Control Engineering Practice最新文献

{"title":"Dynamic real-time optimization to mitigate critical state effects in expert-controlled SAG mills","authors":"Cristobal Mancilla ,&nbsp;Rodrigo Bruna ,&nbsp;Paulina Quintanilla ,&nbsp;Daniel Navia","doi":"10.1016/j.conengprac.2025.106589","DOIUrl":"10.1016/j.conengprac.2025.106589","url":null,"abstract":"<div><div>Semi-autogenous (SAG) mills are critical components in mineral processing, widely used for size reduction to liberate valuable minerals in ore. These mills account for a significant portion of energy consumption and operational costs in mineral concentrator plants, making their optimization a key factor for improving overall process efficiency. Traditional expert control systems used in SAG mill operations lack predictive capabilities, which can result in critical operational states requiring a halt in the process and significantly affecting productivity.</div><div>This work introduces a dynamic real-time optimization (D-RTO) strategy to mitigate the impact of critical states, such as overload or high pressure, in SAG mills controlled by expert systems. The proposed supervisory layer dynamically adjusts the limits of controlled variables based on predictive capabilities provided by a digital twin. Simulation results demonstrate significant performance improvements, including reductions in total tonnage loss of up to 57%, decreases in critical state duration by up to 33%, and increases in average throughput by up to 3.5%. These results underline the effectiveness of the D-RTO strategy, particularly under dynamic and variable conditions. Future research will expand the framework to include power consumption considerations for comprehensive economic optimization.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106589"},"PeriodicalIF":4.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157672","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 recovery model for battery data in the cloud and its application of state-of-charge estimation in electric vehicles","authors":"Ziying Huang,&nbsp;Jingzhe Zhu,&nbsp;Zhenjiang Wang,&nbsp;Xi Zhang,&nbsp;Guodong Fan","doi":"10.1016/j.conengprac.2025.106579","DOIUrl":"10.1016/j.conengprac.2025.106579","url":null,"abstract":"<div><div>High-quality historical battery data is crucial for state estimation and management. However, due to limitations in network bandwidth and storage capacity, the cloud only receives low-frequency (LF) data, while high-frequency (HF) data is stored locally on the EV for a short period. This paper introduces a model-based framework for recovering low-frequency voltage signals. The training and test datasets are first constructed using real-world vehicle data. Subsequently, a multitask learning model within a semi-supervised learning framework is proposed to capture the HF voltage representation of each battery cell. The model successfully upsamples the 0.1 Hz sampling rate data to 1 Hz with a root mean square error (RMSE) of 16.71 mV on the test dataset post-training. A SOC estimation framework based on the unscented Kalman filter and electrochemical model is introduced to capitalize on the high-quality data generated by the voltage recovery framework. This framework estimates SOCs for each individual cell in a battery pack by identifying unique electrochemical parameter sets for each cell. The results demonstrate that the framework can identify the cell with the lowest real SOC and estimate SOC within a 1.8% RMSE margin, even when the cell with the lowest SOC does not exhibit the lowest voltage. Finally, cost-effective enhancements for both the voltage recovery model and the SOC estimation framework are recommended to balance performance with the computational power requirements.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106579"},"PeriodicalIF":4.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109125","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":"Optimisation-based tuning of nonlinear controllers for targeted dynamics: Methodology and a demonstrative wind energy case","authors":"Demián García-Violini ,&nbsp;Carolina A. Evangelista ,&nbsp;Yerai Peña-Sanchez ,&nbsp;Paul Puleston","doi":"10.1016/j.conengprac.2025.106585","DOIUrl":"10.1016/j.conengprac.2025.106585","url":null,"abstract":"<div><div>Tuning controllers for nonlinear systems remains a significant challenge due to their inherent complexity and the lack of systematic design methodologies. This study presents a practical, versatile, and robust optimisation-based procedure for tuning nonlinear controllers, including linear ones, offering a structured alternative to the traditional trial-and-error tuning strategies commonly used in practice. A key contribution of the proposed methodology is the explicit definition and achievement of target closed-loop dynamic behaviours in nonlinear systems, which is analogous to classical dynamics in linear control. By leveraging global optimisation techniques, the procedure systematically identifies controller parameters that minimise deviations from predefined dynamic targets while ensuring robustness and stability across a range of operating conditions. The approach addresses the nonlinear response of the controlled system and provides an intuitive and customisable framework for shaping closed-loop dynamics according to design objectives. The methodology is validated through its application to a wind turbine control problem, demonstrating its ability to tune both proportional–integral (PI) and super-twisting sliding mode controllers (SMC) effectively. The results highlight the sensitivity of nonlinear controllers to parameter selection and underscore the benefits of a systematic tuning approach in achieving consistent performance, preventing actuator saturation, and ensuring system longevity. This study offers a powerful and generalisable solution for tuning controllers in complex nonlinear systems, enabling practitioners to move beyond empirical tuning practice.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106585"},"PeriodicalIF":4.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099686","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":"Model predictive control with residual learning and real-time disturbance rejection: Design and experimentation","authors":"Haodi Zhang ,&nbsp;Junwei Ge ,&nbsp;Jinya Su ,&nbsp;Kun Gu ,&nbsp;Fuyou Wang ,&nbsp;Wen-Hua Chen ,&nbsp;Shihua Li","doi":"10.1016/j.conengprac.2025.106587","DOIUrl":"10.1016/j.conengprac.2025.106587","url":null,"abstract":"<div><div>Model Predictive Control (MPC) deteriorates with low-quality prediction models and unknown external disturbances. Simply incorporating residual physics learning or uncertainty/disturbance rejection alone as in existing studies often yields limited performance gains for MPC. In this study, we integrate sparse Gaussian Process (GP) and Generalized Extended State Observer (GESO) within MPC, forming the GP-MPC-GESO controller. In this framework, GP learns the residual physics, improving the prediction model while reducing GESO’s disturbance estimation load. Meanwhile, GESO estimates the GP’s remaining residual uncertainties and external disturbances in real time and is directly incorporated into MPC prediction model. The synergy between GP residual learning and real-time GESO in managing uncertainties and disturbances significantly enhances MPC’s tracking control performance with a simplified nominal physical model. Comparative trajectory tracking control experiments on Mecanum Wheel Mobile Robots in both indoor and outdoor environments under various settings demonstrate that the proposed GP-MPC-GESO controller reduces RMSE by 12.4% and 16.2% compared to the state-of-the-art MPC-GESO controller in indoor and outdoor Lemniscate tracking, respectively. The video demonstration of this work is available at <span><span>https://drive.google.com/file/d/1LC81S093iogWxzyBcHFuWGLth1u-Wwcx/view?usp=drive_link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106587"},"PeriodicalIF":4.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel few-shot learning approach for cross-domain fault diagnosis in high-voltage circuit breakers","authors":"Qiuyu Yang ,&nbsp;Xiaorong Huang ,&nbsp;Jiangjun Ruan ,&nbsp;Xue Xue ,&nbsp;Yuxiang Liao ,&nbsp;Jingyi Xie","doi":"10.1016/j.conengprac.2025.106577","DOIUrl":"10.1016/j.conengprac.2025.106577","url":null,"abstract":"<div><div>High-voltage circuit breakers (HVCBs) play a pivotal role in ensuring the reliability and safety of power systems. However, cross-domain fault diagnosis remains a challenging task due to domain shift and limited labeled data. This paper introduces a novel few-shot learning framework, the integrated transfer fine-grained metric network (ITFGMN), specifically designed to tackle these issues. The proposed framework integrates three key innovations: (1) a channel-focused convolutional neural network module is introduced for effective feature extraction, enabling the capture of domain-invariant patterns; (2) a domain alignment engine is incorporated to bridge the domain gap, facilitating improved feature alignment across domains; and (3) a fine-grained metrics module employs a weighted prototype-based strategy to dynamically optimize the contribution of support samples and mitigate negative transfer. Comprehensive experiments on real-world HVCB datasets demonstrate that ITFGMN achieves superior performance compared to state-of-the-art methods in cross-domain fault diagnosis, showcasing its potential for practical deployment.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106577"},"PeriodicalIF":4.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099755","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":"Rapid identification method for nonlinear feed drive model parameters using integral driven friction decoupling and dimensionality reduction of cost function","authors":"Jong-Min Lim,&nbsp;Joon-Soo Lee,&nbsp;Ji-Myeong Park,&nbsp;Byung-Kwon Min","doi":"10.1016/j.conengprac.2025.106580","DOIUrl":"10.1016/j.conengprac.2025.106580","url":null,"abstract":"<div><div>The increasing use of digital twins in machine tool design and control requires feed drive models for simulating the system dynamics. Achieving higher accuracy in digital twin applications involves incorporating nonlinear friction models, such as the Stribeck curve or LuGre friction model, into the feed drive models. However, the process of identifying model parameters, such as the equivalent mass and friction parameters, is time-consuming and challenging due to the complexity of multivariable cost functions. In addition, the quantitative evaluation of identification accuracy is challenging. This paper proposes a rapid and accurate method for identifying feed drive model parameters via integral-driven elimination-based friction decoupling and cost function dimensionality reduction. In the equivalent mass identification, friction is decoupled to eliminate the nonlinearity in the identification process. In the feed drive model, the equivalent mass is the coefficient of acceleration, and the friction is modeled as a function of velocity. The friction terms can be modified into conservative terms with respect to velocity by multiplying those with acceleration. By setting the integral interval using driving direction, the friction terms are eliminated, and the resulting feed drive model becomes a linear equation that is composed of only applied force and equivalent mass-related terms. This friction-decoupled equation allows equivalent mass identification through linear regression. In addition, linear equations are derived for friction parameter identification, with slopes corresponding to the Stribeck and LuGre friction parameters, excluding the Stribeck velocity and micro stiffness coefficients. These friction parameters are then expressed as functions of the Stribeck velocity and micro stiffness coefficient. The resulting equations are incorporated into the cost functions for iterative computations, leading to reduced dimensionality. Simulation and experimental results confirm the capability of the proposed method for rapid and accurate identification.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106580"},"PeriodicalIF":4.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099756","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":"Distributed model predictive control of automated vehicle entities in lane-free traffic involving human driven vehicles","authors":"Niloufar Dabestani ,&nbsp;Dimitrios Troullinos ,&nbsp;Ioannis Papamichail ,&nbsp;Mehdi Naderi ,&nbsp;Markos Papageorgiou","doi":"10.1016/j.conengprac.2025.106586","DOIUrl":"10.1016/j.conengprac.2025.106586","url":null,"abstract":"<div><div>This paper presents an event-triggered distributed model predictive control (MPC) scheme for diverse automated vehicle entities populating a lane-free traffic environment with vehicle nudging. The vehicle entities comprise, beyond individual automated vehicles, 1-D snake-like interruptible vehicle platoons and 2-D flexible-shape vehicle flocks. Each entity is driven independently by use of a proper movement strategy for one or multiple automated vehicles deriving from a generic single-vehicle or joint optimal control problem, respectively. Human-driven vehicles may also be present and are considered as obstacles. A two-dimensional double-integrator model is considered for the longitudinal and lateral movements of each vehicle, considering constant and state-dependent bounds on control inputs, including road boundary constraints. A multi-objective function, comprising various weighted sub-objectives, is designed for all vehicles of each entity, considering minimization of fuel consumption, intra-entity and inter-entity collision avoidance, entity-specific desired speed, prevention of infeasible maneuvers and, for multi-vehicle entities, operation of a flexible platoon or deformable flock. A computationally efficient feasible direction algorithm is called, on an event-triggered basis, to compute in real time the numerical solution of each entity’s optimal control problem for finite time-horizons within an MPC framework. Testing and demonstration scenarios are examined on two setups: one on a straight, lane-free motorway and the other on a similar motorway that transitions into a narrowed road after passing through a funnel.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106586"},"PeriodicalIF":4.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099684","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":"Hierarchical MPC-based safe shared control for forward collision avoidance in collaborative driving","authors":"Haoqi Yan ,&nbsp;Yue Qu ,&nbsp;Renjie Li ,&nbsp;Wenyu Li ,&nbsp;Hongqing Chu ,&nbsp;Junjie Zhao ,&nbsp;Guangyuan Yu ,&nbsp;Fei Ma ,&nbsp;Shengbo Eben Li ,&nbsp;Jingliang Duan","doi":"10.1016/j.conengprac.2025.106581","DOIUrl":"10.1016/j.conengprac.2025.106581","url":null,"abstract":"<div><div>Human–machine shared control plays a vital role in enhancing driving safety through collision avoidance assistance. Existing collaborative controllers typically integrate risk assessment and control computation within one Model Predictive Control (MPC) framework, which can lead to computational challenges when incorporating essential nonlinear vehicle dynamics for accurate trajectory evaluation. To address this issue, we propose a novel safe shared control scheme with two key components: First, a trajectory generator produces multiple smooth, collision-free candidate trajectories considering both obstacle avoidance and vehicle stability constraints. Second, a hierarchical MPC module evaluates and executes these trajectories through a dual-layer structure. The upper layer uses a linear model to compute control inputs and then evaluates them with a nonlinear vehicle dynamics model for risk assessment, while the lower layer calculates the actual control commands based on a simplified linear model by tracking the selected optimal trajectory. This separation of trajectory generation, risk evaluation, and control computation significantly enhances both computational efficiency and safety assurance. A series of collision avoidance experiments was conducted on a driving simulator to evaluate the proposed method. Results show that the proposed method significantly enhances safety in human–machine shared control driving.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106581"},"PeriodicalIF":4.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099754","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":"Event-triggered control with reliable Gaussian process learning for remote UAV control","authors":"Dohyun Jang ,&nbsp;Jaehyun Yoo","doi":"10.1016/j.conengprac.2025.106576","DOIUrl":"10.1016/j.conengprac.2025.106576","url":null,"abstract":"<div><div>An event-triggered control strategy executes control updates only when a system needs attention. This strategy has been validated as effective in networked control systems by reducing data communication load. To maximize the efficiency of an event-triggered controller, disturbance compensation is essential. In this study, we employ Gaussian Process (GP) learning to estimate model uncertainties. The GP model is trained and updated online using streaming sensor data, with sparse approximation techniques applied to ensure computational tractability and real-time inference without compromising control responsiveness. The key contribution of the proposed event-triggered controller with GP learning is its guaranteed stability, achieved through an analytical error-bound inequality. This stability ensures a reliable operational range for the control system, enabling secure and adaptive adjustment of event-triggering parameters. Applied to a networked quadrotor flight control system under wind disturbances, the proposed method demonstrates accurate and efficient control performance while remaining computationally feasible for real-time implementation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106576"},"PeriodicalIF":4.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099750","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":"Hierarchical neural affine formation maneuver control of air-bearing robots with prescribed performance and collision avoidance","authors":"Weilun Zhang ,&nbsp;Guan Wang ,&nbsp;Hongwei Xia ,&nbsp;Guangcheng Ma","doi":"10.1016/j.conengprac.2025.106575","DOIUrl":"10.1016/j.conengprac.2025.106575","url":null,"abstract":"<div><div>This paper proposes a hierarchical neural network control framework with prescribed performance for affine maneuver formation of air-bearing robots (ABRs), addressing actuator nonlinearities, false data injection (FDI) attacks, and collision avoidance. Firstly, a virtual system is constructed to establish links between leaders and followers, which calculates and map the leader’s affine formation for each follower while preventing fault propagation in followers. Additionally, a novel prescribed performance controller considering collision between follower ABRs is proposed, integrating a neural network with an extended state observer (ESO) for FDI attacks and actuator nonlinearities. In particular, the comparison between control inputs and saturation thresholds is used for performance boundary design, thus achieving prescribed performance without vulnerability to input saturation. Theoretical analysis guarantees system stability, and experimental results demonstrate the method’s effectiveness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106575"},"PeriodicalIF":4.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099752","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}