Control Engineering Practice最新文献

{"title":"Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle","authors":"Siyuan Guo,&nbsp;Zhi Ning,&nbsp;Ming Lv","doi":"10.1016/j.conengprac.2024.106048","DOIUrl":"10.1016/j.conengprac.2024.106048","url":null,"abstract":"<div><p>The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"152 ","pages":"Article 106048"},"PeriodicalIF":5.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012624","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":"Digital twin-enabled autonomous fault mitigation in diesel engines: An experimental validation","authors":"Raj Pradip Khawale ,&nbsp;Dhrubajit Chowdhury ,&nbsp;Raman Goyal ,&nbsp;Shubhendu Kumar Singh ,&nbsp;Ankur Bhatt ,&nbsp;Brian Gainey ,&nbsp;Benjamin Lawler ,&nbsp;Lara Crawford ,&nbsp;Rahul Rai","doi":"10.1016/j.conengprac.2024.106045","DOIUrl":"10.1016/j.conengprac.2024.106045","url":null,"abstract":"<div><p>Due to the growing demand for robust autonomous systems, automating maintenance and fault mitigation activities has become essential. If an unexpected fault occurs during the travel, the system should be able to manage that fault autonomously and continue its mission. Thus, a robust fault mitigation system is needed that can quickly reconfigure itself in an optimal way. This paper presents a novel digital twin-based fault mitigation strategy that uses hierarchical control architecture. Here, a computationally efficient high-fidelity hybrid engine model is developed to simulate actual engine behavior. This hybrid engine model includes a neural network model representing the cylinder combustion process and well-studied physics-based analytical equations describing the remaining subsystems. This architecture uses a feedback controller on top of the control calibration map, generated offline using the hybrid model, to mitigate faults and modeling errors. The fault mitigation strategies are calibrated and validated through model-in-loop (MIL) and hardware-in-loop (HIL) simulations for various operating points using the Navistar 7.6 liters six-cylinder engine. The effectiveness of the proposed architecture in handling injector nozzle clogging, intake manifold leaks, and pressure shift faults is illustrated. The results demonstrate that the proposed architecture can completely overcome faults and maintain the desired torque in a few seconds. Moreover, the average accuracy of 96% is observed for the engine model compared to experimental data. It is anticipated that the proposed end-to-end architecture will be easily deployable on unmanned marine vessels and can be extended to accommodate other component faults.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"152 ","pages":"Article 106045"},"PeriodicalIF":5.4,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998352","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":"Research on ground mobile robot trajectory tracking control based on MPC and ANFIS","authors":"Yulong You ,&nbsp;Zhong Yang ,&nbsp;Hao-ze Zhuo ,&nbsp;Yaoyu Sui","doi":"10.1016/j.conengprac.2024.106040","DOIUrl":"10.1016/j.conengprac.2024.106040","url":null,"abstract":"<div><p>This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"152 ","pages":"Article 106040"},"PeriodicalIF":5.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998350","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":"Adaptive gain design for Zero-Order Hold discrete-time implementation of explicit reference governor","authors":"Mu’taz A. Momani,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.conengprac.2024.106042","DOIUrl":"10.1016/j.conengprac.2024.106042","url":null,"abstract":"<div><p>Explicit reference governor (ERG) is an <em>add-on</em> unit that provides constraint handling capability to pre-stabilized systems. The main idea behind ERG is to manipulate the derivative of the applied reference in continuous time such that the satisfaction of state and input constraints is guaranteed at all times. However, ERG should be practically implemented in discrete-time. This paper studies the discrete-time implementation of ERG, and provides conditions under which the feasibility and convergence properties of the ERG framework are maintained when the updates of the applied reference are performed in discrete time. Specifically, using Zero-Order Hold (ZOH) discretization method, we develop an adaptive algorithm to adjust the gain of the discretized term based on actual measurements to maintain all properties of ERG when implemented in discrete-time. The proposed approach is validated via extensive simulation and experimental studies.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"152 ","pages":"Article 106042"},"PeriodicalIF":5.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998351","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":"Hybrid offline–online neural identification-based robust adaptive tracking control for quadrotors","authors":"Yijia Zhou ,&nbsp;Jian Di ,&nbsp;Haibo Ji ,&nbsp;Jiulong Wang ,&nbsp;Shaofeng Chen ,&nbsp;Yu Kang","doi":"10.1016/j.conengprac.2024.106032","DOIUrl":"10.1016/j.conengprac.2024.106032","url":null,"abstract":"<div><p>This paper proposes a novel hybrid offline–online neural identification-based robust adaptive control strategy for quadrotors subject to parameter uncertainties and external disturbances. A new method of using hybrid offline–online neural identification is developed to compensate for the residual force and moment caused by parameter uncertainties. Unlike previous methods that ignore the relevance of uncertainties in the time dimension, the proposed neural identification method mines temporal features of states from historical data by introducing long short-term memory (LSTM) networks, resulting in high identification accuracy. Furthermore, an online adaptation update law is designed to optimize the weights of the network estimates for strong robustness. Consequently, based on the identification of the network, a robust tracking controller on <span><math><mrow><mi>S</mi><mi>E</mi><mrow><mo>(</mo><mn>3</mn><mo>)</mo></mrow></mrow></math></span> is constructed, which is capable of attenuating the bounded disturbances by introducing anti-disturbance components. Finally, numerical simulations and experiments in the real physical world are carried out to verify the performance. The experimental results demonstrate that the proposed strategy not only achieves more accurate uncertainty identification in comparison to the existing methods, but also realizes a 44.28% reduction in the root-mean-square error (RMSE) of the position under the lump uncertainties, which illustrates enhanced robustness and generalizability. Video: <span><span>https://youtu.be/3kIG5fcQaVE</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106032"},"PeriodicalIF":5.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979492","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 multiparametric approach to accelerating ReLU neural network based model predictive control","authors":"Dustin Kenefake ,&nbsp;Rahul Kakodkar ,&nbsp;Sahithi S. Akundi ,&nbsp;Moustafa Ali ,&nbsp;Efstratios N. Pistikopoulos","doi":"10.1016/j.conengprac.2024.106041","DOIUrl":"10.1016/j.conengprac.2024.106041","url":null,"abstract":"<div><p>Model Predictive Control (MPC) is a wide spread advanced process control methodology for optimization based control of multi-input and multi-output processes systems. Typically, a surrogate model of the process dynamics is utilized to predict the future states of a process as a function of input actions and an initial state. The predictive model is often a linear model, such as a state space model, due to the computational burden of the resulting optimization problem when utilizing nonlinear models. Recently, rectified linear unit (ReLU) based neural networks (NN) were shown to be mixed integer linear representable, thus allowing their incorporation into mixed integer programming (MIP) frameworks. However, the resulting MIP-based MPC problems are often computationally intractable to solve in real-time. The computational intractability of the reformulated NN-based optimization models is typically addressed in the literature by applying some form of bounds tightening approach. However, this in itself may have a large computational cost. In this work, a novel bound tightening procedure based on a multiparametric (MP) programming formulation of the corresponding MIP reformulated MPC optimization problems is proposed. Which tightening only needs to be computed and applied once-and-offline, thereby significantly improving the computational performance of the MPC in real-time. Some aspects of the effect of regularization during NN regression on the computational difficulty of these optimization problems are also investigated in conjunction with the proposed a priori bounds-tightening approach. The proposed method is compared to the base case without the parametric tightening procedure, as well as NN regularization through two optimal control case studies: (1) A ReLU NN-based MPC of an unstable nonlinear chemostat and, (2) a ReLU NN-based MPC of a nonlinear continuously stirred tank reactor (CSTR). Significant reductions in average time of 99.96% and 91.90% are observed, for the chemostat NN based MPC and CSTR NN based MPC, respectively.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106041"},"PeriodicalIF":5.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979493","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 physics guided data-driven prediction method for dynamic and static feature fusion modeling of rolling force in steel strip production","authors":"Yong Song ,&nbsp;Wendan Xiao ,&nbsp;Fenjia Wang ,&nbsp;Junliang Li ,&nbsp;Feifei Li ,&nbsp;Anrui He ,&nbsp;Chao Liu","doi":"10.1016/j.conengprac.2024.106039","DOIUrl":"10.1016/j.conengprac.2024.106039","url":null,"abstract":"<div><p>The accuracy of rolling force prediction is key to improving the precision of strip thickness control. The compressive load required for the strip in the rolling process is not only related to the size of the billet and process parameters such as deformation speed, temperature, and reduction, but also to the deformation boundary conditions of the billet between the rolls, such as the wear state of the rolls, lubrication conditions, etc. These influencing factors are interrelated and constantly changing, which is particularly prominent in small-batch and multi-specification intermittent production modes. The existing rolling force prediction models are constructed based on the rolling deformation mechanism through numerous simplifications. Due to challenges in fully and accurately characterizing various complex rolling deformation processes, their mapping relationships with process parameters, and constantly changing boundary conditions, the accuracy of the simplified rolling force prediction model is difficult to meet the control requirements of actual production. This paper proposes a physics-guided data-driven (PGDD) rolling force modeling method. It separates rolling condition features into static and dynamic parts using mechanistic and empirical knowledge and introduces a machine learning framework that integrates these parts for modeling. In this framework, the static feature fitting part can establish the influence of process parameters such as billet chemical composition, size, rolling speed, temperature, etc. on the rolling force. Meanwhile, the dynamic feature fitting part is responsible for the collaborative modeling of influencing factors reflecting the evolution rules of roll state, learning the cumulative effects of various complex processing states from a large amount of time-series data formed by different combinations of rolling conditions. Experiments with real production condition data show that the proposed physics-guided data-driven modeling method can accurately predict the rolling force under complex and variable conditions, and its adaptability and accuracy are superior to the online original model and traditional data-driven model.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106039"},"PeriodicalIF":5.4,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963996","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":"Hybrid model- and learning-based fault diagnosis in adaptive buildings","authors":"Jonas Stiefelmaier,&nbsp;Michael Böhm,&nbsp;Oliver Sawodny,&nbsp;Cristina Tarín","doi":"10.1016/j.conengprac.2024.106037","DOIUrl":"10.1016/j.conengprac.2024.106037","url":null,"abstract":"<div><p>Adaptive buildings offer an enormous potential for saving resources and reducing emissions due to their ability to actively compensate deformations, which allows for a significantly lighter supporting structure. The long-term autonomous operation of an adaptive building, a prerequisite for its efficiency, requires the accurate detection and isolation of faults in its sensors and actuators. However, conventional model-based approaches achieve inadequate performance in case of substantial model errors. In that context, this article investigates the potential of integrating unsupervised learning techniques into model-based diagnosis schemes to improve the diagnostic accuracy. Specifically, we propose to train an autoencoder, a type of neural network, to suppress the effects of model errors in parity space residuals. A publicly available dataset of measurements from an adaptive high-rise building is introduced and used for the experimental validation of the proposed diagnosis method. The results are discussed in relation to a similar approach based on the principal component analysis (PCA), as well as standalone model- or learning-based approaches as reference. In different test scenarios, either the autoencoder- or the PCA-based approach is able to suppress the effects of model errors more effectively, yielding a more accurate fault detection. The PCA-based approach however allows for a more accurate fault isolation due to the exact propagation of the considered probability distributions.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106037"},"PeriodicalIF":5.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0967066124001965/pdfft?md5=3ebc97cab8a56a4afa7fa28abc577947&pid=1-s2.0-S0967066124001965-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear active disturbance rejection control for large onshore wind turbines in full wind speed range","authors":"Chengzhen Jia ,&nbsp;Hua Geng ,&nbsp;Yushan Liu ,&nbsp;Lingmei Wang ,&nbsp;Enlong Meng ,&nbsp;Jiwen Ji ,&nbsp;Zhengkun Chen ,&nbsp;Lei Han ,&nbsp;Liming Chen ,&nbsp;Dongjie Guo ,&nbsp;Jiye Liang ,&nbsp;Yinping Fenghong","doi":"10.1016/j.conengprac.2024.106038","DOIUrl":"10.1016/j.conengprac.2024.106038","url":null,"abstract":"<div><p>To achieve real-time estimation and compensation of total system disturbances and improve the control performance of wind turbines under complex turbulent wind conditions, three one-order LADRCs were used to reconstruct the wind turbine core control system. A dynamic variable limit LADRC was designed for torque control, a minimum limit LADRC was applied in pitch control, and a LADRC power controller was designed for decoupling torque and pitch control. The stability of the LADRCs was proven using the Lyapunov method. According to the transfer function of wind turbines and empirical equations, the parameters of each LADRC were tuned. Based on the hardware-in-loop simulation (HILS) test platform, the control algorithm of look-up table, PID, RISC, and LADRC were constructed by PLC language. Through comparative studies, it was verified that the algorithm proposed in this paper can reduce generator rotor speed and power fluctuations by about 13.6% and 1.7% at least, and it can also reduce the blade root load force.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106038"},"PeriodicalIF":5.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0967066124001977/pdfft?md5=3e03d6dcc8abbdd3a16767e3a422d19b&pid=1-s2.0-S0967066124001977-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model predictive control of a magnetic levitation system with prescribed output tracking performance","authors":"Benedikt Oppeneiger ,&nbsp;Lukas Lanza ,&nbsp;Maximilian Schell ,&nbsp;Dario Dennstädt ,&nbsp;Manuel Schaller ,&nbsp;Bert Zamzow ,&nbsp;Thomas Berger ,&nbsp;Karl Worthmann","doi":"10.1016/j.conengprac.2024.106018","DOIUrl":"10.1016/j.conengprac.2024.106018","url":null,"abstract":"<div><p>To guarantee the safe and dependable operation of a magnetic levitation train, the distance between the magnet and the reaction rail needs to be kept within a given range. In this work, we design model predictive controllers which, in addition to complying with these constraints, provide a favorable behavior with regard to performance criteria such as travel comfort and control effort. For this purpose, we present a model of the system and the disturbances affecting it. Several results regarding the mathematical properties of this model are proven to gain insight for controller design. Finally we compare three different controllers w.r.t. performance criteria such as robustness, travel comfort, control effort, and computation time in an extensive numerical simulation study: a linear feedback controller, a model predictive control (MPC) scheme with quadratic stage costs, and the recently-proposed funnel MPC scheme. We show that the MPC closed loop complies with the constraints while also exhibiting excellent performance. Furthermore, we implement the MPC algorithms within the <span>GRAMPC</span> framework. This allows us to reduce the computational effort to a point at which real-time application becomes feasible.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"151 ","pages":"Article 106018"},"PeriodicalIF":5.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0967066124001771/pdfft?md5=d0fc476f063c82ac7097bcc00e456750&pid=1-s2.0-S0967066124001771-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}