Pub Date : 2024-10-24DOI: 10.1016/j.conengprac.2024.106132
Xi Luo , Yifan Cheng , Jinlong Hong , Shiying Dong , Xiaoxiang Na , Bingzhao Gao , Hong Chen
To address range anxiety in electric vehicles (EVs), this paper presents an eco-driving add-on system implemented on a production EV, with comparative field experiments conducted in real-world traffic conditions. The proposed eco-driving system integrates a predictive cruise control (PCC) strategy to effectively utilize connected information, such as road geometry and preceding vehicle behaviors. For real-time implementation, a fast PCC algorithm coupled with the bisection method, warm-start, and improved iterative transversality condition is introduced. Numerical simulations validate the effectiveness of the proposed scheme, achieving an energy-saving effect of approximately 2%. Subsequently, field experiments were conducted in scenarios including smooth-flowing highways and congested urban expressways using a production EV. Compared to the baseline, which consists of the existing cruise control strategy of EVs and the experienced human drivers, our proposed scheme achieves energy savings of approximately 2.2% on highways and 2.6% on urban expressways.
{"title":"Design and experimental validation of eco-driving system for connected and automated electric vehicles","authors":"Xi Luo , Yifan Cheng , Jinlong Hong , Shiying Dong , Xiaoxiang Na , Bingzhao Gao , Hong Chen","doi":"10.1016/j.conengprac.2024.106132","DOIUrl":"10.1016/j.conengprac.2024.106132","url":null,"abstract":"<div><div>To address range anxiety in electric vehicles (EVs), this paper presents an eco-driving add-on system implemented on a production EV, with comparative field experiments conducted in real-world traffic conditions. The proposed eco-driving system integrates a predictive cruise control (PCC) strategy to effectively utilize connected information, such as road geometry and preceding vehicle behaviors. For real-time implementation, a fast PCC algorithm coupled with the bisection method, warm-start, and improved iterative transversality condition is introduced. Numerical simulations validate the effectiveness of the proposed scheme, achieving an energy-saving effect of approximately 2%. Subsequently, field experiments were conducted in scenarios including smooth-flowing highways and congested urban expressways using a production EV. Compared to the baseline, which consists of the existing cruise control strategy of EVs and the experienced human drivers, our proposed scheme achieves energy savings of approximately 2.2% on highways and 2.6% on urban expressways.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106132"},"PeriodicalIF":5.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532936","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}
Pub Date : 2024-10-23DOI: 10.1016/j.conengprac.2024.106125
Yukun Lu , Ran Zhen , Yegang Liu , Jiaming Zhong , Chen Sun , Yanjun Huang , Amir Khajepour
Prioritizing the improvement of truck driver’s ride comfort is crucial for the health and well-being of drivers, driver retention, safety, overall productivity, regulatory compliance, and customer satisfaction. As a solution, adaptive suspension systems are developed to optimize suspension performances. In this study, a novel integrated Skyhook-LQR algorithm is introduced, which aims to simultaneously improve the sprung mass dynamics in vertical, pitch, and roll directions. Most importantly, it requires affordable computational cost and can be processed on an automotive-grade microcontroller. Besides, it is impossible to find one set of optimum control gains for rapid-changing disturbances since the vehicle may be driven on various road surfaces. A gain-adaptive algorithm is developed to intelligently adjust the LQR’s output penalty matrix Q according to onboard sensor measurements to fill this gap. The performance and effectiveness of the proposed techniques are experimentally examined based on a scaled-down cab-over-engine model and a Stewart Platform. The vehicle responses and disturbance inputs are measured by two 6-axis IMUs and four height sensors, and all the messages are transmitted through the CAN Bus. The unmeasurable states are estimated by a Kalman filter observer. The experimental results validated that the integrated Skyhook-LQR has excellent potential in suspension coordinated control, which significantly optimizes ride quality. Meanwhile, the gain-adaptive algorithm detected vehicle motions and provided efficient gain scheduling decisions, by which the undesired vibrations and shocks were further attenuated to some extent.
{"title":"Practical solution for attenuating industrial heavy vehicle vibration: A new gain-adaptive coordinated suspension control system","authors":"Yukun Lu , Ran Zhen , Yegang Liu , Jiaming Zhong , Chen Sun , Yanjun Huang , Amir Khajepour","doi":"10.1016/j.conengprac.2024.106125","DOIUrl":"10.1016/j.conengprac.2024.106125","url":null,"abstract":"<div><div>Prioritizing the improvement of truck driver’s ride comfort is crucial for the health and well-being of drivers, driver retention, safety, overall productivity, regulatory compliance, and customer satisfaction. As a solution, adaptive suspension systems are developed to optimize suspension performances. In this study, a novel integrated Skyhook-LQR algorithm is introduced, which aims to simultaneously improve the sprung mass dynamics in vertical, pitch, and roll directions. Most importantly, it requires affordable computational cost and can be processed on an automotive-grade microcontroller. Besides, it is impossible to find one set of optimum control gains for rapid-changing disturbances since the vehicle may be driven on various road surfaces. A gain-adaptive algorithm is developed to intelligently adjust the LQR’s output penalty matrix Q according to onboard sensor measurements to fill this gap. The performance and effectiveness of the proposed techniques are experimentally examined based on a scaled-down cab-over-engine model and a Stewart Platform. The vehicle responses and disturbance inputs are measured by two 6-axis IMUs and four height sensors, and all the messages are transmitted through the CAN Bus. The unmeasurable states are estimated by a Kalman filter observer. The experimental results validated that the integrated Skyhook-LQR has excellent potential in suspension coordinated control, which significantly optimizes ride quality. Meanwhile, the gain-adaptive algorithm detected vehicle motions and provided efficient gain scheduling decisions, by which the undesired vibrations and shocks were further attenuated to some extent.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106125"},"PeriodicalIF":5.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532935","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}
Pub Date : 2024-10-22DOI: 10.1016/j.conengprac.2024.106135
Sen Liang , Bing Han , Xinfeng Wang , Xinfang Zhou , Qiang Fang , Yanding Wei
Redundantly actuated distributed parallel manipulators (RADPMs) are widely used for posture alignment and assembly of large-scale components. The structural characteristics of multiple redundant actuation chains not only possess potential advantages, but also bring about challenges for multi-joint coordinated motion. To address the synchronization control issue of the system with dynamic uncertainties, a novel adaptive synchronous tracking control (ASTC) scheme is proposed to realize high-precision trajectory tracking and coordination performance simultaneously. In the proposed ASTC scheme, a synchronization error is first introduced to depict the coordination relationship between adjacent joints and coupled with the tracking error to form a composite error in the joint space. Based on the defined errors, a dual-space adaptation law is proposed through the linear parameterized expression of the system dynamic model to obtain feedforward compensation for dynamics. Additionally, in order to restrain the influence of inevitable external disturbances, a robust control compensation term is introduced to improve the disturbance rejection ability. Moreover, the stability of the entire closed-loop system is proved by utilizing the Lyapunov theory. Finally, simulation and experiments are conducted on an actual 4-PPPS RADPM, and the comparative results demonstrate that the proposed scheme can effectively improve the tracking accuracy and synchronization performance of the system.
{"title":"Adaptive synchronous tracking control for n-PPPS redundantly actuated distributed parallel manipulators with dynamic uncertainties","authors":"Sen Liang , Bing Han , Xinfeng Wang , Xinfang Zhou , Qiang Fang , Yanding Wei","doi":"10.1016/j.conengprac.2024.106135","DOIUrl":"10.1016/j.conengprac.2024.106135","url":null,"abstract":"<div><div>Redundantly actuated distributed parallel manipulators (RADPMs) are widely used for posture alignment and assembly of large-scale components. The structural characteristics of multiple redundant actuation chains not only possess potential advantages, but also bring about challenges for multi-joint coordinated motion. To address the synchronization control issue of the system with dynamic uncertainties, a novel adaptive synchronous tracking control (ASTC) scheme is proposed to realize high-precision trajectory tracking and coordination performance simultaneously. In the proposed ASTC scheme, a synchronization error is first introduced to depict the coordination relationship between adjacent joints and coupled with the tracking error to form a composite error in the joint space. Based on the defined errors, a dual-space adaptation law is proposed through the linear parameterized expression of the system dynamic model to obtain feedforward compensation for dynamics. Additionally, in order to restrain the influence of inevitable external disturbances, a robust control compensation term is introduced to improve the disturbance rejection ability. Moreover, the stability of the entire closed-loop system is proved by utilizing the Lyapunov theory. Finally, simulation and experiments are conducted on an actual 4-PPPS RADPM, and the comparative results demonstrate that the proposed scheme can effectively improve the tracking accuracy and synchronization performance of the system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106135"},"PeriodicalIF":5.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532933","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}
Pub Date : 2024-10-22DOI: 10.1016/j.conengprac.2024.106136
Hafiz Mian Muhammad Adil, Hassan Abbas Khan
On-board chargers within electric vehicles (EVs) must efficiently manage grid-to-vehicle (G2V), vehicle-to-grid (V2G), and vehicle-to-vehicle (V2V) modes for sustainable EV operation. This paper introduces a modified hybrid nonlinear control approach that utilizes the whale optimization algorithm-tuned supertwisted synergetic (WOA-ST-syn) technique for a multipurpose on-board charger (MP-OBC). The whale optimization algorithm(WOA) adjusts the parameters of supertwisted synergetic controller using the integral time absolute error, reducing the need for exhaustive trial-and-error adjustments. The controller employs the state space model of a two-stage on-board electric vehicle charging system, ensuring stability through the Lyapunov stability criterion. Simulations in MATLAB/Simulink evaluate the performance of the proposed controller across various operational modes, testing robustness against varying load currents and mode-switching conditions. Results indicate significant improvements over state-of-the-art nonlinear controllers, with minimal chattering, shortest rise time (0.0007 s for AC-DC, 1.5520 s for DC-DC), fastest settling time (0.0447 s for AC-DC, 2.0550 s for DC-DC), and minimal steady-state error (0.0010% for AC-DC, 0.0004% for DC-DC). Controller Hardware-in-the-Loop (C-HIL) experiments were also performed to confirm the real-time applicability of the controller.
{"title":"WOA-tuned supertwisted synergetic control of multipurpose on-board charger for G2V/V2G/V2V operational modes of electric vehicles","authors":"Hafiz Mian Muhammad Adil, Hassan Abbas Khan","doi":"10.1016/j.conengprac.2024.106136","DOIUrl":"10.1016/j.conengprac.2024.106136","url":null,"abstract":"<div><div>On-board chargers within electric vehicles (EVs) must efficiently manage grid-to-vehicle (G2V), vehicle-to-grid (V2G), and vehicle-to-vehicle (V2V) modes for sustainable EV operation. This paper introduces a modified hybrid nonlinear control approach that utilizes the whale optimization algorithm-tuned supertwisted synergetic (WOA-ST-syn) technique for a multipurpose on-board charger (MP-OBC). The whale optimization algorithm(WOA) adjusts the parameters of supertwisted synergetic controller using the integral time absolute error, reducing the need for exhaustive trial-and-error adjustments. The controller employs the state space model of a two-stage on-board electric vehicle charging system, ensuring stability through the Lyapunov stability criterion. Simulations in MATLAB/Simulink evaluate the performance of the proposed controller across various operational modes, testing robustness against varying load currents and mode-switching conditions. Results indicate significant improvements over state-of-the-art nonlinear controllers, with minimal chattering, shortest rise time (0.0007 s for AC-DC, 1.5520 s for DC-DC), fastest settling time (0.0447 s for AC-DC, 2.0550 s for DC-DC), and minimal steady-state error (0.0010% for AC-DC, 0.0004% for DC-DC). Controller Hardware-in-the-Loop (C-HIL) experiments were also performed to confirm the real-time applicability of the controller.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106136"},"PeriodicalIF":5.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532934","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}
Pub Date : 2024-10-21DOI: 10.1016/j.conengprac.2024.106120
Yongze Jin , Xiaohao Song , Yanxi Yang , Xinhong Hei , Nan Feng , Xubo Yang
To improve the fault diagnosis accuracy of rolling bearings under diverse working conditions, an improved domain adversarial neural network is proposed, the feature extraction module is reconstructed by multi-channel and multi-scale CNN-LSTM-ECA (MMCLE) in the proposed network. The MMCLE module consists of several key components. Firstly, the multi-channel multi-scale Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are established to extract spatial features and temporal dependencies of the input data. Then, the Efficient Channel Attention (ECA) module is introduced to weight the effective feature channels. Finally, the domain adversarial training is employed to extract common features from both the source and target domains. By minimizing the domain offset between these domains, the faults of rolling bearing under diverse working conditions can be accurately diagnosed. The simulation results show that, based on the proposed MMCLE model, the domain offset issue can be effectively addressed, and the fault diagnosis accuracy can be improved for samples in the target domain under diverse working conditions. The accuracy and feasibility of the proposed method can be effectively verified.
{"title":"An improved multi-channel and multi-scale domain adversarial neural network for fault diagnosis of the rolling bearing","authors":"Yongze Jin , Xiaohao Song , Yanxi Yang , Xinhong Hei , Nan Feng , Xubo Yang","doi":"10.1016/j.conengprac.2024.106120","DOIUrl":"10.1016/j.conengprac.2024.106120","url":null,"abstract":"<div><div>To improve the fault diagnosis accuracy of rolling bearings under diverse working conditions, an improved domain adversarial neural network is proposed, the feature extraction module is reconstructed by multi-channel and multi-scale CNN-LSTM-ECA (MMCLE) in the proposed network. The MMCLE module consists of several key components. Firstly, the multi-channel multi-scale Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are established to extract spatial features and temporal dependencies of the input data. Then, the Efficient Channel Attention (ECA) module is introduced to weight the effective feature channels. Finally, the domain adversarial training is employed to extract common features from both the source and target domains. By minimizing the domain offset between these domains, the faults of rolling bearing under diverse working conditions can be accurately diagnosed. The simulation results show that, based on the proposed MMCLE model, the domain offset issue can be effectively addressed, and the fault diagnosis accuracy can be improved for samples in the target domain under diverse working conditions. The accuracy and feasibility of the proposed method can be effectively verified.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106120"},"PeriodicalIF":5.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532941","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}
Pub Date : 2024-10-19DOI: 10.1016/j.conengprac.2024.106128
Georg Janisch , Andreas Kugi , Wolfgang Kemmetmüller
Induction machines are widely used in electric vehicles due to their high reliability and low costs. Controlling these machines to meet the high-performance demands presents a significant challenge since they are often operated at high speed and within operating ranges where magnetic saturation plays a significant role. Furthermore, specific motor parameters are not accurately known or vary during operation, e.g., due to temperature changes. Therefore, there is still a demand for control strategies to meet these demands systematically. This paper proposes a novel control strategy combining a model predictive control (MPC) concept with a fast feedback controller and a nonlinear observer. The proposed MPC strategy is based on a magnetic nonlinear model and allows for a long prediction horizon. It features high torque dynamics while ensuring energy optimality in the steady state. The results also show excellent performance for high rotational speeds and the operation at the system limits, outperforming state-of-the-art control concepts.
{"title":"A high-performance model predictive torque control concept for induction machines for electric vehicle applications","authors":"Georg Janisch , Andreas Kugi , Wolfgang Kemmetmüller","doi":"10.1016/j.conengprac.2024.106128","DOIUrl":"10.1016/j.conengprac.2024.106128","url":null,"abstract":"<div><div>Induction machines are widely used in electric vehicles due to their high reliability and low costs. Controlling these machines to meet the high-performance demands presents a significant challenge since they are often operated at high speed and within operating ranges where magnetic saturation plays a significant role. Furthermore, specific motor parameters are not accurately known or vary during operation, e.g., due to temperature changes. Therefore, there is still a demand for control strategies to meet these demands systematically. This paper proposes a novel control strategy combining a model predictive control (MPC) concept with a fast feedback controller and a nonlinear observer. The proposed MPC strategy is based on a magnetic nonlinear model and allows for a long prediction horizon. It features high torque dynamics while ensuring energy optimality in the steady state. The results also show excellent performance for high rotational speeds and the operation at the system limits, outperforming state-of-the-art control concepts.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"153 ","pages":"Article 106128"},"PeriodicalIF":5.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534103","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}
Representation learning approaches have achieved great success in fault diagnosis of large-scale mechanical data, among which the popular auto-encoder method has developed a series of effective variants. In the existing variants, the encoder network is re-employed to encode feature representations of the data, while the decoder network is directly discarded after training, leading to a regrettable waste of computational resources. Instead of proposing advanced variants of the auto-encoder, this paper explicitly penalizes the decoder network with group lasso, thereby transforming waste into treasure. Specifically, the group lasso constrains the column vectors of the decoder network’s weight matrix at the group level, making them reusable for feature selection. Moreover, a smooth function is utilized to approximate the group lasso to prevent numerical oscillations when computing the gradients. The simulated data and experimental gear data are sequentially used to verify the effectiveness of the smooth group lasso through investigations on two representative auto-encoder variants. The results show that the decoder network penalized by smooth group lasso can be re-utilized to guide selection of a subset of key features for training a classifier, exhibiting an extraordinary feature selection capability.
{"title":"A reusable decoder network penalized by smooth group lasso and its applications to large-scale fault diagnosis of machinery","authors":"Zhiqiang Zhang, Hongji He, Shuiqing Xu, Lisheng Yin, Xueping Dong","doi":"10.1016/j.conengprac.2024.106127","DOIUrl":"10.1016/j.conengprac.2024.106127","url":null,"abstract":"<div><div>Representation learning approaches have achieved great success in fault diagnosis of large-scale mechanical data, among which the popular auto-encoder method has developed a series of effective variants. In the existing variants, the encoder network is re-employed to encode feature representations of the data, while the decoder network is directly discarded after training, leading to a regrettable waste of computational resources. Instead of proposing advanced variants of the auto-encoder, this paper explicitly penalizes the decoder network with group lasso, thereby transforming waste into treasure. Specifically, the group lasso constrains the column vectors of the decoder network’s weight matrix at the group level, making them reusable for feature selection. Moreover, a smooth function is utilized to approximate the group lasso to prevent numerical oscillations when computing the gradients. The simulated data and experimental gear data are sequentially used to verify the effectiveness of the smooth group lasso through investigations on two representative auto-encoder variants. The results show that the decoder network penalized by smooth group lasso can be re-utilized to guide selection of a subset of key features for training a classifier, exhibiting an extraordinary feature selection capability.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"153 ","pages":"Article 106127"},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446180","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}
Pub Date : 2024-10-16DOI: 10.1016/j.conengprac.2024.106105
Liang Han , Yue Wang , Ziwei Yan , Xiaoduo Li , Zhang Ren
This study investigates time-varying formation control with communication constraint for general discrete-time multi-agent systems (MASs), which aims to control a swarm of agents to maintain a desired formation while avoiding obstacles in the scenario with spatial constraint. The event-triggered mechanism is introduced to effectively reduce the system communication frequency and an artificial potential field function is incorporated into the proposed controller to achieve obstacle avoidance in formation. The obtained results are applied to solve obstacle avoidance problems for multiple unmanned aerial vehicles (UAVs) in formation flight. Physical simulations are completed with four UAV models on a 3-D visualization simulation platform integrated by Robot Operating System (ROS) and Gazebo. Then, practical experiments are carried out with four quadrotors in a complex experimental scenario combined with the motion capture system. The physical simulation and practical experiments are implemented to verify the effectiveness of the theoretical results.
{"title":"Event-triggered formation control with obstacle avoidance for multi-agent systems applied to multi-UAV formation flying","authors":"Liang Han , Yue Wang , Ziwei Yan , Xiaoduo Li , Zhang Ren","doi":"10.1016/j.conengprac.2024.106105","DOIUrl":"10.1016/j.conengprac.2024.106105","url":null,"abstract":"<div><div>This study investigates time-varying formation control with communication constraint for general discrete-time multi-agent systems (MASs), which aims to control a swarm of agents to maintain a desired formation while avoiding obstacles in the scenario with spatial constraint. The event-triggered mechanism is introduced to effectively reduce the system communication frequency and an artificial potential field function is incorporated into the proposed controller to achieve obstacle avoidance in formation. The obtained results are applied to solve obstacle avoidance problems for multiple unmanned aerial vehicles (UAVs) in formation flight. Physical simulations are completed with four UAV models on a 3-D visualization simulation platform integrated by Robot Operating System (ROS) and Gazebo. Then, practical experiments are carried out with four quadrotors in a complex experimental scenario combined with the motion capture system. The physical simulation and practical experiments are implemented to verify the effectiveness of the theoretical results.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"153 ","pages":"Article 106105"},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442895","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}
Pub Date : 2024-10-16DOI: 10.1016/j.conengprac.2024.106126
Wenqiang Zhao , Hongqian Wei , Qiang Ai , Nan Zheng , Chen Lin , Youtong Zhang
The path following function is a critical component of functional safety for autonomous vehicles, and following precision has garnered increased attention in practical applications. However, control performance can be compromised due to uncertainties in vehicle parameters and discrepancies between the control model and the actual vehicle to be controlled. To address this, a real-time model predictive control for path following of autonomous vehicles is proposed, incorporating an estimation of model mismatch. An adaptive extended Kalman filter is developed to estimate the potential model mismatch terms, and state deviations are compensated accordingly. Subsequently, a parameter-varying model predictive controller is formulated to achieve unbiased path-following control while maintaining robustness to parameter variations. Simulation results demonstrate a significant improvement in lateral following accuracy, with enhancements of 53.85%, 47.83%, and 42.86% compared to the nonlinear model predictive control, robust model predictive control, and learning-based control, respectively. The hardware-in-the-loop and real-road experiments further validate the excellent real-time executability, with a maximum time cost of 12.4 ms, accounting for 62% of the sampling period.
{"title":"Real-time model predictive control of path-following for autonomous vehicles towards model mismatch and uncertainty","authors":"Wenqiang Zhao , Hongqian Wei , Qiang Ai , Nan Zheng , Chen Lin , Youtong Zhang","doi":"10.1016/j.conengprac.2024.106126","DOIUrl":"10.1016/j.conengprac.2024.106126","url":null,"abstract":"<div><div>The path following function is a critical component of functional safety for autonomous vehicles, and following precision has garnered increased attention in practical applications. However, control performance can be compromised due to uncertainties in vehicle parameters and discrepancies between the control model and the actual vehicle to be controlled. To address this, a real-time model predictive control for path following of autonomous vehicles is proposed, incorporating an estimation of model mismatch. An adaptive extended Kalman filter is developed to estimate the potential model mismatch terms, and state deviations are compensated accordingly. Subsequently, a parameter-varying model predictive controller is formulated to achieve unbiased path-following control while maintaining robustness to parameter variations. Simulation results demonstrate a significant improvement in lateral following accuracy, with enhancements of 53.85%, 47.83%, and 42.86% compared to the nonlinear model predictive control, robust model predictive control, and learning-based control, respectively. The hardware-in-the-loop and real-road experiments further validate the excellent real-time executability, with a maximum time cost of 12.4 ms, accounting for 62% of the sampling period.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"153 ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438250","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}
Pub Date : 2024-10-15DOI: 10.1016/j.conengprac.2024.106129
Zhenhong Liao , Ce Xu , Wen Chen , Feng Wang , Jinhua She
Grinding in mineral processing is used to control the ore at the technically feasible and economically optimum particle size to achieve mineral liberation for separation. A circulating-load ratio (CLR) during a semi-autogenous grinding (SAG) milling process is critical for controlling particle size and energy consumption. This paper presents a CLR-prediction model based on clustering SAG milling operating conditions. First, operating parameters affecting the CLR are identified by comprehensively analyzing the complex mechanism and characteristics of a typical industrial SAG milling process. Next, a method is developed to cluster operating conditions of the SAG milling process based on the power consumption and CLR of the process. The method reveals the actual physical significance of each operating condition. Then, support vector regression (SVR) is used to model the CLR in each operating condition. After that, a distance-based model integration strategy is designed to determine the weights of each SVR model to predict the CLR. Finally, integrating the SVR submodels yields a CLR prediction model. Actual run data demonstrated the accuracy and effectiveness of the model in predicting CLR. This method has significant practical value for improving SAG milling efficiency via its utilization in control system design.
矿物加工中的磨矿是将矿石控制在技术上可行、经济上最佳的粒度，以实现矿物分离。半自磨机（SAG）研磨过程中的循环负荷率（CLR）对于控制粒度和能耗至关重要。本文提出了一种基于 SAG 研磨操作条件聚类的 CLR 预测模型。首先，通过全面分析典型工业 SAG 研磨过程的复杂机理和特征，确定了影响 CLR 的操作参数。然后，根据 SAG 磨工艺的功耗和 CLR，开发了一种对 SAG 磨工艺操作条件进行聚类的方法。该方法揭示了每个运行条件的实际物理意义。然后，使用支持向量回归（SVR）对每种操作条件下的 CLR 进行建模。然后，设计一种基于距离的模型集成策略，以确定每个 SVR 模型的权重，从而预测 CLR。最后，对 SVR 子模型进行整合，得出 CLR 预测模型。实际运行数据证明了该模型预测 CLR 的准确性和有效性。这种方法在控制系统设计中的应用对于提高 SAG 磨矿效率具有重要的实用价值。
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