Pub Date : 2024-08-08DOI: 10.1177/01423312241262540
Jiazhou Lu, Guosheng Zhang, Liaoxuan Dai, H. Min, Shi Shang
This paper investigates the problem of parameter estimation for DC-DC buck converter without current sensors. For the circuit, all the parameters of capacitance, inductance, resistance, and input voltage are unknown. A novel Volterra operator-based fixed-time adaptive algorithm is proposed by using only the output voltage and control input signals. By selecting proper kernel function for the Volterra integral operator, the influence of the system initial values can be eliminated, and the calculation of the derivative of the system output can also be avoided. Strict analysis shows that the proposed estimation algorithm can ensure the estimation errors converge to zero in a fixed time independent of the initial error values. Finally, simulation results with different initial values verify the advantages of the proposed algorithm.
{"title":"Volterra operator-based fixed-time adaptive parameter estimation for DC-DC buck converters without current sensors","authors":"Jiazhou Lu, Guosheng Zhang, Liaoxuan Dai, H. Min, Shi Shang","doi":"10.1177/01423312241262540","DOIUrl":"https://doi.org/10.1177/01423312241262540","url":null,"abstract":"This paper investigates the problem of parameter estimation for DC-DC buck converter without current sensors. For the circuit, all the parameters of capacitance, inductance, resistance, and input voltage are unknown. A novel Volterra operator-based fixed-time adaptive algorithm is proposed by using only the output voltage and control input signals. By selecting proper kernel function for the Volterra integral operator, the influence of the system initial values can be eliminated, and the calculation of the derivative of the system output can also be avoided. Strict analysis shows that the proposed estimation algorithm can ensure the estimation errors converge to zero in a fixed time independent of the initial error values. Finally, simulation results with different initial values verify the advantages of the proposed algorithm.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1177/01423312241266687
Zhenshuai Wan, Chong Liu, Yu Fu
The electro-hydraulic servo system (EHSS) performs model uncertainty and state constraints such that the exact model-based controller is difficult to be designed. In this work, a nonlinear disturbance observer (NDO)-based adaptive neural control (ANC) is proposed for the EHSS, in which a nonlinear transformation function is constructed to make the state constraints problem transformed into state unconstraint problem. The NDO is introduced to improve the disturbance rejection ability. The ANC is utilized to approximate unmodeled dynamics. The second-order filters are integrated with backstepping control to solve the explosion of complexity. The proposed NDO-based ANC scheme confines all states within the predefined bounds, improves the robustness of closed-loop system, and alleviates the computation burden. Moreover, the stability analysis for the closed-loop system is given within the Lyapunov framework. Simulations and experiments show that the proposed control scheme can achieve excellent control performance and robustness with regard to full-state constraints and model uncertainty.
{"title":"Nonlinear disturbance observer–based adaptive neural control for electro-hydraulic servo system with model uncertainty and full-state constraints","authors":"Zhenshuai Wan, Chong Liu, Yu Fu","doi":"10.1177/01423312241266687","DOIUrl":"https://doi.org/10.1177/01423312241266687","url":null,"abstract":"The electro-hydraulic servo system (EHSS) performs model uncertainty and state constraints such that the exact model-based controller is difficult to be designed. In this work, a nonlinear disturbance observer (NDO)-based adaptive neural control (ANC) is proposed for the EHSS, in which a nonlinear transformation function is constructed to make the state constraints problem transformed into state unconstraint problem. The NDO is introduced to improve the disturbance rejection ability. The ANC is utilized to approximate unmodeled dynamics. The second-order filters are integrated with backstepping control to solve the explosion of complexity. The proposed NDO-based ANC scheme confines all states within the predefined bounds, improves the robustness of closed-loop system, and alleviates the computation burden. Moreover, the stability analysis for the closed-loop system is given within the Lyapunov framework. Simulations and experiments show that the proposed control scheme can achieve excellent control performance and robustness with regard to full-state constraints and model uncertainty.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1177/01423312241266663
Liang Huang, Qiping Chen, Zhiqiang Jiang, Chengping Zhong, Daoliang You
To coordinate the accuracy and driving stability of intelligent automobile in the path tracking process and improve the adaptive capability of the control algorithm to different working conditions, an intelligent automobile path tracking control method based on T-S fuzzy is proposed. First, the lateral deviation and heading angle deviation during tracking are considered, and the path tracking error equation is established using a 2 degree-of-freedom single-track dynamic model. Second, an adaptive preview algorithm based on vehicle speed, reference path curvature and heading angle deviation is designed, and feedforward control is designed based on the results of the algorithm. Then, the T-S fuzzy control method with fast decision-making capability is utilized to realize the adaptive adjustment of the weight coefficients of the linear quadratic regulation (LQR) controller to adapt to the variable weight path tracking control under different working conditions. Finally, the designed control method is tested on a double-lane road condition using the Carsim-Simulink co-simulation platform. The results show that the designed controller has high tracking accuracy, and can maintain good accuracy and driving stability under different working conditions.
{"title":"Intelligent automobile path tracking control based on T-S fuzzy","authors":"Liang Huang, Qiping Chen, Zhiqiang Jiang, Chengping Zhong, Daoliang You","doi":"10.1177/01423312241266663","DOIUrl":"https://doi.org/10.1177/01423312241266663","url":null,"abstract":"To coordinate the accuracy and driving stability of intelligent automobile in the path tracking process and improve the adaptive capability of the control algorithm to different working conditions, an intelligent automobile path tracking control method based on T-S fuzzy is proposed. First, the lateral deviation and heading angle deviation during tracking are considered, and the path tracking error equation is established using a 2 degree-of-freedom single-track dynamic model. Second, an adaptive preview algorithm based on vehicle speed, reference path curvature and heading angle deviation is designed, and feedforward control is designed based on the results of the algorithm. Then, the T-S fuzzy control method with fast decision-making capability is utilized to realize the adaptive adjustment of the weight coefficients of the linear quadratic regulation (LQR) controller to adapt to the variable weight path tracking control under different working conditions. Finally, the designed control method is tested on a double-lane road condition using the Carsim-Simulink co-simulation platform. The results show that the designed controller has high tracking accuracy, and can maintain good accuracy and driving stability under different working conditions.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1177/01423312241267043
Baoyue Li, Yonghua Yu, Weicheng Wang, Ning Zhang, Meiqiang Xie
The vibration signal of a gearbox contains a large amount of information and can be used for fault diagnosis of gearboxes. In order to efficiently extract fault features from the vibration signals and improve the reliability of fault diagnosis, a gearbox fault diagnosis method based on improved multi-scale fluctuation dispersion entropy (IMFDE) is proposed. The method takes full advantage of sliding coarse-grained processing to alleviate the shortcomings of traditional multi-scale entropy methods and improve the stability of multi-scale fluctuating dispersion entropy (MFDE). The multi-cluster feature selection (MCFS) method is then combined with the selection of low-dimensional sensitive features from the original multi-scale features, and the sensitive feature matrix is input to a random forest (RF) classifier to mine the complex mapping relationship between the input features and the fault type to achieve fault diagnosis of gearboxes. Finally, experimental data of two gearboxes are used to verify the reliability of the proposed method. The results show that the proposed method can accurately determine different fault types of gearboxes and has significant advantages in terms of reliability and stability of fault identification compared with other existing methods.
{"title":"Gearbox fault diagnosis based on improved multi-scale fluctuation dispersion entropy and multi-cluster feature selection","authors":"Baoyue Li, Yonghua Yu, Weicheng Wang, Ning Zhang, Meiqiang Xie","doi":"10.1177/01423312241267043","DOIUrl":"https://doi.org/10.1177/01423312241267043","url":null,"abstract":"The vibration signal of a gearbox contains a large amount of information and can be used for fault diagnosis of gearboxes. In order to efficiently extract fault features from the vibration signals and improve the reliability of fault diagnosis, a gearbox fault diagnosis method based on improved multi-scale fluctuation dispersion entropy (IMFDE) is proposed. The method takes full advantage of sliding coarse-grained processing to alleviate the shortcomings of traditional multi-scale entropy methods and improve the stability of multi-scale fluctuating dispersion entropy (MFDE). The multi-cluster feature selection (MCFS) method is then combined with the selection of low-dimensional sensitive features from the original multi-scale features, and the sensitive feature matrix is input to a random forest (RF) classifier to mine the complex mapping relationship between the input features and the fault type to achieve fault diagnosis of gearboxes. Finally, experimental data of two gearboxes are used to verify the reliability of the proposed method. The results show that the proposed method can accurately determine different fault types of gearboxes and has significant advantages in terms of reliability and stability of fault identification compared with other existing methods.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1177/01423312241254590
F. Sabahi
This paper introduces a novel algorithm that seamlessly integrates type-2 fuzzy systems with a sliding mode controller, aiming to create an optimal bounded control policy for tracking nonlinear problems that are plagued with uncertain or incomplete system dynamics and control input constraints. Proving its efficacy in navigating uncertainties, the proposed approach maintains effectiveness even when such encounters are sporadic or infrequent. The algorithm tactically employs three type-2 fuzzy systems. Among these, the actor and critic fuzzy systems are specifically tasked to resolve the optimal control tracking problem, while the third fuzzy system is designated to approximate the system’s unknown dynamics. The sliding mode controller’s role is instrumental in this setup. It dynamically adjusts to ensure the system’s convergence, enabling precise tracking of the desired trajectory, undeterred by the prevalent uncertainties. We validate the stability of the entire amalgamation, consisting of the actor, critic, identifier and controller. The robustness and efficiency of this innovative method are confirmed through rigorous simulation testing on a nonlinear system. Our results substantiate that the proposed solution excels in optimal tracking control, particularly in situations where system dynamics are uncertain or incomplete and where control input constraints are a critical factor.
{"title":"Optimal bounded policy for nonlinear tracking control of unknown constrained-input systems","authors":"F. Sabahi","doi":"10.1177/01423312241254590","DOIUrl":"https://doi.org/10.1177/01423312241254590","url":null,"abstract":"This paper introduces a novel algorithm that seamlessly integrates type-2 fuzzy systems with a sliding mode controller, aiming to create an optimal bounded control policy for tracking nonlinear problems that are plagued with uncertain or incomplete system dynamics and control input constraints. Proving its efficacy in navigating uncertainties, the proposed approach maintains effectiveness even when such encounters are sporadic or infrequent. The algorithm tactically employs three type-2 fuzzy systems. Among these, the actor and critic fuzzy systems are specifically tasked to resolve the optimal control tracking problem, while the third fuzzy system is designated to approximate the system’s unknown dynamics. The sliding mode controller’s role is instrumental in this setup. It dynamically adjusts to ensure the system’s convergence, enabling precise tracking of the desired trajectory, undeterred by the prevalent uncertainties. We validate the stability of the entire amalgamation, consisting of the actor, critic, identifier and controller. The robustness and efficiency of this innovative method are confirmed through rigorous simulation testing on a nonlinear system. Our results substantiate that the proposed solution excels in optimal tracking control, particularly in situations where system dynamics are uncertain or incomplete and where control input constraints are a critical factor.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1177/01423312241253639
Mohammad Ali Labbaf Khaniki, Amirhossein Samii, Mahsan Tavakoli‐Kakhki
This article introduces a method for the adaptive control of a six-dimensional (6D) hyperchaotic system using a multi-input multi-output (MIMO) approach, leveraging the deep deterministic policy gradient (DDPG) algorithm. The states and tracking errors of the hyperchaotic system are amalgamated to form an input image signal. This signal is then processed by a deep convolutional neural network (CNN) to extract profound features. Subsequently, the DDPG determines the coefficients of the proportional–integral–derivative (PID) controller based on the features discerned from the CNN. The proposed approach exhibits robustness to uncertainties and varying initial conditions, attributed to the DDPG’s ability to learn from the input image signal and adaptively adjust control policies and PID coefficients. The results demonstrate that the proposed adaptive PID controller, integrated with DDPG and CNN, surpasses conventional controllers in terms of synchronization accuracy and response speed. The paper presents the following: a 6D hyperchaotic system’s dynamic model, a CNN-based DDPG’s structure, and how it performs and compares to traditional methods. Then, it summarizes the main findings.
{"title":"Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system","authors":"Mohammad Ali Labbaf Khaniki, Amirhossein Samii, Mahsan Tavakoli‐Kakhki","doi":"10.1177/01423312241253639","DOIUrl":"https://doi.org/10.1177/01423312241253639","url":null,"abstract":"This article introduces a method for the adaptive control of a six-dimensional (6D) hyperchaotic system using a multi-input multi-output (MIMO) approach, leveraging the deep deterministic policy gradient (DDPG) algorithm. The states and tracking errors of the hyperchaotic system are amalgamated to form an input image signal. This signal is then processed by a deep convolutional neural network (CNN) to extract profound features. Subsequently, the DDPG determines the coefficients of the proportional–integral–derivative (PID) controller based on the features discerned from the CNN. The proposed approach exhibits robustness to uncertainties and varying initial conditions, attributed to the DDPG’s ability to learn from the input image signal and adaptively adjust control policies and PID coefficients. The results demonstrate that the proposed adaptive PID controller, integrated with DDPG and CNN, surpasses conventional controllers in terms of synchronization accuracy and response speed. The paper presents the following: a 6D hyperchaotic system’s dynamic model, a CNN-based DDPG’s structure, and how it performs and compares to traditional methods. Then, it summarizes the main findings.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1177/01423312241234000
Hongfei Zhang, D. She, Hu Wang, Yaoming Li, Jin Chen
Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem that cross-domain fault diagnosis cannot be performed due to the distribution differences between different working conditions, a transfer diagnosis method based on multi-representation adversarial neural network is proposed. First, the multi-representation neural network is applied to extract multiscale features. Second, the domain adversarial network is utilized to set the gradient inversion layer and extract the domain invariant features in the multiscale features. In terms of the loss function, the Wasserstein function and cross-entropy loss function are utilized to measure the distance between the source domain and the target domain. The experimental case of rolling bearing supports the effectiveness and superiority of the proposed method.
{"title":"A multi-representation transfer adversarial network for intelligent fault diagnosis of rotating machinery","authors":"Hongfei Zhang, D. She, Hu Wang, Yaoming Li, Jin Chen","doi":"10.1177/01423312241234000","DOIUrl":"https://doi.org/10.1177/01423312241234000","url":null,"abstract":"Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem that cross-domain fault diagnosis cannot be performed due to the distribution differences between different working conditions, a transfer diagnosis method based on multi-representation adversarial neural network is proposed. First, the multi-representation neural network is applied to extract multiscale features. Second, the domain adversarial network is utilized to set the gradient inversion layer and extract the domain invariant features in the multiscale features. In terms of the loss function, the Wasserstein function and cross-entropy loss function are utilized to measure the distance between the source domain and the target domain. The experimental case of rolling bearing supports the effectiveness and superiority of the proposed method.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"6 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1177/01423312241234409
Yubin Pan, Hua Wang, Jie Chen, R. Hong
Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.
{"title":"A new fault component selection strategy based on statistical detection for slewing bearing weak signal de-noising","authors":"Yubin Pan, Hua Wang, Jie Chen, R. Hong","doi":"10.1177/01423312241234409","DOIUrl":"https://doi.org/10.1177/01423312241234409","url":null,"abstract":"Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"9 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-17DOI: 10.1177/01423312241233822
Shaoqi Xu, Mingjie Cai, Baofang Wang
In this paper, an adaptive practical prescribed-time consensus (PPTC) for multiple mechanical systems with full-state constraints is discussed. We first propose a new nonlinear mapping (NM). By transforming the full state–constrained system with the NM, we can obtain an unconstrained system. Then combined with neural networks, graph theory, and practical prescribed-time control theory, a distributed adaptive PPTC protocol is proposed for the unconstrained system, which can ensure that position errors and speed errors reach a certain region within a prescribed-time and full-state constraints are satisfied. Finally, an example is given to demonstrate that this method can be implemented.
{"title":"Research on adaptive practical prescribed-time consensus of multiple mechanical systems with full-state constraints","authors":"Shaoqi Xu, Mingjie Cai, Baofang Wang","doi":"10.1177/01423312241233822","DOIUrl":"https://doi.org/10.1177/01423312241233822","url":null,"abstract":"In this paper, an adaptive practical prescribed-time consensus (PPTC) for multiple mechanical systems with full-state constraints is discussed. We first propose a new nonlinear mapping (NM). By transforming the full state–constrained system with the NM, we can obtain an unconstrained system. Then combined with neural networks, graph theory, and practical prescribed-time control theory, a distributed adaptive PPTC protocol is proposed for the unconstrained system, which can ensure that position errors and speed errors reach a certain region within a prescribed-time and full-state constraints are satisfied. Finally, an example is given to demonstrate that this method can be implemented.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-17DOI: 10.1177/01423312241235105
Liangpei Huang, Hua Huang
The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.
{"title":"Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems","authors":"Liangpei Huang, Hua Huang","doi":"10.1177/01423312241235105","DOIUrl":"https://doi.org/10.1177/01423312241235105","url":null,"abstract":"The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"189 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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