Pub Date : 2024-10-30DOI: 10.1016/j.conengprac.2024.106138
Jiahui Hu , Yonghua Lu , Jing Li , Haibo Yang , Jingjing Liu
Gaze is a non-verbal behavior that is an important communication cue and a direct reflection of subjective intent. However, few research works have intervened gaze into the aerial teleoperation circuits of unmanned aerial vehicles (UAVs). This paper proposed an aerial teleoperation framework based on gaze-guidance, mainly built on the novel theory of non-invasive gaze tracking and gaze-drive. We demonstrate how a monocular gaze tracker can acquire human gaze signals and convert them into lupin and efficient control intentions, thus allowing humans to assign tasks to an automated quadrotor without body movements. Extensive and complex simulations and real-world experiments are conducted to verify the superior performance of the proposed method in obstacle traversal.
{"title":"Aerial teleoperation for quadrotors based on gaze-guidance","authors":"Jiahui Hu , Yonghua Lu , Jing Li , Haibo Yang , Jingjing Liu","doi":"10.1016/j.conengprac.2024.106138","DOIUrl":"10.1016/j.conengprac.2024.106138","url":null,"abstract":"<div><div>Gaze is a non-verbal behavior that is an important communication cue and a direct reflection of subjective intent. However, few research works have intervened gaze into the aerial teleoperation circuits of unmanned aerial vehicles (UAVs). This paper proposed an aerial teleoperation framework based on gaze-guidance, mainly built on the novel theory of non-invasive gaze tracking and gaze-drive. We demonstrate how a monocular gaze tracker can acquire human gaze signals and convert them into lupin and efficient control intentions, thus allowing humans to assign tasks to an automated quadrotor without body movements. Extensive and complex simulations and real-world experiments are conducted to verify the superior performance of the proposed method in obstacle traversal.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106138"},"PeriodicalIF":5.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553444","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 : 2024-10-29DOI: 10.1016/j.conengprac.2024.106140
Liujiayi Zhao, Pengyu Song, Chunhui Zhao
Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.
{"title":"Causal similarity learning with multi-level predictive relation aggregation for grouped root cause diagnosis of industrial faults","authors":"Liujiayi Zhao, Pengyu Song, Chunhui Zhao","doi":"10.1016/j.conengprac.2024.106140","DOIUrl":"10.1016/j.conengprac.2024.106140","url":null,"abstract":"<div><div>Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106140"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539824","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 : 2024-10-29DOI: 10.1016/j.conengprac.2024.106134
Junlin Zhou , Christopher T. Freeman , William Holderbaum
Model-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems.
{"title":"Multiple-model iterative learning control with application to stroke rehabilitation","authors":"Junlin Zhou , Christopher T. Freeman , William Holderbaum","doi":"10.1016/j.conengprac.2024.106134","DOIUrl":"10.1016/j.conengprac.2024.106134","url":null,"abstract":"<div><div>Model-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106134"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539825","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}
Pub Date : 2024-10-25DOI: 10.1016/j.conengprac.2024.106131
XinYu Zhao, LiMei Wang
The performance of permanent magnet linear synchronous motor in tracking is influenced by payload uncertainty and unknown disturbances. Traditional constant-gain super-twisting control typically use a high control gain exceeding the total disturbances to maintain the stability of the system. However, these controllers may lead to control input oversaturation when disturbances decrease and the control gain is not appropriately chosen. To address this issue, this paper proposes a new Fractional Order Barrier Function Adaptive Super-Twisting (FOBFAST) control strategy. The advantages of FOBFAST include: (1) mitigation of system chattering through the design of the super-twisting algorithm and the fractional-order integral terminal sliding mode manifold; (2) achieving convergence of system error to a predetermined zero-neighborhood without requiring information about the disturbance upper bound; (3) dynamic adjustment of control gain to a smaller value as tracking error converges to the origin. Furthermore, an improved barrier function is proposed to address the issue of large control amplitudes, limiting the maximum allowable control gain and ensuring system stability. Experimental results demonstrate that the proposed control strategy not only enhances position tracking performance but also exhibits superior robustness.
{"title":"Bounded control of PMLSM servo system based on fractional order barrier function adaptive super-twisting approach","authors":"XinYu Zhao, LiMei Wang","doi":"10.1016/j.conengprac.2024.106131","DOIUrl":"10.1016/j.conengprac.2024.106131","url":null,"abstract":"<div><div>The performance of permanent magnet linear synchronous motor in tracking is influenced by payload uncertainty and unknown disturbances. Traditional constant-gain super-twisting control typically use a high control gain exceeding the total disturbances to maintain the stability of the system. However, these controllers may lead to control input oversaturation when disturbances decrease and the control gain is not appropriately chosen. To address this issue, this paper proposes a new Fractional Order Barrier Function Adaptive Super-Twisting (FOBFAST) control strategy. The advantages of FOBFAST include: (1) mitigation of system chattering through the design of the super-twisting algorithm and the fractional-order integral terminal sliding mode manifold; (2) achieving convergence of system error to a predetermined zero-neighborhood without requiring information about the disturbance upper bound; (3) dynamic adjustment of control gain to a smaller value as tracking error converges to the origin. Furthermore, an improved barrier function is proposed to address the issue of large control amplitudes, limiting the maximum allowable control gain and ensuring system stability. Experimental results demonstrate that the proposed control strategy not only enhances position tracking performance but also exhibits superior robustness.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106131"},"PeriodicalIF":5.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532939","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 : 2024-10-25DOI: 10.1016/j.conengprac.2024.106139
Liang Zhou, Zhong-Qi Li, Hui Yang, Chang Tan
A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [0.176 km/h, 0.152 km/h], while the control force and acceleration are within [55.7 kN, 44.8 kN] and [0.564 m/s, 0.496 m/s], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.
{"title":"Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme","authors":"Liang Zhou, Zhong-Qi Li, Hui Yang, Chang Tan","doi":"10.1016/j.conengprac.2024.106139","DOIUrl":"10.1016/j.conengprac.2024.106139","url":null,"abstract":"<div><div>A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [<span><math><mo>−</mo></math></span>0.176 km/h, 0.152 km/h], while the control force and acceleration are within [<span><math><mo>−</mo></math></span>55.7 kN, 44.8 kN] and [<span><math><mo>−</mo></math></span>0.564 m/s<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, 0.496 m/s<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106139"},"PeriodicalIF":5.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532940","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 : 2024-10-24DOI: 10.1016/j.conengprac.2024.106123
Songhua Liu , Xun Lang , Jiande Wu , Yufeng Zhang , Cong Lei , Hongye Su
Monitoring oscillatory behavior in industrial control systems is essential to ensure process safety and enhance productivity, but unfortunately, current decomposition-based monitoring methods struggle to extract and accurately detect multiple oscillations. This struggle is primarily due to the level of noise susceptibility in current monitoring methods and the intermittent nature of multiple oscillations in industrial control systems. As a potential solution, variational mode decomposition (VMD) shows promising advantages in processing non-stationary industrial signals with a significant amount of interference. Nevertheless, improperly configuring the number of modes in the VMD can lead to mode mixing and degrade the oscillation extraction accuracy. To overcome this challenge, we propose a corrective VMD approach that automatically adjusts the mode number to create a robust and automated framework for monitoring multiple oscillations. Our framework excels in detecting oscillatory behavior and quantifying the number of oscillations, even in the presence of noisy, intermittent, and irregular disturbances. To validate its effectiveness and practicality, we applied the framework to both a benchmark industrial dataset and a self-constructed industrial dataset, comparing its performance against state-of-the-art oscillation detection methods. The framework demonstrated superior accuracy, achieving 93.90% in detecting oscillations and 85.37% in quantifying the number of oscillations within the benchmark dataset, with similarly excellent results observed in the self-constructed industrial dataset.
{"title":"Corrective variational mode decomposition to detect multiple oscillations in process control systems","authors":"Songhua Liu , Xun Lang , Jiande Wu , Yufeng Zhang , Cong Lei , Hongye Su","doi":"10.1016/j.conengprac.2024.106123","DOIUrl":"10.1016/j.conengprac.2024.106123","url":null,"abstract":"<div><div>Monitoring oscillatory behavior in industrial control systems is essential to ensure process safety and enhance productivity, but unfortunately, current decomposition-based monitoring methods struggle to extract and accurately detect multiple oscillations. This struggle is primarily due to the level of noise susceptibility in current monitoring methods and the intermittent nature of multiple oscillations in industrial control systems. As a potential solution, variational mode decomposition (VMD) shows promising advantages in processing non-stationary industrial signals with a significant amount of interference. Nevertheless, improperly configuring the number of modes in the VMD can lead to mode mixing and degrade the oscillation extraction accuracy. To overcome this challenge, we propose a corrective VMD approach that automatically adjusts the mode number to create a robust and automated framework for monitoring multiple oscillations. Our framework excels in detecting oscillatory behavior and quantifying the number of oscillations, even in the presence of noisy, intermittent, and irregular disturbances. To validate its effectiveness and practicality, we applied the framework to both a benchmark industrial dataset and a self-constructed industrial dataset, comparing its performance against state-of-the-art oscillation detection methods. The framework demonstrated superior accuracy, achieving 93.90% in detecting oscillations and 85.37% in quantifying the number of oscillations within the benchmark dataset, with similarly excellent results observed in the self-constructed industrial dataset.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106123"},"PeriodicalIF":5.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532937","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 : 2024-10-24DOI: 10.1016/j.conengprac.2024.106130
Aliakbar Davoodi, Ahmad W. Al-Dabbagh
In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.
{"title":"Hierarchical grouping and visualization of correlated alarms using time-augmented word embedding","authors":"Aliakbar Davoodi, Ahmad W. Al-Dabbagh","doi":"10.1016/j.conengprac.2024.106130","DOIUrl":"10.1016/j.conengprac.2024.106130","url":null,"abstract":"<div><div>In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106130"},"PeriodicalIF":5.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532938","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}
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.
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