Control Engineering Practice最新文献

{"title":"Neural network-based state observation utilizing a history-of-error performance index","authors":"Samira Asadi,&nbsp;Mehrdad Moallem","doi":"10.1016/j.conengprac.2025.106578","DOIUrl":"10.1016/j.conengprac.2025.106578","url":null,"abstract":"<div><div>Accurate state estimation is crucial for the control and monitoring of multivariable nonlinear systems. Neural network-based observers offer promising solutions due to their universal approximation capabilities; however, maintaining precision and robustness in the presence of nonlinearities and parametric uncertainties remains a significant challenge. This paper presents an adaptive neural network observer that incorporates a history-of-error term into the weight update rules of a modified backpropagation algorithm. An e-modification term is introduced to ensure bounded state-estimation errors, with stability formally established through a Lyapunov-based analysis. Simulation and experimental studies on a one-link arm under gravity, actuated by a DC motor, demonstrate that the proposed observer can significantly enhance the estimation accuracy and convergence speed when compared to conventional neural network observers. Comparative studies indicate an approximate 50% improvement in state estimation and control accuracy, highlighting the effectiveness of the proposed approach.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106578"},"PeriodicalIF":4.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099753","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":"Data-driven controller tuning of hybrid integrator-gain systems for settling time optimization","authors":"Jonas G. Hendrikx ,&nbsp;Wouter Weekers ,&nbsp;Luke F. van Eijk ,&nbsp;Marcel F. Heertjes ,&nbsp;Nathan van de Wouw","doi":"10.1016/j.conengprac.2025.106555","DOIUrl":"10.1016/j.conengprac.2025.106555","url":null,"abstract":"<div><div>In this work, we present a novel data-driven tuning framework for a class of nonlinear controllers, namely those based on the so-called hybrid integrator-gain system (HIGS). In particular, we focus on minimizing the settling time in point-to-point tasks, i.e., the time required for the error to converge and settle within a desired error bound after the task has finished. The proposed approach is based on sampled-data extremum-seeking control and allows simultaneous tuning of both linear and nonlinear parts of the controller, while guaranteeing input-to-state stability based solely on non-parametric frequency-response function data of the plant. These stability properties are guaranteed by a newly developed procedure for the data-driven verification of existing stability criteria. The efficacy of the proposed approach in tuning HIGS-based controllers for improving the settling time is validated extensively with a case study on an industrial wire bonder showing significant improvements in the worst-case settling time compared to LTI control.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106555"},"PeriodicalIF":4.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099751","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":"Friction compensation computed torque control of a cylindrical 5DOF pneumatic robot","authors":"Eduardo A. Perondi ,&nbsp;Carlos A.C. Sarmanho Jr. ,&nbsp;Leonardo Missiaggia ,&nbsp;Mauro A.B. Cunha","doi":"10.1016/j.conengprac.2025.106567","DOIUrl":"10.1016/j.conengprac.2025.106567","url":null,"abstract":"<div><div>This paper proposes a new strategy for controlling a robotic system by using a friction compensation based on a modification in the classical Computed Torque formulation applied to a pneumatically driven system. The proposed strategy is applied to synthesize a control law for a five degree of freedom robot, which dynamic model is interpreted as two subsystems: a mechanical subsystem driven by a pneumatic one. This interpretation allows the introduction of the friction estimation at the force level in the pneumatic subsystem. The closed loop system is proven to be Lyapunov stable, and a robustness analysis is also provided. The convergence of tracking errors is shown through experimental results, illustrating the main characteristics of the proposed control strategy when applied to a real robot.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106567"},"PeriodicalIF":4.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099749","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":"Reference trajectory learning based adaptive iterative impedance control for a lower limb rehabilitation exoskeleton with actuator saturation","authors":"Yong Yang ,&nbsp;Hongjun Chen ,&nbsp;Xia Liu ,&nbsp;Deqing Huang ,&nbsp;Yanan Li","doi":"10.1016/j.conengprac.2025.106574","DOIUrl":"10.1016/j.conengprac.2025.106574","url":null,"abstract":"<div><div>In this paper, adaptive iterative learning impedance control is developed for a lower limb rehabilitation exoskeleton subject to unknown reference trajectory, unknown nonlinearities, and actuator saturation. A novel dual-loop learning control strategy is proposed for human-exoskeleton interaction, where the outer control loop is designed to follow a target impedance model and the inner position loop is constructed for tracking a balanced trajectory. First, the contact force between the patient and the exoskeleton is used to learn the reference trajectory in an iterative manner. Second, under the framework of backstepping technique, an adaptive iterative learning controller is developed to deal with the unknown nonlinearities and improve the tracking performance. In order to ensure the safety of patient’s limbs during human-exoskeleton interaction, the actuator saturation is considered and addressed by introducing an auxiliary system. Third, with the design of the reference trajectory learning algorithm and the adaptive iterative controller, the convergence of both the target impedance following and trajectory tracking is proved rigorously, and the boundedness of all the involved signals are guaranteed. Finally, the effectiveness of the proposed control scheme is verified by both simulation and experimental study on a 2-DOF exoskeleton.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106574"},"PeriodicalIF":4.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099748","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":"Reducing the communication of distributed model predictive control: Autoencoders and formation control","authors":"Torben Schiz,&nbsp;Henrik Ebel","doi":"10.1016/j.conengprac.2025.106560","DOIUrl":"10.1016/j.conengprac.2025.106560","url":null,"abstract":"<div><div>Communication remains a key factor limiting the applicability of distributed model predictive control (DMPC) in realistic settings, despite advances in wireless communication. DMPC schemes can require an overwhelming amount of information exchange between agents as the amount of data depends on the length of the predication horizon, for which some applications require a significant length to formally guarantee nominal asymptotic stability. This work aims to provide an approach to reduce the communication effort of DMPC by reducing the size of the communicated data between agents. Using an autoencoder, the communicated data is reduced by the encoder part of the autoencoder prior to communication and reconstructed by the decoder part upon reception within the distributed optimization algorithm that constitutes the DMPC scheme. The choice of a learning-based reduction method is motivated by structure inherent to the data, which results from the data’s connection to solutions of optimal control problems. The approach is implemented and tested at the example of formation control of differential-drive robots, which is challenging for optimization-based control due to the robots’ nonholonomic constraints, and which is interesting due to the practical importance of mobile robotics. The applicability of the proposed approach is presented first in the form of a simulative analysis showing that the resulting control performance yields a satisfactory accuracy. In particular, the proposed approach outperforms the canonical naive way to reduce communication by reducing the length of the prediction horizon. Moreover, it is shown that numerical experiments conducted on embedded computation hardware, with real distributed computation and wireless communication, work well with the proposed way of reducing communication even in practical scenarios in which full communication fails, as the full-size data messages are not communicated in a timely-enough manner. This shows an objective benefit of using the proposed communication reduction in practice, especially in situations in which a lot of communication happens within a given time span, e.g., because of a large number of agents, a densely connected communication graph, and/or frequent data exchange.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106560"},"PeriodicalIF":4.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049014","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 temporal–spatial synthesis projection for process monitoring under multi-factor disturbances","authors":"Shumei Zhang ,&nbsp;Hongtu Li ,&nbsp;Shuai Tan ,&nbsp;Feng Dong","doi":"10.1016/j.conengprac.2025.106563","DOIUrl":"10.1016/j.conengprac.2025.106563","url":null,"abstract":"<div><div>Process monitoring methods in industrial applications frequently encounter performance degradation challenges stemming from multi-factor disturbances, including mode switching, outlier interference, and dynamic variations. Existing approaches rarely demonstrate sufficient robustness to overcome the comprehensive disturbances generated by these factors. This paper proposes a robust temporal–spatial synthesis projection (RTSSP) strategy to enhance algorithmic robustness by considering both spatial and temporal information. A hybrid neighborhood–kernel similarity (HNKS) is defined by integrating both global distance and local neighborhood information, enabling comprehensive capture of spatial-scale features in multimodal data while leveraging neighborhood topological differences to suppress outlier influence. Additionally, RTSSP explores both the real manifold data structure and temporal information, which captures dynamic changes and learns the synthesis projection from the spatial–temporal dimension to extract the core features with high discriminative properties. Finally, experimental validation through numerical simulations and a two-phase flow process case demonstrates the significant advantages of the proposed method.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106563"},"PeriodicalIF":4.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049012","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":"Reinforcement learning from human feedback with fast–slow updates for stable driving strategies","authors":"Hengcong Guo,&nbsp;Rohan Khaire,&nbsp;Junfeng Zhao","doi":"10.1016/j.conengprac.2025.106549","DOIUrl":"10.1016/j.conengprac.2025.106549","url":null,"abstract":"<div><div>Reinforcement learning (RL) offers a promising framework for decision-making in automated vehicles (AVs), yet its practical application faces major obstacles, including sparse and delayed rewards, unstable policy optimization, and difficulties in designing effective reward functions. To address these challenges, we propose a human-in-the-loop RL framework with a fast–slow update architecture that enables reward-free training while maintaining policy stability. The fast update relies on direct feedback during human takeovers, using binary preference signals to guide the agent in early training. The slow update introduces a similarity constraint by comparing the agent’s actions to those of an auxiliary expert network trained exclusively on human-labeled transitions. This dual-update strategy allows the agent to benefit from efficient exploration while remaining anchored to human-aligned behavior. The method optimizes a combined objective consisting of temporal difference loss, proxy value loss from human preferences, and a similarity loss. Experiments conducted in the CARLA simulator demonstrate that this approach achieves lower takeover rates, faster convergence, and improved driving stability compared to standard RL methods. These results highlight the effectiveness of structured human feedback in reducing training burden and enhancing real-world readiness of autonomous driving policies. The code is available at: <span><span>https://github.com/BELIV-ASU/aPVP0.9.10.1.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106549"},"PeriodicalIF":4.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049015","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 safe control for nonlinear quadrotor with a cable-suspended payload systems via control barrier function and disturbance estimator","authors":"Zhiyuan Zheng ,&nbsp;Shiji Tong ,&nbsp;Yunlin Zhang ,&nbsp;Yang Zhu ,&nbsp;Tieshan Li ,&nbsp;Jinliang Shao","doi":"10.1016/j.conengprac.2025.106564","DOIUrl":"10.1016/j.conengprac.2025.106564","url":null,"abstract":"<div><div>In quadrotor with a cable-suspended payload transportation tasks, achieving real-time safe control of the system is significantly challenging due to the system’s strong nonlinearity and underactuated nature. This challenge is further amplified by the presence of model uncertainties and external disturbances. To address this issue, we first derive the safe condition of the quadrotor with a cable-suspended payload system subjected to obstacles and construct a control barrier function accordingly. Then, considering the impact of disturbances on control performance and system safety, a unified control barrier function-uncertainty and disturbance estimator framework is developed to simultaneously ensure safety and robustness. Finally, the safe control problem is reformulated as a modified quadratic program to guarantee its engineering feasibility and is solved online using the IPOPT solver. The proposed methodology is deployed on a custom-built quadrotor, and extensive experiments in the environment with obstacles and disturbances are implemented to validate its effectiveness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106564"},"PeriodicalIF":4.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049013","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":"Advanced precision tracking control for permanent magnet linear synchronous motors utilizing a novel global dynamic fractional order sliding mode","authors":"XinYu Zhao ,&nbsp;LiMei Wang ,&nbsp;Zelai Xu ,&nbsp;Weiyu Wang","doi":"10.1016/j.conengprac.2025.106565","DOIUrl":"10.1016/j.conengprac.2025.106565","url":null,"abstract":"<div><div>This paper proposes a novel dynamic fractional order super-twisting terminal sliding mode control method aimed at suppressing the effects of undesired nonlinear dynamics, unstructured uncertainties, parameter mismatch and disturbance load on the PMLSM system performance, realizing high-precision tracking control with strong robustness. Firstly, a dynamic model of the system is developed, taking into account nonlinear friction and uncertainty. Then, combining the principles of fractional order and terminal sliding mode control, using the exponential function with tracking error as the independent variable as the control gain of the fractional order component, the fractional-order dynamic terminal sliding mode manifold is proposed, which achieves rapid convergence and a smooth transition process. Meanwhile, to address complex uncertainties of the system, a dynamic super-twisting algorithm, grounded in Lyapunov theory, is formulated. This algorithm allows for global dynamic adjustment and precise compensation for uncertainties and disturbances. Finally, simulation and experimental results demonstrate that the proposed method outperforms existing controllers in terms of tracking accuracy and robustness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106565"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026632","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 zone predictive safety filter for drill string torsional vibrations control considering measurement delay and data loss","authors":"Yilong Zhang ,&nbsp;Baochang Xu ,&nbsp;Yiqi Chen ,&nbsp;Wei Liu ,&nbsp;Ruijie Gao ,&nbsp;Jun Yao","doi":"10.1016/j.conengprac.2025.106561","DOIUrl":"10.1016/j.conengprac.2025.106561","url":null,"abstract":"<div><div>During drilling operations, bit fatigue and failure caused by undesirable vibrations frequently lead to substantial increases in drilling time and costs. Among these vibrations, the torsional vibration, whose severe form is also known as stick–slip oscillation, represents one of the most destructive vibrations. Existing advanced control methods, such as Sliding Mode Control (SMC) and Robust Control, often face limitations, including difficulties in on-site deployment and measurement delays or data loss in downhole data. This study proposes a novel “plug-and-play” approach that augments the existing torsional control system through the integration of two key modules: the Zone Predictive Safety Filter (Zone PSF) and Robust Moving Horizon Estimator (Robust MHE). The Zone PSF module detects and eliminates the stick–slip oscillations in real time. This ensures the safety of the drilling system throughout the drilling process. The Robust MHE module addresses bit rotational speed estimation challenges under time-varying measurement delays and data loss scenarios. It employs adaptive gain technology to maintain the stability of the estimation process even in non-Persistently Exciting (PE) drilling conditions. Finally, the numerical simulation results indicate that the proposed method yields superior control performance and robustness for the torsional control system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106561"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026631","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}