Control Engineering Practice最新文献_第4页

Enhanced trajectory tracking for AUVs: Adaptive Super-Twisting Sliding Mode Control with disturbance observer 基于扰动观测器的auv自适应超扭转滑模控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-11 DOI: 10.1016/j.conengprac.2025.106426

Zhiwei Sun , Bohan Wang , ZhiYan Li , Yuanyuan Zhao , Ting Huang , Xi Li , Yong Zhao , Peng Qian , Dahai Zhang

{"title":"Enhanced trajectory tracking for AUVs: Adaptive Super-Twisting Sliding Mode Control with disturbance observer","authors":"Zhiwei Sun , Bohan Wang , ZhiYan Li , Yuanyuan Zhao , Ting Huang , Xi Li , Yong Zhao , Peng Qian , Dahai Zhang","doi":"10.1016/j.conengprac.2025.106426","DOIUrl":"10.1016/j.conengprac.2025.106426","url":null,"abstract":"<div><div>This paper proposes a novel composite disturbance rejection controller that enhances the trajectory tracking performance of Autonomous Underwater Vehicles (AUVs), which is essential for achieving autonomy and precise navigation despite uncertainties in model parameters and time-varying disturbances. The controller adeptly merges Adaptive Super-Twisting Sliding Mode Control (ASTSMC) with a Super-Twisting Nonlinear Disturbance Observer (STNDO), which has effectively enhanced its ability to counteract disturbances. It is worth noting that the controller operates without precise model information and prior knowledge of disturbance boundaries, and this feature offers high adaptability. Due to its adaptive law and a reduced parameter set, the controller offers parameters that are not only straightforward to tune but also exceptionally suitable for practical deployment. The stability of the closed-loop system with the composite disturbance rejection controller is validated via the Lyapunov concept. Numerical simulations conclusively demonstrate the controller’s superiority in countering time-varying external disturbances, even under hydrodynamic parameter errors of up to 40%. Moreover, real-time experiments across different operating conditions have validated the effectiveness and robustness of the proposed controller, which showcases its potential as a robust solution for advanced AUV operations.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106426"},"PeriodicalIF":5.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255429","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

Data-driven dynamic modeling of renewable CO2 emissions in multimode industrial co-processing processes 多模式工业协同加工过程中可再生CO2排放的数据驱动动态建模

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-10 DOI: 10.1016/j.conengprac.2025.106424

Liang Cao , Jianping Su , Yankai Cao , Lim C. Siang , Gary Lee , Jin Li , R. Bhushan Gopaluni

{"title":"Data-driven dynamic modeling of renewable CO2 emissions in multimode industrial co-processing processes","authors":"Liang Cao , Jianping Su , Yankai Cao , Lim C. Siang , Gary Lee , Jin Li , R. Bhushan Gopaluni","doi":"10.1016/j.conengprac.2025.106424","DOIUrl":"10.1016/j.conengprac.2025.106424","url":null,"abstract":"<div><div>Accurate modeling and real-time monitoring of renewable CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions in biofeedstock co-processing technologies are critical yet challenging, hindered by limited experimental data and static operational assumptions. This study introduces a novel data-driven dynamic modeling approach using an extensive dataset comprising 43,662 samples from the Parkland refinery. We implement change point detection algorithms to automatically partition the data into segments corresponding to different operating conditions and develop segment-specific robust regression models to predict CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. The proposed framework uniquely integrates change point detection with robust regression, forming a dynamic monitoring system that continuously adapts to multimode industrial processes while balancing numerical accuracy, interpretability, and computational efficiency. These findings reveal that the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission ratio per unit of biofeedstock to fossil fuels fluctuates between 51% and 82% under varying operating conditions. The dynamic model exhibits strong agreement with experimental data, providing refineries with a practical, reliable tool for real-time emissions monitoring and regulatory compliance in industrial co-processing applications.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106424"},"PeriodicalIF":5.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241252","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

Towards reliable control: Uncertainty-aware domain preserving stacked auto-encoder for data-driven modeling in large-scale industrial systems 迈向可靠控制：面向大规模工业系统数据驱动建模的不确定性感知域保留堆叠自编码器

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-10 DOI: 10.1016/j.conengprac.2025.106383

Yijing Fang , Zhaohui Jiang , Weihua Gui , Ling Shen

{"title":"Towards reliable control: Uncertainty-aware domain preserving stacked auto-encoder for data-driven modeling in large-scale industrial systems","authors":"Yijing Fang , Zhaohui Jiang , Weihua Gui , Ling Shen","doi":"10.1016/j.conengprac.2025.106383","DOIUrl":"10.1016/j.conengprac.2025.106383","url":null,"abstract":"<div><div>Online monitoring of operational states and quality indices in industrial processes is a vital source of information for enhancing production efficiency. This is particularly true for large-scale industrial systems, where existing methods often overlook the influence of uncertain information from abrupt operational fluctuations under time-varying processes and random noise during data collection and measurement. To address this issue, this paper proposes an uncertainty-aware key-domain-preserving stacked auto-encoder (UADP-SAE) model developed to capture the spatiotemporal distribution characteristics of dynamic operational changes in industrial systems. Based on this, an uncertainty quantification framework is designed to guide feature learning of the model by using uncertainty estimates. In addition, heteroscedastic uncertainty related to input noise is incorporated into the loss function, mitigating the adverse effects of high-uncertainty data on feature learning and enhancing the reliability of knowledge acquisition. Finally, the proposed method is validated on a real-world dataset from a large-scale industrial ironmaking system. Experimental results demonstrate that the proposed method outperforms traditional methods, achieving almost 10% improvement across multiple evaluation metrics for the prediction of three sintered ore quality indicators.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106383"},"PeriodicalIF":5.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255430","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

Parameter optimization of nonlinear friction models and trajectory control of linear motor stage 非线性摩擦模型参数优化与直线电机工作台轨迹控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-06 DOI: 10.1016/j.conengprac.2025.106439

Her-Terng Yau , Yu-Tsun Chen

{"title":"Parameter optimization of nonlinear friction models and trajectory control of linear motor stage","authors":"Her-Terng Yau , Yu-Tsun Chen","doi":"10.1016/j.conengprac.2025.106439","DOIUrl":"10.1016/j.conengprac.2025.106439","url":null,"abstract":"<div><div>Achieving high positioning accuracy with low-resolution encoders is vital for reducing sensor costs in industrial systems. However, nonlinear friction between linear motors and sliding rails, especially at low velocities, causes hysteresis and creep, severely affecting motion accuracy. To address this, we propose a high-precision control method that does not rely on high-resolution encoders. By separating static and dynamic friction modeling, and using particle swarm optimization (PSO) to tune parameters, the proposed models achieve RMSEs of 0.43 μm and 1.14 mm, respectively. Our novel friction feedforward compensation strategy automatically switches between these models based on experimentally determined thresholds, helping the feedback controller reduce nonlinear disturbances. Furthermore, we develop a friction feedforward PD-type iterative learning control (FFPDILC), integrating PID and PDILC with friction compensation. This enhances the learning effect and improves convergence speed. Experiments show a significant RMSE reduction from 18 μm to 0.83 μm (95.4 % improvement), and tracking errors are reduced by 51.1 % compared to PDILC under standard conditions and 48.9 % under additional load conditions. These results validate the method's effectiveness in improving precision under limited sensor resolution.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106439"},"PeriodicalIF":5.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229857","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

Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies 从非均匀观测中恢复非线性动力学：一种基于物理的识别方法与实际案例研究

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-05 DOI: 10.1016/j.conengprac.2025.106411

Cesare Donati , Martina Mammarella , Fabrizio Dabbene , Carlo Novara , Constantino Lagoa

{"title":"Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies","authors":"Cesare Donati , Martina Mammarella , Fabrizio Dabbene , Carlo Novara , Constantino Lagoa","doi":"10.1016/j.conengprac.2025.106411","DOIUrl":"10.1016/j.conengprac.2025.106411","url":null,"abstract":"<div><div>Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-uniform observations, i.e., data scenarios that include missing measurements, multiple runs, or aggregated observations. The goal is to provide a general method for recovering the dynamics of nonlinear systems from non-uniform data, enabling accurate tracking of system behavior over time. The approach integrates domain-specific physical principles with black-box models, overcoming the limits of traditional linear or purely black-box methods. The description of this novel framework is supported by a theoretical study on the effect of non-uniform observations on the accuracy of parameter estimation. Specifically, we demonstrate the existence of upper bounds on the parametric error resulting from missing measurements and aggregated observations. Then, the effectiveness of the approach is demonstrated through two case studies. These include a practical application with missing samples, i.e., the identification of a continuous stirred-tank reactor using real data, and a simulated Lotka–Volterra system under aggregated observations. The results highlight the ability of the framework to robustly estimate the system parameters and to accurately reconstruct the model dynamics despite the availability of non-uniform measurements.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106411"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221893","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

Machine scheduling optimization via multi-strategy information-aware genetic algorithm in steelmaking continuous casting industrial process 基于多策略信息感知遗传算法的炼钢连铸工艺调度优化

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-05 DOI: 10.1016/j.conengprac.2025.106404

Lin Guan , Yalin Wang , Xujie Tan , Chenliang Liu , Weihua Gui

{"title":"Machine scheduling optimization via multi-strategy information-aware genetic algorithm in steelmaking continuous casting industrial process","authors":"Lin Guan , Yalin Wang , Xujie Tan , Chenliang Liu , Weihua Gui","doi":"10.1016/j.conengprac.2025.106404","DOIUrl":"10.1016/j.conengprac.2025.106404","url":null,"abstract":"<div><div>Scheduling optimization of the steelmaking continuous casting (SCC) process is vital for enhancing production efficiency and reducing costs in steel enterprises. However, the stringent requirement for uninterrupted casting and the severe imbalance in machine allocation present significant challenges for conventional intelligent optimization algorithms. In particular, these algorithms face the problem of balancing global exploration, local exploitation, and limited population diversity. To this end, this paper proposes a novel multi-strategy information-aware genetic algorithm (MSIAGA) that integrates the requirement for uninterrupted production with the synergy of multiple innovative strategies to achieve optimal equipment scheduling. First, a continuous production-guided chromosome encoding method is developed to ensure that equipment scheduling strictly adheres to uninterrupted casting conditions. Second, a dynamically adaptive mutation strategy is designed to enhance the global exploration and local search capabilities of the model. In addition, a dynamic elite retention strategy is introduced, utilizing retention scores to prioritize elite solutions and enhance population diversity. Finally, extensive experimental results of 21 SCC scheduling cases with different complexity demonstrate that the proposed method outperforms several other representative methods.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106404"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212127","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

Quality prediction of a fermentation process using multi-scale GRU with hybrid modeling strategy 基于混合建模策略的多尺度GRU发酵过程质量预测

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-05 DOI: 10.1016/j.conengprac.2025.106408

Jiakai Zheng, Zheng Zhou, Shunyi Zhao, Xiaoli Luan, Fei Liu

{"title":"Quality prediction of a fermentation process using multi-scale GRU with hybrid modeling strategy","authors":"Jiakai Zheng, Zheng Zhou, Shunyi Zhao, Xiaoli Luan, Fei Liu","doi":"10.1016/j.conengprac.2025.106408","DOIUrl":"10.1016/j.conengprac.2025.106408","url":null,"abstract":"<div><div>Fermentation is a crucial process in the production of food, pharmaceuticals, and other products, but its inherent complexity often makes it challenging to consistently meet quality standards. This paper proposes a multi-scale gated recurrent unit-based quality prediction method using a hybrid modeling strategy to enhance product quality consistency. This hybrid approach integrates the advantages of regression models in extracting precise and effective features with the superior performance of classification models in handling complex decision boundaries. Specifically, the regression network maps process variables to quality-related features, while the classification network then assigns these features to the corresponding product quality categories. Additionally, changes in the metabolic demands of microorganisms and the continuous consumption of substrates give rise to multi-stage dynamics, thereby further complicating the modeling process. To address this issue, a multi-stage segmentation approach is implemented within the classification network to more effectively capture the distinct phases of fermentation. Moreover, stages with minimal impact on training performance are excluded, streamlining and accelerating the model training process. Experimental results demonstrate that the proposed approach significantly improves the accuracy of quality predictions, thereby achieving more consistent quality control in fermentation processes.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106408"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221895","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

Optimizing industrial oven temperature uniformity: A model predictive control framework with rapid control prototyping 优化工业烘箱温度均匀性：一个具有快速控制原型的模型预测控制框架

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-05 DOI: 10.1016/j.conengprac.2025.106409

Leandro Pitturelli, Davide Previtali, Fabio Previdi, Antonio Ferramosca

{"title":"Optimizing industrial oven temperature uniformity: A model predictive control framework with rapid control prototyping","authors":"Leandro Pitturelli, Davide Previtali, Fabio Previdi, Antonio Ferramosca","doi":"10.1016/j.conengprac.2025.106409","DOIUrl":"10.1016/j.conengprac.2025.106409","url":null,"abstract":"<div><div>Industrial ovens are pivotal in manufacturing, particularly food processing, electronics, and materials fabrication. In this context, temperature control algorithms must satisfy demanding control specifications, among which: setpoint tracking, disturbance rejection, energy saving, and actuator limitations. Specifically, one of the critical challenges in industrial ovens is achieving a uniform temperature distribution within the oven cavity, especially in the presence of disturbances. This paper focuses on a particular kind of industrial ovens installed in shrink tunnels, which are employed in manufacturing applications for polymeric packaging. In these applications, temperature uniformity is a key factor in determining the quality of the packages resulting from the heat shrinking process, making it a major concern. Consequently, we propose three Model Predictive Control (MPC) strategies for shrink tunnels aimed for temperature uniformity: an MPC for tracking (as a baseline), and two zone-based MPCs that steer the oven temperatures towards either a fixed or an adaptive range rather than a single target point. All control strategies are thoroughly and experimentally validated on a shrink tunnel workbench installed in a manufacturing facility. Specifically, we adopt the Rapid Control Prototyping (RCP) paradigm to speed up controller implementation and performance assessment. Experimental results demonstrate that the zone-based MPC strategies significantly improve the temperature uniformity within the oven cavity compared to the MPC for tracking formulation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106409"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221894","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

An experiment study for unmanned aerial manipulator systems with L1 adaptive augmentation of geometric control 基于L1自适应增强几何控制的无人机机械臂系统实验研究

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-04 DOI: 10.1016/j.conengprac.2025.106418

Kexin Cai, Hai Yu, Zhaopeng Zhang, Xiao Liang, Yongchun Fang, Jianda Han

{"title":"An experiment study for unmanned aerial manipulator systems with L1 adaptive augmentation of geometric control","authors":"Kexin Cai, Hai Yu, Zhaopeng Zhang, Xiao Liang, Yongchun Fang, Jianda Han","doi":"10.1016/j.conengprac.2025.106418","DOIUrl":"10.1016/j.conengprac.2025.106418","url":null,"abstract":"<div><div>Unmanned aerial manipulators, integrating multi-rotor unmanned aerial vehicles (UAVs) and multi-link manipulators, are extending the capabilities of UAVs to interact with the environment and demonstrating vast potential for diverse applications. However, the dynamic coupling between the UAV and the manipulator, along with the unmodeled nonlinear disturbances exerting on the aerial platform, pose challenges for high-performance control of the aerial manipulator. To this end, this paper proposes an <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> adaptive augmentation control method for the aerial manipulator to compensate the disturbances brought by manipulator motion and unmodeled uncertainties. Specifically, a baseline geometric controller is constructed to directly compensate the measurable wrench disturbances exerted on the UAV by the manipulator motion, while an <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> adaptive controller is designed to compensate the residual unknown wrench disturbances. By Lyapunov techniques, it is proven that the error signals of the closed-loop system are uniformly ultimately bounded, which demonstrates the theoretical effectiveness of the proposed method. Finally, the feasibility and robustness of the proposed control method are validated through three groups of hardware experiments.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106418"},"PeriodicalIF":5.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203898","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

Design of an adaptive MPC control system for unmanned ground vehicle based on FP-ADMM and RBFNN 基于FP-ADMM和RBFNN的无人地面车辆自适应MPC控制系统设计

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-06-04 DOI: 10.1016/j.conengprac.2025.106399

Yulong You , Zhong Yang , Hao-ze Zhuo , Nuo Xu , Luwei Liao , Wenbin Jiang

{"title":"Design of an adaptive MPC control system for unmanned ground vehicle based on FP-ADMM and RBFNN","authors":"Yulong You , Zhong Yang , Hao-ze Zhuo , Nuo Xu , Luwei Liao , Wenbin Jiang","doi":"10.1016/j.conengprac.2025.106399","DOIUrl":"10.1016/j.conengprac.2025.106399","url":null,"abstract":"<div><div>To address the challenges of nonlinearity, real-time performance, and parameter uncertainty in distributed six-wheel unmanned ground vehicles (UGV) operating in complex high-speed dynamic environments, this paper proposes a method for achieving precise control in such conditions. First, a dynamic predictive model is established, considering road inclination and time-varying curvature. Next, the grey wolf optimizer (GWO) is employed to adaptively optimize the weight coefficients of the model predictive control (MPC) objective function, creating an adaptive MPC (AMPC) system that enhances overall performance, allowing it to handle various complex terrains. Additionally, the Fast Proximal Alternating Direction Method of Multipliers (FP-ADMM) is utilized to solve the quadratic programming (QP) problem, improving computational efficiency and ensuring real-time performance for UGV at high speeds. To further reduce trajectory tracking errors caused by model uncertainties and external disturbances, a compensation controller based on the radial basis function neural network (RBFNN) is introduced. This controller learns from the system’s control errors and generates real-time compensation signals, combining its output with the AMPC output to perform trajectory tracking tasks for the UGV. The stability of the proposed method is proven based on Lyapunov stability theory. Experimental results from real-world testing show that the proposed method achieved trajectory tracking accuracies of 0.08 m on off-road and 0.06 m on inclined road. Compared to traditional MPC methods, this strategy demonstrates superior tracking performance on complex high-speed terrains, with significant improvements in both real-time performance and tracking accuracy.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106399"},"PeriodicalIF":5.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203850","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