Pub Date : 2026-02-09DOI: 10.1016/j.conengprac.2026.106815
Bikang Hua , Zhaofeng Du , Tianhao Liu , Runqi Chai , Jiping Xu , Senchun Chai
This paper presents and validates an efficient dual-layer trajectory planning framework for the dynamic environment. The upper-layer utilizes offline global planning to generate an optimal global trajectory in a static obstacle environment, while the lower-layer performs online local planning, enabling real-time obstacle avoidance by predicting the future states of the dynamic obstacle and employing appropriate avoidance strategies. The main novelty lies in the following aspects: firstly, a fault-tolerant dynamic obstacle avoidance strategy, along with an LSTM-based trajectory prediction network, enables flexible velocity planning or local trajectory replanning based on the situation; secondly, a convex optimization-based parallel stitching strategy for local trajectory replanning, where candidate trajectories are generated through parallel computation and the optimal stitching solution is greedily selected. During the parallel problem-solving process, the original problem is transformed into a convex optimization problem via linearization and convexification to enhance solution efficiency. Iterative numerical solving is applied, with Line Search steps introduced between iterations to reduce deviation from original constraints and further minimize approximation errors. Simulation and experimental results validate the effectiveness and practicality of the proposed framework.
{"title":"Convex optimization-based parallel trajectory stitching in dynamic environments: A dual-layer trajectory planning framework","authors":"Bikang Hua , Zhaofeng Du , Tianhao Liu , Runqi Chai , Jiping Xu , Senchun Chai","doi":"10.1016/j.conengprac.2026.106815","DOIUrl":"10.1016/j.conengprac.2026.106815","url":null,"abstract":"<div><div>This paper presents and validates an efficient dual-layer trajectory planning framework for the dynamic environment. The upper-layer utilizes offline global planning to generate an optimal global trajectory in a static obstacle environment, while the lower-layer performs online local planning, enabling real-time obstacle avoidance by predicting the future states of the dynamic obstacle and employing appropriate avoidance strategies. The main novelty lies in the following aspects: firstly, a fault-tolerant dynamic obstacle avoidance strategy, along with an LSTM-based trajectory prediction network, enables flexible velocity planning or local trajectory replanning based on the situation; secondly, a convex optimization-based parallel stitching strategy for local trajectory replanning, where candidate trajectories are generated through parallel computation and the optimal stitching solution is greedily selected. During the parallel problem-solving process, the original problem is transformed into a convex optimization problem via linearization and convexification to enhance solution efficiency. Iterative numerical solving is applied, with Line Search steps introduced between iterations to reduce deviation from original constraints and further minimize approximation errors. Simulation and experimental results validate the effectiveness and practicality of the proposed framework.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106815"},"PeriodicalIF":4.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191321","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}
Pub Date : 2026-02-09DOI: 10.1016/j.conengprac.2026.106822
Jie Hu , Fan Yang , Hongxiang Li , Zixin Huang , Witold Pedrycz
Iron ore sintering is governed by highly nonlinear dynamics and multiple operating conditions. The data of the process exhibits characteristics such as noise and imbalance. Traditional clustering methods, such as fuzzy C-means (FCM), often struggle to handle issues such as noise, data imbalance, and fuzzy clustering boundaries. This paper presents a multi-stage clustering framework that unifies information-granule modeling, intelligent optimization, and fuzzy clustering. Unlike conventional point-based methods, rectangular information granules are explicitly constructed to capture boundary uncertainty and subsequently optimized by particle swarm optimization, providing interpretable and adaptive data abstractions. A new weighted fuzzy-granule distance is then introduced to cluster the optimized granules, enabling precise partitions even in overlapping or weak-boundary regions. By replacing sensitive centroid estimates under noise with interpretable granules, the framework improves transitions, reduces membership ambiguity, and improves robustness. The method was benchmarked on six public UCI datasets and real-world production data of sintering process coming from an integrated steelworks. Against FCM, kernel-based FCM, spectral clustering and clustering algorithm based on bidirectional conical information granularity (CBCG), the proposed method achieved the highest average accuracy, Fowlkes-Mallows index, and rand index while retaining competitive rand index values. The main contributions of this paper are as follows: A novel industrial condition identification framework based on rectangular information granules is proposed, where rectangular information granularity is employed to replace the traditional point-based cluster center representation. A robust clustering framework combining weighted granule distance and fuzzy clustering is developed, enhancing clustering accuracy. On real-world production dataset, the proposed method improves accuracy by 22% compared to CBCG and achieves a 5.5% accuracy improvement over the field application method FCM.
{"title":"A multi-condition fuzzy clustering method based on rectangular information granules and its application to an industry process","authors":"Jie Hu , Fan Yang , Hongxiang Li , Zixin Huang , Witold Pedrycz","doi":"10.1016/j.conengprac.2026.106822","DOIUrl":"10.1016/j.conengprac.2026.106822","url":null,"abstract":"<div><div>Iron ore sintering is governed by highly nonlinear dynamics and multiple operating conditions. The data of the process exhibits characteristics such as noise and imbalance. Traditional clustering methods, such as fuzzy C-means (FCM), often struggle to handle issues such as noise, data imbalance, and fuzzy clustering boundaries. This paper presents a multi-stage clustering framework that unifies information-granule modeling, intelligent optimization, and fuzzy clustering. Unlike conventional point-based methods, rectangular information granules are explicitly constructed to capture boundary uncertainty and subsequently optimized by particle swarm optimization, providing interpretable and adaptive data abstractions. A new weighted fuzzy-granule distance is then introduced to cluster the optimized granules, enabling precise partitions even in overlapping or weak-boundary regions. By replacing sensitive centroid estimates under noise with interpretable granules, the framework improves transitions, reduces membership ambiguity, and improves robustness. The method was benchmarked on six public UCI datasets and real-world production data of sintering process coming from an integrated steelworks. Against FCM, kernel-based FCM, spectral clustering and clustering algorithm based on bidirectional conical information granularity (CBCG), the proposed method achieved the highest average accuracy, Fowlkes-Mallows index, and rand index while retaining competitive rand index values. The main contributions of this paper are as follows: A novel industrial condition identification framework based on rectangular information granules is proposed, where rectangular information granularity is employed to replace the traditional point-based cluster center representation. A robust clustering framework combining weighted granule distance and fuzzy clustering is developed, enhancing clustering accuracy. On real-world production dataset, the proposed method improves accuracy by 22% compared to CBCG and achieves a 5.5% accuracy improvement over the field application method FCM.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106822"},"PeriodicalIF":4.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191313","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}
Pub Date : 2026-02-06DOI: 10.1016/j.conengprac.2026.106821
Marco Herrera , Oscar Camacho , Alvaro Javier Prado-Romo
This paper introduces a novel control approach called Model-Free Dynamic Sliding Mode Control with an Iterated-Integral Sliding Surface (MF-DSMC), designed to improve the control performance of chemical processes characterized by nonlinear dynamics, time-varying behavior, and system uncertainties. The proposed method integrates the robustness of Sliding Mode Control (SMC) with the adaptability of Model-Free Control (MFC), using an iterated-integral sliding surface to enhance transient response. A data-driven ultralocal model is employed in real time to estimate the system’s behavior and adapt the switching gain dynamically. This reduces the dependence on fixed parameters and helps to mitigate chattering effects. The MF-DSMC strategy is validated through two thermal process case studies: (i) a simulated mixing tank with long and varying time delay, and (ii) an experimental setup using the Temperature Control Laboratory (TCLab) platform. The simulation assesses the controller’s robustness and reference tracking under nonlinear and delayed conditions. The experimental setup evaluates its real-time performance. The results show that MF-DSMC achieves improved precision, adaptability, and robustness compared to conventional control methods. In particular, during the TCLab experiments, MF-DSMC obtained the better performance indices in terms of Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Total Variation of the control signal (TVu), outperforming a classical Model-Free Proportional-Integral (MF-PI) controller.
{"title":"A model-free dynamic sliding mode control approach with iterated-integral terms on sliding surface for chemical processes","authors":"Marco Herrera , Oscar Camacho , Alvaro Javier Prado-Romo","doi":"10.1016/j.conengprac.2026.106821","DOIUrl":"10.1016/j.conengprac.2026.106821","url":null,"abstract":"<div><div>This paper introduces a novel control approach called Model-Free Dynamic Sliding Mode Control with an Iterated-Integral Sliding Surface (MF-DSMC), designed to improve the control performance of chemical processes characterized by nonlinear dynamics, time-varying behavior, and system uncertainties. The proposed method integrates the robustness of Sliding Mode Control (SMC) with the adaptability of Model-Free Control (MFC), using an iterated-integral sliding surface to enhance transient response. A data-driven ultralocal model is employed in real time to estimate the system’s behavior and adapt the switching gain dynamically. This reduces the dependence on fixed parameters and helps to mitigate chattering effects. The MF-DSMC strategy is validated through two thermal process case studies: (i) a simulated mixing tank with long and varying time delay, and (ii) an experimental setup using the Temperature Control Laboratory (TCLab) platform. The simulation assesses the controller’s robustness and reference tracking under nonlinear and delayed conditions. The experimental setup evaluates its real-time performance. The results show that MF-DSMC achieves improved precision, adaptability, and robustness compared to conventional control methods. In particular, during the TCLab experiments, MF-DSMC obtained the better performance indices in terms of Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Total Variation of the control signal (TVu), outperforming a classical Model-Free Proportional-Integral (MF-PI) controller.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106821"},"PeriodicalIF":4.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191314","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}
Pub Date : 2026-02-05DOI: 10.1016/j.conengprac.2026.106819
Wenlong Lin , Wanquan Liu , Xiangxiang Zou , Deshan Meng , Hui-Jie Sun , Ping Wang , Muhammad Imran
In this paper, the fully-actuated model predictive control for the trajectory tracking of cable-driven manipulators is investigated. For the dynamic system of a cable-driven manipulator, the fully-actuated property is derived. Then, a novel trajectory tracking control law is designed by combining model predictive control technique and the fully-actuated system approach. The developed fully-actuated controller reduces the computational burden of predictive control for the cable-driven manipulator system, increases the response speed, and improves the performance of vibration suppression. The asymptotical stability of the closed-loop system is proven via the Lyapunov stability theory. Simulations and experiments are carried out to demonstrate the effectiveness and superiority of the proposed control method, highlighting the advantages of the fully-actuated controller in computational efficiency and vibration suppression.
{"title":"A novel model predictive control law for over-actuated space cable-driven manipulators","authors":"Wenlong Lin , Wanquan Liu , Xiangxiang Zou , Deshan Meng , Hui-Jie Sun , Ping Wang , Muhammad Imran","doi":"10.1016/j.conengprac.2026.106819","DOIUrl":"10.1016/j.conengprac.2026.106819","url":null,"abstract":"<div><div>In this paper, the fully-actuated model predictive control for the trajectory tracking of cable-driven manipulators is investigated. For the dynamic system of a cable-driven manipulator, the fully-actuated property is derived. Then, a novel trajectory tracking control law is designed by combining model predictive control technique and the fully-actuated system approach. The developed fully-actuated controller reduces the computational burden of predictive control for the cable-driven manipulator system, increases the response speed, and improves the performance of vibration suppression. The asymptotical stability of the closed-loop system is proven via the Lyapunov stability theory. Simulations and experiments are carried out to demonstrate the effectiveness and superiority of the proposed control method, highlighting the advantages of the fully-actuated controller in computational efficiency and vibration suppression.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106819"},"PeriodicalIF":4.6,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191315","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}
Pub Date : 2026-02-05DOI: 10.1016/j.conengprac.2026.106794
Karim Nadim , Ahmed Ragab , Hakim Ghezzaz
Industrial plants are equipped with several local controllers with a high degree of interaction. Controllers in complex systems tend to operate in a competitive way to achieve their own objective, which can negatively impact other controllers and consequently the global KPI. In addition, the rapid changes in process dynamics, the variations, and fluctuations in the process conditions and production targets introduce major challenges in optimizing the whole process. As a result, operators struggle to adjust the controllers’ setpoints to optimize the process operation. Therefore, there is a clear need for an approach that captures the controllers’ interdependence and optimizes the setpoints in real-time to ensure energy-efficient operations. This paper proposes an intelligent decentralized supervisory control approach based on multi-agent deep reinforcement learning (MADRL) to recommend the optimal combinations of controllers’ setpoints that maintain desired process operation. Multiple agents are developed based on the deep deterministic policy gradient algorithm to collaborate and control different interconnected subsystems. The agents are trained via interacting with a process simulation, where each agent performs actions (setpoint changes) and observes certain rewards (global KPI to be maximized) and states (measured variables) from the simulation. The approach is validated on a case study based on a heat recovery network of a thermomechanical pulp mill comprising four different subsystems. The proposed decentralized approach was compared to two centralized approaches: a baseline control set by the process expert and a single DDPG agent. The multi-agent approach was able to reduce the steam flow consumption by 6.7 % compared to the experts’ baseline and 5.3% compared to the single agent with faster convergence. Two possible strategies were proposed to implement this approach in the industry, depending on the criticality of the process and the degree of fidelity of its process simulation.
{"title":"Multi agent deep reinforcement learning for supervising local controllers in energy-intensive industrial processes","authors":"Karim Nadim , Ahmed Ragab , Hakim Ghezzaz","doi":"10.1016/j.conengprac.2026.106794","DOIUrl":"10.1016/j.conengprac.2026.106794","url":null,"abstract":"<div><div>Industrial plants are equipped with several local controllers with a high degree of interaction. Controllers in complex systems tend to operate in a competitive way to achieve their own objective, which can negatively impact other controllers and consequently the global KPI. In addition, the rapid changes in process dynamics, the variations, and fluctuations in the process conditions and production targets introduce major challenges in optimizing the whole process. As a result, operators struggle to adjust the controllers’ setpoints to optimize the process operation. Therefore, there is a clear need for an approach that captures the controllers’ interdependence and optimizes the setpoints in real-time to ensure energy-efficient operations. This paper proposes an intelligent decentralized supervisory control approach based on multi-agent deep reinforcement learning (MADRL) to recommend the optimal combinations of controllers’ setpoints that maintain desired process operation. Multiple agents are developed based on the deep deterministic policy gradient algorithm to collaborate and control different interconnected subsystems. The agents are trained via interacting with a process simulation, where each agent performs actions (setpoint changes) and observes certain rewards (global KPI to be maximized) and states (measured variables) from the simulation. The approach is validated on a case study based on a heat recovery network of a thermomechanical pulp mill comprising four different subsystems. The proposed decentralized approach was compared to two centralized approaches: a baseline control set by the process expert and a single DDPG agent. The multi-agent approach was able to reduce the steam flow consumption by 6.7 % compared to the experts’ baseline and 5.3% compared to the single agent with faster convergence. Two possible strategies were proposed to implement this approach in the industry, depending on the criticality of the process and the degree of fidelity of its process simulation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106794"},"PeriodicalIF":4.6,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191320","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}
Pub Date : 2026-02-03DOI: 10.1016/j.conengprac.2026.106810
Hussain Ahmed, Matti Vilkko
Transitioning district heating in energy communities (ECs) from fossil-fuel to electrification requires advanced analytical tools for long-term assessment and informed decision-making. This paper proposes two novel mixed-integer linear programming models for a Finnish EC with diverse power-generating and storage units, with gas boiler and electric boiler configurations to promote sector-coupling. Both models are simulated over a year using operational data to compare operating costs, carbon emissions, and reliance on local renewable electricity generation. Results show that the electric boilers configuration significantly reduces emissions, lowers costs, and improves local renewable energy utilization, highlighting the benefits of electrifying ECs for future sustainability.
{"title":"Formulation of MILP models to assess the techno-economic impact of district heating electrification in energy communities","authors":"Hussain Ahmed, Matti Vilkko","doi":"10.1016/j.conengprac.2026.106810","DOIUrl":"10.1016/j.conengprac.2026.106810","url":null,"abstract":"<div><div>Transitioning district heating in energy communities (ECs) from fossil-fuel to electrification requires advanced analytical tools for long-term assessment and informed decision-making. This paper proposes two novel mixed-integer linear programming models for a Finnish EC with diverse power-generating and storage units, with gas boiler and electric boiler configurations to promote sector-coupling. Both models are simulated over a year using operational data to compare operating costs, carbon emissions, and reliance on local renewable electricity generation. Results show that the electric boilers configuration significantly reduces emissions, lowers costs, and improves local renewable energy utilization, highlighting the benefits of electrifying ECs for future sustainability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106810"},"PeriodicalIF":4.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191312","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}
Pub Date : 2026-02-02DOI: 10.1016/j.conengprac.2026.106816
Yunpeng Guo , Qifu Chen , Jianqi An , Zhuang Li , Jinhua She
As the smelting intensity (SI) affects both the chemical reactions and physical changes inside blast furnaces (BF), the relationship between the gas utilization rate (GUR) and blast supply parameters varies according to different levels of SI. This paper introduces a GUR prediction model that considers SI classification information. First, the impact of SI on the state parameters of a BF is evaluated from the perspective of the molten iron smelting mechanism. Then, a fuzzy C-means clustering method (FCM) is presented to classify SI based on state parameters. Subsequently, an SI-aware GUR prediction model is constructed using principal component analysis (PCA) and an extreme learning machine (ELM) to predict GUR development trends. Finally, the model is used to predict real-world GUR data under different SI levels. Analysis of real-world production data shows that the proposed method accurately predicts GUR and outperforms methods that do not account for SI classification.
{"title":"Prediction model of the gas utilization rate in a blast furnace considering smelting intensity classification","authors":"Yunpeng Guo , Qifu Chen , Jianqi An , Zhuang Li , Jinhua She","doi":"10.1016/j.conengprac.2026.106816","DOIUrl":"10.1016/j.conengprac.2026.106816","url":null,"abstract":"<div><div>As the smelting intensity (SI) affects both the chemical reactions and physical changes inside blast furnaces (BF), the relationship between the gas utilization rate (GUR) and blast supply parameters varies according to different levels of SI. This paper introduces a GUR prediction model that considers SI classification information. First, the impact of SI on the state parameters of a BF is evaluated from the perspective of the molten iron smelting mechanism. Then, a fuzzy C-means clustering method (FCM) is presented to classify SI based on state parameters. Subsequently, an SI-aware GUR prediction model is constructed using principal component analysis (PCA) and an extreme learning machine (ELM) to predict GUR development trends. Finally, the model is used to predict real-world GUR data under different SI levels. Analysis of real-world production data shows that the proposed method accurately predicts GUR and outperforms methods that do not account for SI classification.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106816"},"PeriodicalIF":4.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191318","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}
Wireless sensor networks (WSNs) are an important component of cyber-physical systems (CPS). They consist of low-resource, low-power nodes that perform local measurement and communication. While the CPS framework relies on complex computation and optimization over a network, this approach is not feasible in remote areas lacking stable network infrastructure. Under such situations, computation and optimization must be executed locally where the WSN nodes are installed. Using the IoT devices of the WSN nodes for performing computation and optimization is a possible approach, but the limited computational resources of these devices make it impossible to implement advanced control laws, such as Model Predictive Control (MPC). One solution for this is to decompose a single MPC problem into small subproblems, each of which is implemented on a low resource IoT device. In this paper, we focus on a parallel MPC method and derive an analytic form of unconstrained solutions of subproblems. With this derivation, we propose improved implementation algorithms of the parallel MPC for multiple low-resource IoT devices. Compared to existing time-splitting parallel MPC schemes, our parallel MPC scheme requires a smaller amount of data exchanged among subproblems, making it suitable for implementations using wireless communication capabilities of low-resource IoT devices. The proposed method is experimentally validated using multiple micro:bits with wireless communications.
{"title":"Experimental validation of parallel model predictive control on multiple low-resource IoT devices","authors":"Shunta Yamamoto, Naoyuki Hara, Keiji Konishi, Yoshiki Sugitani","doi":"10.1016/j.conengprac.2026.106818","DOIUrl":"10.1016/j.conengprac.2026.106818","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs) are an important component of cyber-physical systems (CPS). They consist of low-resource, low-power nodes that perform local measurement and communication. While the CPS framework relies on complex computation and optimization over a network, this approach is not feasible in remote areas lacking stable network infrastructure. Under such situations, computation and optimization must be executed locally where the WSN nodes are installed. Using the IoT devices of the WSN nodes for performing computation and optimization is a possible approach, but the limited computational resources of these devices make it impossible to implement advanced control laws, such as Model Predictive Control (MPC). One solution for this is to decompose a single MPC problem into small subproblems, each of which is implemented on a low resource IoT device. In this paper, we focus on a parallel MPC method and derive an analytic form of unconstrained solutions of subproblems. With this derivation, we propose improved implementation algorithms of the parallel MPC for multiple low-resource IoT devices. Compared to existing time-splitting parallel MPC schemes, our parallel MPC scheme requires a smaller amount of data exchanged among subproblems, making it suitable for implementations using wireless communication capabilities of low-resource IoT devices. The proposed method is experimentally validated using multiple micro:bits with wireless communications.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106818"},"PeriodicalIF":4.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191309","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}
Pub Date : 2026-01-31DOI: 10.1016/j.conengprac.2026.106817
Carlos Conejo , Vicenç Puig , Bernardo Morcego
Accurate vehicle models are essential for prediction and control in automated driving. Physics-based approaches provide interpretability and accuracy when system parameters are well captured, while data-driven models adapt to unmodeled dynamics at the cost of interpretability. This paper presents a systematic reachability-based benchmark comparing both approaches on a real autonomous platform. Models are evaluated under nominal, predefined safe state, and unexpected malfunction scenarios using accuracy, computational, and safety metrics. Results show that physics-based models excel in foreseen conditions, whereas data-driven models remain robust under unexpected disturbances. The study provides practical guidelines for model selection in safety-critical applications and motivates future hybrid strategies that combine accuracy with adaptability.
{"title":"Reachability -based benchmarking of physics-based and data-driven models for automated driving","authors":"Carlos Conejo , Vicenç Puig , Bernardo Morcego","doi":"10.1016/j.conengprac.2026.106817","DOIUrl":"10.1016/j.conengprac.2026.106817","url":null,"abstract":"<div><div>Accurate vehicle models are essential for prediction and control in automated driving. Physics-based approaches provide interpretability and accuracy when system parameters are well captured, while data-driven models adapt to unmodeled dynamics at the cost of interpretability. This paper presents a systematic reachability-based benchmark comparing both approaches on a real autonomous platform. Models are evaluated under nominal, predefined safe state, and unexpected malfunction scenarios using accuracy, computational, and safety metrics. Results show that physics-based models excel in foreseen conditions, whereas data-driven models remain robust under unexpected disturbances. The study provides practical guidelines for model selection in safety-critical applications and motivates future hybrid strategies that combine accuracy with adaptability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106817"},"PeriodicalIF":4.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191310","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}
This paper quantitatively compares and evaluates fault-tolerant control strategies to ensure vehicle-level safety under complete steer-by-wire (SbW) fault conditions. Previous studies were often limited to specific failure types or lacked systematic strategy comparisons, making it difficult to clearly identify their applicability and limitations. In this study, a unified fault-tolerant control framework addressing two types of complete SbW failures-fixed steering angle (FSA) and loss of steering torque (LST)–was developed. Within this framework, various strategies including rear-wheel steering (RWS), torque vectoring (TV), and their combinations were implemented, and performance was analyzed using standard evaluation scenarios: slow-ramp, sine-sweep, step, and 1-period sine steer. Simulation results indicate that under FSA failure, RWS-based strategies are relatively effective, with all strategies achieving near-nominal vehicle performance at high speeds. In contrast, LST failure leads to significant performance degradation due to unintended front-wheel steering, making nominal-level cornering unattainable. RWS-only control exhibits severe limitations, while partial compensation is achieved when combined with TV, demonstrating the benefit of multi-actuator coordination under fault conditions. These findings were further validated through real-vehicle tests, confirming the practical applicability of the proposed SbW fault-tolerant controller. By systematically comparing multiple strategies across both FSA and LST failure types under complete SbW conditions, the study provides fundamental insights for designing fault-tolerant controllers that account for failure-specific characteristics, establishing a foundation for future real-vehicle implementation and application research.
{"title":"Comparative study of fault-tolerant control strategies for complete steer-by-wire failures","authors":"Yunchul Ha , Seunguk Jeon , Aldo Sorniotti , Seunghoon Woo","doi":"10.1016/j.conengprac.2026.106812","DOIUrl":"10.1016/j.conengprac.2026.106812","url":null,"abstract":"<div><div>This paper quantitatively compares and evaluates fault-tolerant control strategies to ensure vehicle-level safety under complete steer-by-wire (SbW) fault conditions. Previous studies were often limited to specific failure types or lacked systematic strategy comparisons, making it difficult to clearly identify their applicability and limitations. In this study, a unified fault-tolerant control framework addressing two types of complete SbW failures-fixed steering angle (FSA) and loss of steering torque (LST)–was developed. Within this framework, various strategies including rear-wheel steering (RWS), torque vectoring (TV), and their combinations were implemented, and performance was analyzed using standard evaluation scenarios: slow-ramp, sine-sweep, step, and 1-period sine steer. Simulation results indicate that under FSA failure, RWS-based strategies are relatively effective, with all strategies achieving near-nominal vehicle performance at high speeds. In contrast, LST failure leads to significant performance degradation due to unintended front-wheel steering, making nominal-level cornering unattainable. RWS-only control exhibits severe limitations, while partial compensation is achieved when combined with TV, demonstrating the benefit of multi-actuator coordination under fault conditions. These findings were further validated through real-vehicle tests, confirming the practical applicability of the proposed SbW fault-tolerant controller. By systematically comparing multiple strategies across both FSA and LST failure types under complete SbW conditions, the study provides fundamental insights for designing fault-tolerant controllers that account for failure-specific characteristics, establishing a foundation for future real-vehicle implementation and application research.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"170 ","pages":"Article 106812"},"PeriodicalIF":4.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081621","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}
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