mechanism that integrates control valves, hydraulic motors, gearboxes, and, ultimately, nonlinear ground-interaction wheel dynamics on rough terrain. Deviations in each stage of these independently operated wheel systems—arising from modeling uncertainties and disturbances such as wheel slippage and uneven torque distribution on rough terrain—can disrupt motion balance between wheels and further amplify deviations. This can lead the robot to deviate from its course, oscillate, or lose traction, ultimately resulting in overall instability, which may pose a risk to the heavy-weight robot’s surrounding environment. To develop a synchronous control strategy for distributed HWMR systems to mitigate such challenges in uncertain environments, this paper proposes a novel robust torque-observer-based valve control (RTOVC) framework for IWD-actuated wheels, guaranteeing robustness and uniformly exponential stability of the entire system. As a foundation for this approach, a robust torque observer network based on an adaptive barrier Lyapunov function (BLF) is designed to obtain the required wheel/motor torques, ensuring that the actual velocities of IWD-actuated wheels align with the reference values in motion dynamic frames in the presence of wheel slippages. It eliminates the closed-loop dependency on fault-prone torque or pressure sensors in hydraulic actuation mechanisms. Building on this, an additional adaptive BLF-based control network in the valve-actuated hydraulic mechanism is employed to regulate fluid flow, generating the required torque in the first network for each wheel under system uncertainties. The RTOVC framework reduces fault risks in HWMRs by constraining key input–output signals—such as valve control signals, actual wheel velocities, tracking errors, and required motor/wheel torques—within logarithmic BLFs, ensuring safe operation. A comprehensive experimental analysis on a 6,500-kg hydraulic-powered IWD-actuated HWMR operating on rough terrain, where failures may arise due to severe slipping conditions and hydraulic system uncertainties, confirms the RTOVC’s robust performance compared with two other state-of-the-art control strategies.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106459"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A temporal-aware dual-attention network for fault diagnosis in industrial processes","authors":"Tongkang Zhang ,&nbsp;Yongchao Zhang ,&nbsp;Chun Li ,&nbsp;Datong Li ,&nbsp;Jinliang Ding","doi":"10.1016/j.conengprac.2025.106468","DOIUrl":"10.1016/j.conengprac.2025.106468","url":null,"abstract":"<div><div>In recent years, deep learning approaches based on attention mechanisms have been extensively applied in the fault detection and diagnosis of complex industrial processes. However, existing methods typically extract features from process data at the time-point level, which makes it challenging to capture the long-term temporal features of series-level fault information. Therefore, this paper proposes a novel industrial fault diagnosis method based on the temporal-aware dual-attention network (TDANet). The temporal semantic features are extracted from inter-series and intra-series variations through the temporal second-order attention mechanism. The multi-series feature fusion achieves coarse-to-fine feature fusion, enhancing the temporal representation of fault information. Experimental results on the benchmark and real-world datasets demonstrate that TDANet outperforms comparative models in terms of accuracy and F1 score, thereby validating the effectiveness of the proposed method.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106468"},"PeriodicalIF":5.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergency braking control for commercial electric vehicles with electric-pneumatic composite system based on federated adhesion estimation","authors":"Zhineng Long ,&nbsp;Huiyuan Xiong ,&nbsp;Minhao Liu ,&nbsp;Junzhi Zhang","doi":"10.1016/j.conengprac.2025.106462","DOIUrl":"10.1016/j.conengprac.2025.106462","url":null,"abstract":"<div><div>The performance of composite braking in emergencies is critical to the safety of commercial electric vehicles (CEVs). This study tackles challenges in enhancing braking performance, including road adhesion variations, brake response differences, and real-time computational demands. Firstly, an event-triggered federated extended Kalman filter (ET-FEKF) is proposed to estimate relative adhesion and essential vehicle state parameters. The event-triggered mechanism primarily selects more appropriate estimation models based on vehicle states, rather than reducing computation frequency. Meanwhile, the federated structure integrates estimates from each sensor. Secondly, a nonlinear model incorporating the hysteresis effect of the electric-pneumatic composite braking system is developed. Based on this model, a torque control method using the corrective regulation periodic predictive controller (CRPPC) is introduced to improve emergency braking performance. This approach reduces computational load and enhances energy regenerative efficiency, thereby mitigating brake wear. Finally, the proposed method is validated through hardware-in-the-loop (HIL) and vehicle-in-the-loop (VIL) testing across various scenarios. Results demonstrate that, compared to baseline methods, the proposed strategy decreases computational burden, increases mean fully developed deceleration (MFDD) by 6.36%, enhances torque reduction control actions by 53.6%, and improves energy regenerative efficiency by 77.4%.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106462"},"PeriodicalIF":5.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel non-precise vehicle speed-based acceleration slip regulation scheme for distributed drive electric vehicles","authors":"Xin Bai ,&nbsp;Ruiqi Fang ,&nbsp;Shuo Bai ,&nbsp;Jingyu Hu ,&nbsp;Mingzhuo Zhao ,&nbsp;Jinhao Liang ,&nbsp;Xiaoyuan Zhu ,&nbsp;Guodong Yin","doi":"10.1016/j.conengprac.2025.106453","DOIUrl":"10.1016/j.conengprac.2025.106453","url":null,"abstract":"<div><div>Distributed drive electric vehicles (DDEVs) are at the forefront of a transformative shift in the electric vehicle industry. The acceleration slip regulation (ASR) system, a specialized dynamic function within the DDEV application layer, plays a crucial role in ensuring vehicle stability at extreme handling limits. However, its practical implementation faces two main challenges: the high cost of accurate vehicle speed measurement and the real-time reference slip ratio identification. To address these issues, this paper proposes a non-precise vehicle speed-based ASR scheme (NPVS-ASRS). The NPVS-ASRS adopts a cascade structure, with the outer loop controlling driving force and the inner loop controlling wheel speed. A wheel speed limiter is implemented between the outer and inner control loops to prevent slip, with vehicle speed used solely to constrain wheel speed boundaries. Additionally, the paper presents a graphical method for conveniently verifying the stability of the NPVS-ASRS using the circle criterion. Finally, a real-time and efficient reference slip ratio identification method is designed based on the graphical characteristics of the curve model map for various road types. Simulation and road test results demonstrate the robustness and adaptability of the proposed NPVS-ASRS in handling complex road conditions and external disturbances.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106453"},"PeriodicalIF":5.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}