Control Engineering Practice最新文献_第7页

Design and control of a hardware-in-the-loop based lab-scale down-hole drilling rig 基于硬件在环的实验室规模井下钻机的设计与控制

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-20 DOI: 10.1016/j.conengprac.2025.106506

Zihang Zhang , Dongzuo Tian , Xingyong Song

引用次数: 0

Reinforcement learning based automatic tuning of PID controllers in multivariable grinding mill circuits 多变量磨机电路中基于强化学习的PID控制器自动整定

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-19 DOI: 10.1016/j.conengprac.2025.106522

J.A. van Niekerk , J.D. le Roux , I.K. Craig

{"title":"Reinforcement learning based automatic tuning of PID controllers in multivariable grinding mill circuits","authors":"J.A. van Niekerk , J.D. le Roux , I.K. Craig","doi":"10.1016/j.conengprac.2025.106522","DOIUrl":"10.1016/j.conengprac.2025.106522","url":null,"abstract":"<div><div>Process controllers are extensively utilised in industry and necessitate precise tuning to ensure optimal performance. While tuning controllers through the basic trial-and-error method is possible, this approach typically leads to suboptimal results unless performed by an expert. This study investigates the use of reinforcement learning (RL) for the automatic tuning of proportional–integral–derivative (PID) controllers that control a grinding mill circuit represented by a multivariable nonlinear plant model which was verified using industrial data. By employing the proximal policy optimisation (PPO) algorithm, the RL agent adjusts the controller parameters to enhance closed-loop performance. The problem is formulated to maximise a reward function specifically designed to achieve the desired controller performance. Agent actions are analytically constrained to minimise the risk of closed-loop instability and unsafe behaviours during training. The simulation results indicate that the automatically tuned controller outperforms the manually tuned controller in setpoint tracking. The proposed approach presents a promising solution for real-time controller tuning in industrial processes, potentially increasing productivity and product quality while reducing the need for manual intervention. This research contributes to the field by establishing a robust framework for applying RL in process control, designing effective reward functions, constraining the agent to a safe operational space, and demonstrating its potential to address the challenges associated with PID controller tuning in grinding mill circuits.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106522"},"PeriodicalIF":4.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867478","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}

引用次数: 0

Threshold-driven frequency feature integration for neural network-based oscillation detection and quantification in process industries 过程工业中基于神经网络的振荡检测与量化的阈值驱动频率特征集成

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-19 DOI: 10.1016/j.conengprac.2025.106531

Abhishek Bansal, Resmi Suresh, Prabirkumar Saha

{"title":"Threshold-driven frequency feature integration for neural network-based oscillation detection and quantification in process industries","authors":"Abhishek Bansal, Resmi Suresh, Prabirkumar Saha","doi":"10.1016/j.conengprac.2025.106531","DOIUrl":"10.1016/j.conengprac.2025.106531","url":null,"abstract":"<div><div>Oscillatory behavior in control loops is a prevalent challenge in process industries, often resulting in detrimental effects such as decreased product quality, lower throughput, and higher energy consumption. These oscillations are typically caused by factors such as valve stiction, suboptimal controller tuning, and external disturbances. This paper introduces a neural network-based method for detecting oscillations, applying data pre-processing and domain-informed feature engineering techniques to improve accuracy while minimizing computational demands. The input features to the neural network are prominent features from the Fast Fourier Transform (FFT) and FFT of Autocorrelation Function (ACF) of dynamic process data, identified based on peaks in the frequency domain data. A sensitivity analysis is performed to evaluate the impact of the number of input features on the model’s accuracy, precision, and recall. The analysis shows that the proposed method achieves a reduction of up to 80% in the number of input features compared to existing techniques in the literature, thus reducing computational time without sacrificing performance, making it suitable for online applications. The proposed algorithm achieves a 96.63% accuracy and a recall of 0.96 for detecting oscillatory behavior. In addition, algorithms are proposed in this work to quantify the oscillation period and the amplitude of the oscillation. The oscillation period is calculated based on the frequency and amplitude obtained from the FFT of ACF of dynamic process data, giving an overall accuracy of 93.15% for regular and irregular oscillations. The performance of the method for predicting the amplitude of oscillation is presented for industrial data to validate its effectiveness in real-world scenarios.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106531"},"PeriodicalIF":4.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867480","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}

引用次数: 0

A robust unknown input observer for open channel irrigation systems 明渠灌溉系统的鲁棒未知输入观测器

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-19 DOI: 10.1016/j.conengprac.2025.106510

Juan Pablo Arango , Lucien Etienne , Eric Duviella , Kokou Langueh , Pablo Segovia , Vicenç Puig

{"title":"A robust unknown input observer for open channel irrigation systems","authors":"Juan Pablo Arango , Lucien Etienne , Eric Duviella , Kokou Langueh , Pablo Segovia , Vicenç Puig","doi":"10.1016/j.conengprac.2025.106510","DOIUrl":"10.1016/j.conengprac.2025.106510","url":null,"abstract":"<div><div>In agriculture, most of the water for irrigation is transported by means of open-flow channel networks. To ensure their optimal operation, it is very important to monitor all system state variables accurately. This paper proposes a new state estimation scheme able to mitigate the effect of unknown inputs (e.g., user demands, seepage and rain) and noise based on a robust unknown input observer (RUIO) that expresses the canal control-oriented model as a one-sided Lipschitz (OSL) quadratically inner bounded (QIB) system. The modeling methodology also includes the discharges of each gate, along with a transition flow that considers the effect of potential energy (channel slope) and kinetic energy (velocity in the transport of matter and frictional losses). The performance of the proposed observer is evaluated on the Corning channel benchmark using data provided by SIC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, which is a high-fidelity simulator that solves numerically the Saint-Venant equations and thus generates data that is close to the real canal operation. The obtained results demonstrate that the RUIO is capable of estimating the upstream heights from the downstream height measurements (which are subject to noise and unknown inputs), hence showing that this strategy can lead to savings in terms of required sensors.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106510"},"PeriodicalIF":4.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867482","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}

引用次数: 0

Onboard sensors-based road surface roughness identification using multi-module LSTM-DKF algorithm 基于多模块LSTM-DKF算法的车载传感器路面粗糙度识别

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-19 DOI: 10.1016/j.conengprac.2025.106530

Shaohua Li , Jianwei Li , Xuewei Wang , Zekun Yang

{"title":"Onboard sensors-based road surface roughness identification using multi-module LSTM-DKF algorithm","authors":"Shaohua Li , Jianwei Li , Xuewei Wang , Zekun Yang","doi":"10.1016/j.conengprac.2025.106530","DOIUrl":"10.1016/j.conengprac.2025.106530","url":null,"abstract":"<div><div>To realize the preview control of the intelligent chassis suspension and improve the vehicle ride comfort based on onboard sensors, an accurate and rapid road roughness identification algorithm is proposed, which considers varying road conditions at all wheels using data-model fusion method integration. Multi-module long short-term memory network combined with a discrete Kalman filter (LSTM-DKF) is proposed in this paper. The algorithm employs parallel LSTM neural networks for each wheel, leveraging vehicle response data obtained from onboard sensors. The hyperparameters of the LSTM networks are optimized using a genetic algorithm to ensure accurate identification of road surface levels. Furthermore, the noise matrix within the discrete Kalman filter of each sub-module is iteratively updated based on the identified road surface level. Therefore, multi-module LSTM-DKF can adaptively identify the road surface roughness under four wheels simultaneously in complex road conditions. Simulation and vehicle field test results show that the proposed multi-module LSTM-DKF can quickly and accurately identify the level and profile of road roughness. Compared with the road roughness identification algorithm based on Kalman filter, the multi-module LSTM-DKF can improve the correlation coefficient <em>r</em> of the identification results by 3.11%, and reduce both the root mean square error (RMSE) and maximum absolute error (MAE) by more than 20%. Those outcomes validate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106530"},"PeriodicalIF":4.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867481","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}

引用次数: 0

Explicit machine learning-based MPC for distributed control of nonlinear processes 基于显式机器学习的MPC非线性过程分布式控制

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-18 DOI: 10.1016/j.conengprac.2025.106534

Wenlong Wang , Yuhe Tian , Zhe Wu

{"title":"Explicit machine learning-based MPC for distributed control of nonlinear processes","authors":"Wenlong Wang , Yuhe Tian , Zhe Wu","doi":"10.1016/j.conengprac.2025.106534","DOIUrl":"10.1016/j.conengprac.2025.106534","url":null,"abstract":"<div><div>This work develops an explicit machine learning-based model predictive control method (explicit ML-MPC) for real-time dynamic control of chemical processes. Traditional ML-MPC for dynamic control of chemical processes in Aspen Plus Dynamics often struggles with computational efficiency. While explicit ML-MPC reduces computation time by converting ML-based optimization problems of implicit ML-MPC to mixed-integer quadratic programming (MIQP), the computational complexity grows rapidly with the dimensionality of the decision space for complex chemical process networks. To overcome this limitation, a distributed control strategy within the explicit ML-MPC framework is introduced, using multiprocessing for parallel computation to further improve the real-time performance of explicit ML-MPC. A k-d tree-based search algorithm is also developed to construct MIQP problems efficiently in real time. The entire control framework is developed as an open-source, generalizable code repository that allows users to easily design, implement, and customize real-time ML-MPC controllers within Aspen Plus Dynamics, which could significantly accelerate the adoption of advanced control strategies in the chemical industry. Finally, the effectiveness of the proposed approach is demonstrated through the closed-loop control of a chemical process network in Aspen Plus Dynamics, showing notable improvements in computational efficiency.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106534"},"PeriodicalIF":4.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860983","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}

引用次数: 0

Trajectory tracking and load anti-swing control for quadrotor-slung-load system under time-varying disturbances: A dual time-varying uncertainty and disturbance estimator-based approach 时变扰动下四旋翼悬挂系统的轨迹跟踪与负载抗摆控制：一种基于时变不确定性和扰动估计的双方法

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-18 DOI: 10.1016/j.conengprac.2025.106518

Huiting Bai, Shutao Qi, Ruijie Hu, Yuanzhuo Zhang, Qinglin Zeng, Yang Zhu

{"title":"Trajectory tracking and load anti-swing control for quadrotor-slung-load system under time-varying disturbances: A dual time-varying uncertainty and disturbance estimator-based approach","authors":"Huiting Bai, Shutao Qi, Ruijie Hu, Yuanzhuo Zhang, Qinglin Zeng, Yang Zhu","doi":"10.1016/j.conengprac.2025.106518","DOIUrl":"10.1016/j.conengprac.2025.106518","url":null,"abstract":"<div><div>In aerial transportation and delivery of a slung load by a quadrotor, existing control frameworks with a single, fixed-parameter-based disturbance estimator might not sufficiently consider and address the time-varying disturbances present in both quadrotor trajectory tracking and load anti-swing control loops, particularly during different flight phases. To better reject these disturbances, we propose a dual, time-varying uncertainty and disturbance estimator (TVUDE)-based control framework. In the framework, the fixed parameter of the classic disturbance estimator is replaced by a time-varying parameter. This replacement enables the estimator to better respond to the bandwidth and amplitude change of the disturbance, rather than seeking an ”optimal” constant parameter once and for all. Furthermore, two TVUDEs are designed to respectively reject the disturbances in both two control loops such that good overall robustness against multi-loop disturbances is guaranteed. Comparative simulation and experimental results validate the performance superiority of the proposed framework over existing methods.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106518"},"PeriodicalIF":4.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860984","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}

引用次数: 0

Offset-free stochastic quadratic dynamic matrix control formulations using polynomial chaos expansions 使用多项式混沌展开的无偏移随机二次动态矩阵控制公式

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-14 DOI: 10.1016/j.conengprac.2025.106514

Wallace Gian Yion Tan , Krystian Ganko , Srimanta Santra , Matthias von Andrian , Richard D. Braatz

{"title":"Offset-free stochastic quadratic dynamic matrix control formulations using polynomial chaos expansions","authors":"Wallace Gian Yion Tan , Krystian Ganko , Srimanta Santra , Matthias von Andrian , Richard D. Braatz","doi":"10.1016/j.conengprac.2025.106514","DOIUrl":"10.1016/j.conengprac.2025.106514","url":null,"abstract":"<div><div>Probabilistic uncertainties in the model parameters result in distributional uncertainties in the model predictions. While such uncertainty descriptions have been incorporated into model predictive control (MPC) formulations using polynomial chaos theory (PCT), more care is required to ensure integral action than in traditional MPC. This article thoroughly examines offset-free formulations of PCT-based MPC for multiple-input, multiple-output linear time-invariant systems. We compile, prove, and validate features of multiple stochastic MPC formulations. Under mild assumptions, these features include (i) guarantees for the existence of a full column-rank integrator to eliminate offset in multiple performance indices; (ii) guarantees of nominal closed-loop stability for the unconstrained systems, and (iii) computationally efficient, spectrally accurate resolution of parametric uncertainty. Application of our stochastic MPC formulations to setpoint tracking and disturbance rejection in numerical case studies demonstrate the asymptotic removal of offset in <em>all</em> higher-order contributions to output variation due to parametric uncertainty.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106514"},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827768","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}

引用次数: 0

Autonomous UAV last-mile delivery in urban environments: A survey on deep learning and reinforcement learning solutions 自主无人机在城市环境中的最后一英里交付：深度学习和强化学习解决方案的调查

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-14 DOI: 10.1016/j.conengprac.2025.106491

Jingrui Guo , Yangyang Zhou , Laurent Burlion , Andrey V. Savkin , Chao Huang

{"title":"Autonomous UAV last-mile delivery in urban environments: A survey on deep learning and reinforcement learning solutions","authors":"Jingrui Guo , Yangyang Zhou , Laurent Burlion , Andrey V. Savkin , Chao Huang","doi":"10.1016/j.conengprac.2025.106491","DOIUrl":"10.1016/j.conengprac.2025.106491","url":null,"abstract":"<div><div>This survey investigates the convergence of deep learning (DL) and reinforcement learning (RL) for unmanned aerial vehicle (UAV) applications, particularly in autonomous last-mile delivery. The ongoing growth of e-commerce heightens the need for advanced UAV technologies to overcome urban logistics challenges, including navigation and package delivery. DL and RL offer promising methods for object detection, path planning, and decision-making, enhancing delivery efficiency. However, significant challenges persist, particularly regarding scalability, computational constraints, and adaptation to volatile urban settings. Large UAV fleets and intricate city environments exacerbate scalability issues, while limited onboard processing capacity hinders the use of computationally intensive DL and RL models. Moreover, adapting to unpredictable conditions demands robust navigation strategies. This survey emphasizes hybrid approaches that integrate supervised and reinforcement learning or employ transfer learning to adapt pre-trained models. Techniques like model based RL and domain adaptation are highlighted as potential pathways to improve generalizability and bridge the gap between simulation and real-world deployment.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106491"},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827769","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}

引用次数: 0

A dynamic convergence second-order sliding mode control structure for PMSM with an observer based on a novel sliding mode reaching law 基于新型滑模趋近律的PMSM二阶滑模动态收敛控制结构

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-08-13 DOI: 10.1016/j.conengprac.2025.106504

Zhang Zhang, Zerong Chen, Neng Li, Kaiwen Chen, N.C. Cheung, Jianfei Pan

{"title":"A dynamic convergence second-order sliding mode control structure for PMSM with an observer based on a novel sliding mode reaching law","authors":"Zhang Zhang, Zerong Chen, Neng Li, Kaiwen Chen, N.C. Cheung, Jianfei Pan","doi":"10.1016/j.conengprac.2025.106504","DOIUrl":"10.1016/j.conengprac.2025.106504","url":null,"abstract":"<div><div>Sliding mode control (SMC) is widely used to improve the speed control performance of permanent magnet synchronous motors (PMSMs). However, challenges such as chattering and implementation difficulties often make traditional SMC ineffective and difficult to implement in practice. This paper proposes a dynamic convergence second-order sliding mode control (SOSMC) structure to solve these problems. This method reduces the reliance on sufficiently large switching gains, effectively addressing the conflict between disturbance rejection and chattering reduction. Specifically, this proposed method outlines principles for dynamically adjusting the convergence process and enhance disturbance rejection capability. Furthermore, it contains an adaptive switching gain influenced by the convergence of the system state to enhance its overall performance. Compared to conventional methods, this approach demonstrates significant improvements in speed drop reduction and settling time, achieving 97.3% and 86.9% of the performance of the controller with disturbance observer (DOB), respectively. To verify the effectiveness of the proposed structure, a fuzzy-based adaptive method is designed and integrated with the control structure. Apart from the proposed SOSMC structure, a sliding mode disturbance observer (SMDO) is designed with a novel sliding mode reaching law (SMRL) to reduce chattering issue and improve disturbance rejection in PMSM systems, showing faster convergence and greater accuracy compared with traditional SMRLs. Finally, both simulation and experimental results are conducted to demonstrate the effectiveness of the proposed methods.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106504"},"PeriodicalIF":4.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829636","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}

引用次数: 0