Pub Date : 2026-04-01Epub Date: 2026-01-29DOI: 10.1016/j.conengprac.2026.106793
Chuang Chen , Weifeng Liu , Meng Zhou , Lei Cai
In large-scale unknown water surface environment exploration, hierarchical exploration methods are an effective way to reduce computational overhead. However, existing hierarchical exploration methods suffer from low trajectory quality and poor feasibility, leading to low autonomous exploration efficiency of USVs (Unmanned Surface Vehicles). Therefore, this paper proposes a deep reinforcement learning exploration method based on motion cost rewards. This method jointly optimizes the decision-making process and motion planning. The motion cost of each trajectory segment of the USV is calculated using an analytical method, enabling the policy network to take into account both exploration efficiency and trajectory feasibility during the decision-making process. Finally, nonlinear model predictive control (NMPC) is used for trajectory tracking control. Simulation and real-world experimental results show that the proposed method achieves better performance in terms of exploration efficiency and path cost.
{"title":"A deep reinforcement learning exploration method based on motion cost rewards","authors":"Chuang Chen , Weifeng Liu , Meng Zhou , Lei Cai","doi":"10.1016/j.conengprac.2026.106793","DOIUrl":"10.1016/j.conengprac.2026.106793","url":null,"abstract":"<div><div>In large-scale unknown water surface environment exploration, hierarchical exploration methods are an effective way to reduce computational overhead. However, existing hierarchical exploration methods suffer from low trajectory quality and poor feasibility, leading to low autonomous exploration efficiency of USVs (Unmanned Surface Vehicles). Therefore, this paper proposes a deep reinforcement learning exploration method based on motion cost rewards. This method jointly optimizes the decision-making process and motion planning. The motion cost of each trajectory segment of the USV is calculated using an analytical method, enabling the policy network to take into account both exploration efficiency and trajectory feasibility during the decision-making process. Finally, nonlinear model predictive control (NMPC) is used for trajectory tracking control. Simulation and real-world experimental results show that the proposed method achieves better performance in terms of exploration efficiency and path cost.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106793"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078361","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 : 2026-04-01Epub Date: 2026-01-28DOI: 10.1016/j.conengprac.2026.106796
Liu Zhan , Xiaowei Xu , Zian Bai , Xiaofeng Guo , Mingxing Deng , Yingxue Zou
Aiming at the deterioration of ride comfort caused by uncertain time delay of magnetorheological (MR) damper, a feedforward-feedback collaborative mode is proposed by integrating Long Short-Term Memory (LSTM) and Deep Reinforcement Learning (DRL) to alleviate time delay and optimize damping effect. Firstly, fuzzy Linear Quadratic Regulator algorithm is employed to simulate and control an active suspension to obtain the ideal control state information without time delay, and the LSTM is developed and trained using the ideal state information to establish the prediction model based on ideal experience; Secondly, within the Soft Actor-Critic (SAC), the prediction model is utilized to predict real-time observations, yielding predicted values for next state. Relevant experience is added to replay buffer of DRL, and the reward item of prediction error is introduced to obtain a SAC algorithm with Predictive Experience Guidance (SAC-PEG). Finally, the results of passive suspension, Proximal Policy Optimization, SAC, Twin Delayed Deep Deterministic Policy Gradient and SAC-PEG are compared by simulations and bench experiments. The simulations demonstrate that body acceleration controlled by SAC-PEG is 25.52 % lower than that of passive suspension, and suspension working space and tire dynamic load are increased by 90.59 % and 66.35 %; Compared with SAC, when suspension working space and tire dynamic load are only deteriorated by 7.956 % and 5.440 %, body acceleration is optimized by 4.143 %. Bench experiment also achieved satisfactory results. The results validated that SAC-PEG has better mitigation effect on uncertain time delay than other comparative methods, and can improve the smoothness problem caused by uncertain time delay.
{"title":"Intelligent compensation for uncertain time delay in vehicle magnetorheological suspension control using predictive experience","authors":"Liu Zhan , Xiaowei Xu , Zian Bai , Xiaofeng Guo , Mingxing Deng , Yingxue Zou","doi":"10.1016/j.conengprac.2026.106796","DOIUrl":"10.1016/j.conengprac.2026.106796","url":null,"abstract":"<div><div>Aiming at the deterioration of ride comfort caused by uncertain time delay of magnetorheological (MR) damper, a feedforward-feedback collaborative mode is proposed by integrating Long Short-Term Memory (LSTM) and Deep Reinforcement Learning (DRL) to alleviate time delay and optimize damping effect. Firstly, fuzzy Linear Quadratic Regulator algorithm is employed to simulate and control an active suspension to obtain the ideal control state information without time delay, and the LSTM is developed and trained using the ideal state information to establish the prediction model based on ideal experience; Secondly, within the Soft Actor-Critic (SAC), the prediction model is utilized to predict real-time observations, yielding predicted values for next state. Relevant experience is added to replay buffer of DRL, and the reward item of prediction error is introduced to obtain a SAC algorithm with Predictive Experience Guidance (SAC-PEG). Finally, the results of passive suspension, Proximal Policy Optimization, SAC, Twin Delayed Deep Deterministic Policy Gradient and SAC-PEG are compared by simulations and bench experiments. The simulations demonstrate that body acceleration controlled by SAC-PEG is 25.52 % lower than that of passive suspension, and suspension working space and tire dynamic load are increased by 90.59 % and 66.35 %; Compared with SAC, when suspension working space and tire dynamic load are only deteriorated by 7.956 % and 5.440 %, body acceleration is optimized by 4.143 %. Bench experiment also achieved satisfactory results. The results validated that SAC-PEG has better mitigation effect on uncertain time delay than other comparative methods, and can improve the smoothness problem caused by uncertain time delay.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106796"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078362","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 : 2026-04-01Epub Date: 2026-01-22DOI: 10.1016/j.conengprac.2026.106795
Xiwen Tian , Xiaojia Wang , Jiaqi Lyu , Wenbo Liu , Yusheng Wang , Aobei Shen , Zhifei Wu
Variations in vertical load significantly influence the tire adhesion performance and vehicle stability under dynamic conditions, creating challenges to the efficient control of distributed drive electric vehicles (DDEVs). This paper proposes a coordinated control strategy of DDEVs based on state parameter identification to address the vehicle stability issue under arbitrary conditions. First, based on K-means++ clustering algorithm, the stability state is classified into three categories: stable state, transient stable state, and unstable state. According to the classification results, a hierarchical cooperative control strategy combining active front wheel steering (AFS) and direct yaw moment control (DYC) is developed to dynamically adjust the specific working range and weight distribution of AFS and DYC. The simulation analysis is conducted under double lane change and slalom maneuvers. Finally, the yaw rate of the stability state variable is reduced by 18.6% through experimental test, which further verifies the performance of the proposed coordinated control strategy.
{"title":"Coordinated control strategy for distributed drive electric vehicles based on state parameter identification","authors":"Xiwen Tian , Xiaojia Wang , Jiaqi Lyu , Wenbo Liu , Yusheng Wang , Aobei Shen , Zhifei Wu","doi":"10.1016/j.conengprac.2026.106795","DOIUrl":"10.1016/j.conengprac.2026.106795","url":null,"abstract":"<div><div>Variations in vertical load significantly influence the tire adhesion performance and vehicle stability under dynamic conditions, creating challenges to the efficient control of distributed drive electric vehicles (DDEVs). This paper proposes a coordinated control strategy of DDEVs based on state parameter identification to address the vehicle stability issue under arbitrary conditions. First, based on K-means++ clustering algorithm, the stability state is classified into three categories: stable state, transient stable state, and unstable state. According to the classification results, a hierarchical cooperative control strategy combining active front wheel steering (AFS) and direct yaw moment control (DYC) is developed to dynamically adjust the specific working range and weight distribution of AFS and DYC. The simulation analysis is conducted under double lane change and slalom maneuvers. Finally, the yaw rate of the stability state variable is reduced by 18.6% through experimental test, which further verifies the performance of the proposed coordinated control strategy.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106795"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038363","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 : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.conengprac.2026.106792
Gengchen Liu , Song Gao , Junheng Jiang , Zhangmin Luo , Gang Jiang
Accurate path planning is particularly important for unmanned vehicles in complex mountainous environments. Compared with two-dimensional terrain, mountainous three-dimensional terrain not only introduces more uncertainty but also interference from dynamic obstacles, which dramatically increases the difficulty of path planning. As such, conventional planning methods often struggle to identify efficient solutions. Although path planning techniques utilizing deep reinforcement learning have provided new strategies for solving such problems, existing algorithms face a variety of challenges, including poor network stability, susceptibility to gradient explosion, insufficient reward guidance, and an imbalance between exploration and utilization. To overcome these issues, this paper introduces three novel contributions. First, the dueling double DQN is structurally optimized, and various techniques are introduced to prevent instability and gradient explosion. Second, a new reward function is developed to combine the Bessel hierarchical A* path guidance algorithm with the artificial potential field method, enabling unmanned vehicles to identify the optimal path while dynamically avoiding obstacles. Finally, a chaotic annealing multi-phased strategy is proposed as an action selection policy, which gradually transitions from the exploration stage to the exploitation stage by optimizing the balance between the two as the learning process advances. In addition, a 3D terrain model based on a real mountain environment was generated using the grayscale map algorithm. A series of simulation experiments were conducted to evaluate the performance of the proposed method, as measured by search efficiency, success rate, and path quality. A comparative analysis and comparison with existing DRL path planning algorithms was also performed to provide additional insights.
{"title":"Mountain UGV path planning via optimized dueling double DQN (D3QN): Structural optimization, path-guided rewards, and phased action policy","authors":"Gengchen Liu , Song Gao , Junheng Jiang , Zhangmin Luo , Gang Jiang","doi":"10.1016/j.conengprac.2026.106792","DOIUrl":"10.1016/j.conengprac.2026.106792","url":null,"abstract":"<div><div>Accurate path planning is particularly important for unmanned vehicles in complex mountainous environments. Compared with two-dimensional terrain, mountainous three-dimensional terrain not only introduces more uncertainty but also interference from dynamic obstacles, which dramatically increases the difficulty of path planning. As such, conventional planning methods often struggle to identify efficient solutions. Although path planning techniques utilizing deep reinforcement learning have provided new strategies for solving such problems, existing algorithms face a variety of challenges, including poor network stability, susceptibility to gradient explosion, insufficient reward guidance, and an imbalance between exploration and utilization. To overcome these issues, this paper introduces three novel contributions. First, the dueling double DQN is structurally optimized, and various techniques are introduced to prevent instability and gradient explosion. Second, a new reward function is developed to combine the Bessel hierarchical A* path guidance algorithm with the artificial potential field method, enabling unmanned vehicles to identify the optimal path while dynamically avoiding obstacles. Finally, a chaotic annealing multi-phased strategy is proposed as an action selection policy, which gradually transitions from the exploration stage to the exploitation stage by optimizing the balance between the two as the learning process advances. In addition, a 3D terrain model based on a real mountain environment was generated using the grayscale map algorithm. A series of simulation experiments were conducted to evaluate the performance of the proposed method, as measured by search efficiency, success rate, and path quality. A comparative analysis and comparison with existing DRL path planning algorithms was also performed to provide additional insights.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106792"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038360","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 : 2026-04-01Epub Date: 2026-01-09DOI: 10.1016/j.conengprac.2026.106757
Rami Jradi , Hala Rifaï , José Fermi Guerrero-Castellanos , Samer Mohammed
In this paper, a control strategy for an Actuated Ankle Foot Orthosis (AAFO) is proposed to provide the assistance needed by the wearer at the ankle joint level. The control scheme is based on a contraction-based active disturbance rejection controller (Cont-ADRC). It includes an estimation of the human muscular torque and difficult-to-capture external torques affecting the AAFO-wearer system at the ankle joint level alongside unmodeled dynamics by means of a nonlinear disturbance observer (NDOB). A contraction-based variable gain controller determines the amount of assistance to be provided by the AAFO to perform the movement in complement to the aforementioned muscular torque. The variable gain controller provides a compromise between the low frequency disturbance rejection and the high frequency measurement noise attenuation. Using a contraction-based differential Lyapunov analysis, the trajectories of the AAFO-wearer system subject to the proposed active disturbance rejection controller are proved to be incrementally bounded, which is considered to be a stronger form of boundedness with respect to the uniform one. To demonstrate the efficiency of the Cont-ADRC, it has been applied in real-time experiments with robustness tests, involving three healthy subjects during walking activities. The outcomes revealed its superiority over other ADRCs developed for wearable robotics where it showed improved tracking accuracy compared to PID and Control Lyapunov Functions-based ADRC and reduced computational efforts compared to adaptive-based ADRC.
{"title":"Contraction-based active disturbance rejection controller for an active ankle foot orthosis","authors":"Rami Jradi , Hala Rifaï , José Fermi Guerrero-Castellanos , Samer Mohammed","doi":"10.1016/j.conengprac.2026.106757","DOIUrl":"10.1016/j.conengprac.2026.106757","url":null,"abstract":"<div><div>In this paper, a control strategy for an Actuated Ankle Foot Orthosis (AAFO) is proposed to provide the assistance needed by the wearer at the ankle joint level. The control scheme is based on a contraction-based active disturbance rejection controller (Cont-ADRC). It includes an estimation of the human muscular torque and difficult-to-capture external torques affecting the AAFO-wearer system at the ankle joint level alongside unmodeled dynamics by means of a nonlinear disturbance observer (NDOB). A contraction-based variable gain controller determines the amount of assistance to be provided by the AAFO to perform the movement in complement to the aforementioned muscular torque. The variable gain controller provides a compromise between the low frequency disturbance rejection and the high frequency measurement noise attenuation. Using a contraction-based differential Lyapunov analysis, the trajectories of the AAFO-wearer system subject to the proposed active disturbance rejection controller are proved to be incrementally bounded, which is considered to be a stronger form of boundedness with respect to the uniform one. To demonstrate the efficiency of the Cont-ADRC, it has been applied in real-time experiments with robustness tests, involving three healthy subjects during walking activities. The outcomes revealed its superiority over other ADRCs developed for wearable robotics where it showed improved tracking accuracy compared to PID and Control Lyapunov Functions-based ADRC and reduced computational efforts compared to adaptive-based ADRC.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106757"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928904","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 : 2026-04-01Epub Date: 2026-01-16DOI: 10.1016/j.conengprac.2026.106767
John Cortés-Romero , Horacio Coral-Enriquez , Brian Camilo Gómez-León , Hebertt Sira-Ramírez
Implementable applications are often compromised by signals that allow some algebraic representation. Those signals, namely disturbances, negatively affect the performance of control strategies, especially in high-performance contexts. Common issues with existing methods that try to estimate and compensate for those disturbances include complex dimensions, inflexibility, cumbersome design processes, and problems related to high-gain observers that effectively compensate for the disturbances considered in the scheme. This paper introduces a novel, structurally simple multi-observer scheme that leverages prior knowledge of disturbances. By incorporating internal models for each disturbance component, this strategy enhances design simplicity, increases flexibility by allowing the addition of new observers for emerging disturbances, and circumvents problems associated with high dimensions and high gain. Additionally, a novel solution is presented to address the estimation of periodic signals without including a high-dimension observer. The effectiveness of this approach is demonstrated through well-executed and convincing experimental results.
{"title":"Decoupled multi-observer design for disturbance estimation with low-order internal models","authors":"John Cortés-Romero , Horacio Coral-Enriquez , Brian Camilo Gómez-León , Hebertt Sira-Ramírez","doi":"10.1016/j.conengprac.2026.106767","DOIUrl":"10.1016/j.conengprac.2026.106767","url":null,"abstract":"<div><div>Implementable applications are often compromised by signals that allow some algebraic representation. Those signals, namely disturbances, negatively affect the performance of control strategies, especially in high-performance contexts. Common issues with existing methods that try to estimate and compensate for those disturbances include complex dimensions, inflexibility, cumbersome design processes, and problems related to high-gain observers that effectively compensate for the disturbances considered in the scheme. This paper introduces a novel, structurally simple multi-observer scheme that leverages prior knowledge of disturbances. By incorporating internal models for each disturbance component, this strategy enhances design simplicity, increases flexibility by allowing the addition of new observers for emerging disturbances, and circumvents problems associated with high dimensions and high gain. Additionally, a novel solution is presented to address the estimation of periodic signals without including a high-dimension observer. The effectiveness of this approach is demonstrated through well-executed and convincing experimental results.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106767"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979886","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.conengprac.2026.106769
S.K. Mallipeddi , M. Menghini , S. Simani , P. Castaldi
Path following for underwater vehicles remains a significant challenge due to underactuation in the sway and heave directions. Most existing approaches rely on line-of-sight guidance to address this issue. In this paper, we explore an alternative approach using kinematic guidance, based on virtual reference point guidance, wherein a fictitious point offset from the vehicle’s center of rotation is used to reformulate the kinematic control problem and mitigate underactuation constraints. While this concept has been explored to some extent, previous works have largely overlooked the impact of the vehicle’s attitude. To address this limitation, we propose a solution that simultaneously accounts for the vehicle’s attitude while minimizing cross-track error by defining the error dynamics in the body reference frame, which enables direct control of yaw and sway through yaw rate actuation. A model predictive controller is designed to optimize both attitude stabilization and trajectory tracking performance and is enhanced with an adaptive extended Kalman filter-like observer to estimate the sideslip caused by sea currents and external disturbances. The proposed controller is evaluated under the influence of sea currents and modeling uncertainties, and compared to an existing method from the literature, demonstrating its effectiveness in maintaining path-following accuracy while stabilizing the attitude in the presences of the sea currents.
{"title":"Kinematic guidance using virtual reference point for underactuated marine vehicles with sideslip compensation","authors":"S.K. Mallipeddi , M. Menghini , S. Simani , P. Castaldi","doi":"10.1016/j.conengprac.2026.106769","DOIUrl":"10.1016/j.conengprac.2026.106769","url":null,"abstract":"<div><div>Path following for underwater vehicles remains a significant challenge due to underactuation in the sway and heave directions. Most existing approaches rely on line-of-sight guidance to address this issue. In this paper, we explore an alternative approach using kinematic guidance, based on virtual reference point guidance, wherein a fictitious point offset from the vehicle’s center of rotation is used to reformulate the kinematic control problem and mitigate underactuation constraints. While this concept has been explored to some extent, previous works have largely overlooked the impact of the vehicle’s attitude. To address this limitation, we propose a solution that simultaneously accounts for the vehicle’s attitude while minimizing cross-track error by defining the error dynamics in the body reference frame, which enables direct control of yaw and sway through yaw rate actuation. A model predictive controller is designed to optimize both attitude stabilization and trajectory tracking performance and is enhanced with an adaptive extended Kalman filter-like observer to estimate the sideslip caused by sea currents and external disturbances. The proposed controller is evaluated under the influence of sea currents and modeling uncertainties, and compared to an existing method from the literature, demonstrating its effectiveness in maintaining path-following accuracy while stabilizing the attitude in the presences of the sea currents.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106769"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038455","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}
With the advancements towards automated and autonomous driving system, this paper develops a novel steer-by-wire (SbW) configuration based on a dual three-phase permanent magnet synchronous motor (DTP-PMSM). This system incorporates an innovative triple redundant orthogonal decoupling technology. The DTP-PMSM is decoupled into three independently controllable two-phase orthogonal motors that drive the steering system through rigid coaxial output. To improve the steering angle tracking accuracy and anti-interference capability of this triple redundant SbW system, this paper propose a two-layer control strategy. The outer layer features an Angle Tracking Controller (ATC) utilizing a non-singular fast terminal sliding mode approach combined with an extended state observer. The ATC tracks the steering angle and outputs the target current. The inner layer employs a Torque Synchronous Controller (TSC), which allocates the target current as reference torque signals to the three redundant motors. Taking into account the delay of the signal, this paper introduce an improved generalized predictive torque synchronization algorithm with mean deviation coupling, optimized via a wavelet neural network. This algorithm balances the output torque between the three redundant motors, suppresses torque asynchrony caused by parameter variations, disturbances, and faults, and improves steering tracking performance. Crucially, to prevent imbalance resulting from the fixed gain in the deviation-coupling structure, this paper propose a wavelet neural network compensator. This compensator dynamically optimizes the structural gain, enabling rapid and precise deviation compensation to achieve fast elimination of torque errors between the three redundant motors. Experimental results demonstrate that the triple redundant motor system achieves rapid torque synchronization and significantly improves the steering angle tracking performance of the SbW system.
{"title":"Redundant torque syncronization and steering angle tracking strategy for dual three phase steer-by-wire system","authors":"Haoyu Sun, Wanzhong Zhao, Chunyan Wang, Zhongkai Luan, Weihe Liang, Ziyu Zhang, Xiaochuan Zhou, Yukai Chu","doi":"10.1016/j.conengprac.2026.106784","DOIUrl":"10.1016/j.conengprac.2026.106784","url":null,"abstract":"<div><div>With the advancements towards automated and autonomous driving system, this paper develops a novel steer-by-wire (SbW) configuration based on a dual three-phase permanent magnet synchronous motor (DTP-PMSM). This system incorporates an innovative triple redundant orthogonal decoupling technology. The DTP-PMSM is decoupled into three independently controllable two-phase orthogonal motors that drive the steering system through rigid coaxial output. To improve the steering angle tracking accuracy and anti-interference capability of this triple redundant SbW system, this paper propose a two-layer control strategy. The outer layer features an Angle Tracking Controller (ATC) utilizing a non-singular fast terminal sliding mode approach combined with an extended state observer. The ATC tracks the steering angle and outputs the target current. The inner layer employs a Torque Synchronous Controller (TSC), which allocates the target current as reference torque signals to the three redundant motors. Taking into account the delay of the signal, this paper introduce an improved generalized predictive torque synchronization algorithm with mean deviation coupling, optimized via a wavelet neural network. This algorithm balances the output torque between the three redundant motors, suppresses torque asynchrony caused by parameter variations, disturbances, and faults, and improves steering tracking performance. Crucially, to prevent imbalance resulting from the fixed gain in the deviation-coupling structure, this paper propose a wavelet neural network compensator. This compensator dynamically optimizes the structural gain, enabling rapid and precise deviation compensation to achieve fast elimination of torque errors between the three redundant motors. Experimental results demonstrate that the triple redundant motor system achieves rapid torque synchronization and significantly improves the steering angle tracking performance of the SbW system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106784"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038453","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 : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.conengprac.2025.106724
Gustavo G. Koch , Lucas Borin , Caio Osório , Mokthar Aly , Margarita Norambuena , Jose Rodriguez , Fernanda Carnieluti , Humberto Pinheiro , Ricardo C.L.F. Oliveira , Vinícius F. Montagner
This paper introduces a new methodology for designing robust current controllers for grid-connected converters (GCCs) with LCL filters, ensuring suitable operation from strong to very weak grid conditions. The approach combines i) a polytopic plant model accounting for control delay and parametric uncertainties, ii) improved linear matrix inequality (LMI) synthesis conditions for robust pole placement, and iii) experimental validation via Controller Hardware-in-the-Loop (C-HIL). The LMI-based design integrated with C-HIL guarantees theoretical robustness and provides practical insights on the performance of the controller with unmodeled dynamics and nonlinearities, enhancing the robustness-performance trade-off while reducing costs and risks. Experimental results for a GCC under a grid with large impedance uncertainty and voltage harmonics show that traditional LMI techniques produce higher control gains causing persistent saturation of the actuator and degrading the performance in real implementation. Conversely, the proposed methodology ensures compliance with reference tracking, harmonics rejection, and voltage dip recovery, even under very weak grids (short-circuit ratio (SCR) = 1). Compared to methods relying on LMIs and C-HIL, the proposal is much superior, computing control gains at least 20 times faster through a fully deterministic convex optimization, while ensuring high-performance when implemented online on off-the-shelf digital signal processors.
{"title":"Improved control of grid-connected converters from strong to very weak conditions integrating more effective LMIs and C-HIL","authors":"Gustavo G. Koch , Lucas Borin , Caio Osório , Mokthar Aly , Margarita Norambuena , Jose Rodriguez , Fernanda Carnieluti , Humberto Pinheiro , Ricardo C.L.F. Oliveira , Vinícius F. Montagner","doi":"10.1016/j.conengprac.2025.106724","DOIUrl":"10.1016/j.conengprac.2025.106724","url":null,"abstract":"<div><div>This paper introduces a new methodology for designing robust current controllers for grid-connected converters (GCCs) with LCL filters, ensuring suitable operation from strong to very weak grid conditions. The approach combines i) a polytopic plant model accounting for control delay and parametric uncertainties, ii) improved linear matrix inequality (LMI) synthesis conditions for robust pole placement, and iii) experimental validation via Controller Hardware-in-the-Loop (C-HIL). The LMI-based design integrated with C-HIL guarantees theoretical robustness and provides practical insights on the performance of the controller with unmodeled dynamics and nonlinearities, enhancing the robustness-performance trade-off while reducing costs and risks. Experimental results for a GCC under a grid with large impedance uncertainty and voltage harmonics show that traditional LMI techniques produce higher control gains causing persistent saturation of the actuator and degrading the performance in real implementation. Conversely, the proposed methodology ensures compliance with reference tracking, harmonics rejection, and voltage dip recovery, even under very weak grids (short-circuit ratio (SCR) = 1). Compared to methods relying on LMIs and C-HIL, the proposal is much superior, computing control gains at least 20 times faster through a fully deterministic convex optimization, while ensuring high-performance when implemented online on off-the-shelf digital signal processors.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"169 ","pages":"Article 106724"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038454","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 : 2026-04-01Epub Date: 2026-01-21DOI: 10.1016/j.conengprac.2026.106785
Songmiao Li , Yangfan Zhou , Pengze Liu , Dan Ye , Bi Zhang , Xingang Zhao
Functional electrical stimulation (FES) has shown promise in restoring motor functions for patients with spinal cord injury and stroke. However, its clinical application is limited by insufficient accuracy in modeling muscle dynamics and the lack of robust control strategies under complex disturbances. To address these challenges, this study proposes a closed-loop framework that integrates high-precision modeling with strong robustness. A Hammerstein model enhanced by Kolmogorov-Arnold Networks (KAN) is constructed, where the explicit mathematical representation of KAN significantly improves the nonlinear dynamic modeling of muscle behavior. Additionally, a forgetting factor recursive least squares (FFRLS) algorithm is employed for online identification of time-varying parameters, achieving improved performance over traditional approaches. Further, a sliding-mode tube model predictive control (SMC-Tube MPC) strategy driven by surface electromyography (sEMG) feedback is developed. By combining the disturbance rejection capability of sliding mode control with the state constraint handling features of Tube-MPC, the proposed controller enables stable torque tracking under complex perturbations. The framework is validated on an experimental platform integrating a dynamometer, sEMG acquisition device, and electrical stimulator. Experiments with healthy subjects demonstrate high accuracy and strong robustness of the proposed system.
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