Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976586
Chenhan Zhang, Zhenlei Wang, Liang Zhao
Machine learning technologies have received great attention in the field of optimization under uncertainty, which learn effective information unsupervised from uncertain data. This work proposes a novel data-driven uncertainty set that uses two machine learning methods and a typical uncertainty set: partial least squares is adopted to decompose the dataset into two subspaces by extracting the relation between data, and then the uncertainties within the divided subspaces are further described by support vector clustering-based and polyhedral uncertainty sets. The proposed data-driven uncertainty set-induced robust optimization framework not only preserve the tractability similar to the classical ones, but also tradeoffs between robustness and optimality well. The final real-world example of cracking furnace scheduling demonstrates the applicability and validity of the proposed framework.
{"title":"Unsupervised Learning-based Robust Optimization for Ethylene Cracking Furnace Scheduling","authors":"Chenhan Zhang, Zhenlei Wang, Liang Zhao","doi":"10.1109/IAI55780.2022.9976586","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976586","url":null,"abstract":"Machine learning technologies have received great attention in the field of optimization under uncertainty, which learn effective information unsupervised from uncertain data. This work proposes a novel data-driven uncertainty set that uses two machine learning methods and a typical uncertainty set: partial least squares is adopted to decompose the dataset into two subspaces by extracting the relation between data, and then the uncertainties within the divided subspaces are further described by support vector clustering-based and polyhedral uncertainty sets. The proposed data-driven uncertainty set-induced robust optimization framework not only preserve the tractability similar to the classical ones, but also tradeoffs between robustness and optimality well. The final real-world example of cracking furnace scheduling demonstrates the applicability and validity of the proposed framework.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130047659","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976642
J. Leventides, E. Melas, C. Poulios
We apply the Koopman operator theory and Extended Dynamic Mode Decomposition in the inverted pendulum model. The inverted pendulum is one of the fundamental problems in the theory of systems and control, due to its theoretical value, along with its practical applications. The inverted pendulum is a nonlinear system, its equation of motion is a nonlinear differential equation. This makes the computation of an appropriate control law a difficult task. Koopman operator captures the dynamics of a nonlinear system, however it is infinite dimensional. In this study, we approximate the inverted pendulum with a data-driven finite dimensional linear system which is defined on some augmented state space. We approximate so the trajectories of the system and obtain an alternative description of the system based on Koopman operator theory, Extended Dynamic Mode Decomposition, and Machine Learning.
{"title":"Koopman operators and Extended dynamic mode decomposition for the inverted pendulum","authors":"J. Leventides, E. Melas, C. Poulios","doi":"10.1109/IAI55780.2022.9976642","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976642","url":null,"abstract":"We apply the Koopman operator theory and Extended Dynamic Mode Decomposition in the inverted pendulum model. The inverted pendulum is one of the fundamental problems in the theory of systems and control, due to its theoretical value, along with its practical applications. The inverted pendulum is a nonlinear system, its equation of motion is a nonlinear differential equation. This makes the computation of an appropriate control law a difficult task. Koopman operator captures the dynamics of a nonlinear system, however it is infinite dimensional. In this study, we approximate the inverted pendulum with a data-driven finite dimensional linear system which is defined on some augmented state space. We approximate so the trajectories of the system and obtain an alternative description of the system based on Koopman operator theory, Extended Dynamic Mode Decomposition, and Machine Learning.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130916986","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976816
Shuyu Wang, Shoujin Huang, N. Lu
As is well known, the cutting tool wear has a negative impact on machining precision. A precise tool wear monitoring method plays an important role in facilitating in-time cutting tool replacement, decreasing the risk of tool failure, and enhancing the machining precision. This work proposes an end-to-end approach for online tool wear monitoring based on deep learning. Firstly, a temporal convolutional network (TCN) is designed to extract features in time series from raw sensor data acquired during the cutting process. Secondly, a fully connected network is built to decode the extracted features into the exact value of tool wear. Finally, the approach is validated on PHM 2010 challenge dataset. Experimental studies show that the flank wear of the cutting tool can be monitored not only precisely, but also fast, indicating that the proposed approach has great prospects for application.
{"title":"Real-time Tool Wear Monitoring Based on A Temporal Convolutional Network","authors":"Shuyu Wang, Shoujin Huang, N. Lu","doi":"10.1109/IAI55780.2022.9976816","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976816","url":null,"abstract":"As is well known, the cutting tool wear has a negative impact on machining precision. A precise tool wear monitoring method plays an important role in facilitating in-time cutting tool replacement, decreasing the risk of tool failure, and enhancing the machining precision. This work proposes an end-to-end approach for online tool wear monitoring based on deep learning. Firstly, a temporal convolutional network (TCN) is designed to extract features in time series from raw sensor data acquired during the cutting process. Secondly, a fully connected network is built to decode the extracted features into the exact value of tool wear. Finally, the approach is validated on PHM 2010 challenge dataset. Experimental studies show that the flank wear of the cutting tool can be monitored not only precisely, but also fast, indicating that the proposed approach has great prospects for application.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132224816","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976658
Zhiling Ren, Yun Dong, Dong Lin
This paper presents a hierarchical model predictive control method for the management of agricultural greenhouse systems. The proposed approach consists of an optimization layer and a control layer. At the optimization layer, an optimization strategy is proposed to minimize the total costs of greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO2) supply and carbon emissions while maintaining greenhouse environmental factors, including temperature, humidity and CO2 concentration, within specified ranges. The proposed method is compared with a baseline method that minimizes greenhouse operating costs. At the control layer, a model predictive controller (MPC) is designed to track the reference trajectory obtained from the optimization layer. Simulation results show that the proposed method can reduce the total cost by R827 and the carbon emissions by 1.16 tons compared with the baseline method. Moreover, the designed MPC controller is verified to have good control performance under different levels of system disturbances. The proposed method is helpful to realize cleaner production and sustainable development of agricultural greenhouses.
{"title":"Hierarchical model predictive control for managing agricultural greenhouse systems","authors":"Zhiling Ren, Yun Dong, Dong Lin","doi":"10.1109/IAI55780.2022.9976658","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976658","url":null,"abstract":"This paper presents a hierarchical model predictive control method for the management of agricultural greenhouse systems. The proposed approach consists of an optimization layer and a control layer. At the optimization layer, an optimization strategy is proposed to minimize the total costs of greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO2) supply and carbon emissions while maintaining greenhouse environmental factors, including temperature, humidity and CO2 concentration, within specified ranges. The proposed method is compared with a baseline method that minimizes greenhouse operating costs. At the control layer, a model predictive controller (MPC) is designed to track the reference trajectory obtained from the optimization layer. Simulation results show that the proposed method can reduce the total cost by R827 and the carbon emissions by 1.16 tons compared with the baseline method. Moreover, the designed MPC controller is verified to have good control performance under different levels of system disturbances. The proposed method is helpful to realize cleaner production and sustainable development of agricultural greenhouses.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501226","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976820
Hongcheng Wang, Yuchen Jiang, Hao Wang, Hao Luo
Flexible production lines are the mainstream choice in the current manufacturing industry. In a flexible line, a single machine can execute a variety of processing tasks. Machine breakdowns are common unexpected disturbances in manufacturing. Machine breakdowns in a flexible production line that result in the unexpected shutdown of the flexible production line's operation unit will have a significant influence on the overall manufacturing process. A two-stage dynamic scheduling strategy is used in this paper to solve the problem that the repair time of the machine cannot be accurately estimated when the machine fails in the production process of the flexible production lines. When any machine fails or is repaired, this strategy is used to set up the best production schedule for flexible production lines. The two-stage scheduling strategy can avoid estimating the repair time of the machine so that dynamic scheduling can be carried out accurately according to the actual situation. The imperialist competitive algorithm(ICA) is originally suitable for continuous optimization problems, while this problem falls within the category of discrete optimization. In this paper, the idea of hybridization of genetic algorithm is used to improve the ICA, so that it is suitable for discrete optimization problems to solve dynamic scheduling. Experiments demonstrate the effectiveness of the two-stage dynamic scheduling strategy and the improved imperialist competitive algorithm.
{"title":"An online optimization scheme of the dynamic flexible job shop scheduling problem for intelligent manufacturing","authors":"Hongcheng Wang, Yuchen Jiang, Hao Wang, Hao Luo","doi":"10.1109/IAI55780.2022.9976820","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976820","url":null,"abstract":"Flexible production lines are the mainstream choice in the current manufacturing industry. In a flexible line, a single machine can execute a variety of processing tasks. Machine breakdowns are common unexpected disturbances in manufacturing. Machine breakdowns in a flexible production line that result in the unexpected shutdown of the flexible production line's operation unit will have a significant influence on the overall manufacturing process. A two-stage dynamic scheduling strategy is used in this paper to solve the problem that the repair time of the machine cannot be accurately estimated when the machine fails in the production process of the flexible production lines. When any machine fails or is repaired, this strategy is used to set up the best production schedule for flexible production lines. The two-stage scheduling strategy can avoid estimating the repair time of the machine so that dynamic scheduling can be carried out accurately according to the actual situation. The imperialist competitive algorithm(ICA) is originally suitable for continuous optimization problems, while this problem falls within the category of discrete optimization. In this paper, the idea of hybridization of genetic algorithm is used to improve the ICA, so that it is suitable for discrete optimization problems to solve dynamic scheduling. Experiments demonstrate the effectiveness of the two-stage dynamic scheduling strategy and the improved imperialist competitive algorithm.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121963254","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976620
Huijuan Huang, Lin Pan
This study addresses the problem of simultaneous pickup and delivery of urban and rural networks. Big data is used to predict the congestion index of vehicles in different time periods under different road types to calculate the actual travel speed, and a time-varying road network model is established with the goal of minimizing the total cost. An improved adaptive genetic algorithm is designed and its effectiveness is verified by an example. Finally, the impact of traffic congestion level and carbon tax cost on the distribution scheme is discussed through sensitivity analysis. The results show that the improved adaptive genetic algorithm has better solution performance. Traffic congestion will affect speed, which in turn will affect the cost of delivery. The increase in carbon tax will not only affect the cost of carbon emissions but also have a negative impact on other costs and reduce corporate profits.
{"title":"Simultaneous Pickup and Delivery Vehicle Route Optimization with Time Windows under Time-varying Road Networks","authors":"Huijuan Huang, Lin Pan","doi":"10.1109/IAI55780.2022.9976620","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976620","url":null,"abstract":"This study addresses the problem of simultaneous pickup and delivery of urban and rural networks. Big data is used to predict the congestion index of vehicles in different time periods under different road types to calculate the actual travel speed, and a time-varying road network model is established with the goal of minimizing the total cost. An improved adaptive genetic algorithm is designed and its effectiveness is verified by an example. Finally, the impact of traffic congestion level and carbon tax cost on the distribution scheme is discussed through sensitivity analysis. The results show that the improved adaptive genetic algorithm has better solution performance. Traffic congestion will affect speed, which in turn will affect the cost of delivery. The increase in carbon tax will not only affect the cost of carbon emissions but also have a negative impact on other costs and reduce corporate profits.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121044152","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}
In this paper, an adaptive neural network finite-time fault-tolerant control scheme is proposed for a fixed-wing UAV under state constraints and actuator fault. To build a state-constraint model, the inertial position dynamics are first formulated to compact model. A Butterworth low-pass filter is introduced to solve the algebraic loop involved by control input. Moreover, the lumped unknown nonlinearities inherent in the UAV system, actuator fault, external disturbances, and approximation errors are respectively identified by utilizing neural network and nonlinear disturbance observer. Furthermore, a barrier Lyapunov function is used to constrain the states of the UAV and verify the finite-time stability of the designed control scheme. Eventually, the effectiveness is demonstrated by simulation results.
{"title":"Adaptive Neural Network Finite-Time Fault-Tolerant Control of Fixed-Wing UAV Under State Constraints and Actuator Fault","authors":"Yiwei Xu, Zhong Yang, Ruifeng Zhou, Ziquan Yu, Fuyang Chen, You Zhang","doi":"10.1109/IAI55780.2022.9976698","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976698","url":null,"abstract":"In this paper, an adaptive neural network finite-time fault-tolerant control scheme is proposed for a fixed-wing UAV under state constraints and actuator fault. To build a state-constraint model, the inertial position dynamics are first formulated to compact model. A Butterworth low-pass filter is introduced to solve the algebraic loop involved by control input. Moreover, the lumped unknown nonlinearities inherent in the UAV system, actuator fault, external disturbances, and approximation errors are respectively identified by utilizing neural network and nonlinear disturbance observer. Furthermore, a barrier Lyapunov function is used to constrain the states of the UAV and verify the finite-time stability of the designed control scheme. Eventually, the effectiveness is demonstrated by simulation results.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115784445","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}
High-density polyethylene (HDPE) are colorless and transparent particles, which are critical raw materials of many plastic products. HDPE particles with defects would affect the quality of final products and the economic benefits of enterprises. At present, there is lack of methods to identify defective HDPE particles quickly and efficiently. To address above problems, intelligent vision detection is introduced into the quality analysis of HDPE, and a set of quality analysis and detection schemes of HDPE are designed in this paper. Firstly, for obtaining better imaging quality, analysis and selection of the background color of the detection scenario is conducted. Secondly, particle conveying and photographing sensing strategy is designed for upgrading production line. Thirdly, intelligent detection of defective particles based on YOLO is merged into the analysis system. According to the experiment results, the blue color is selected as the optimal background. The recognition accuracy reaches 99.39% with the blue background color samples, thus defect particles of HDPE could be detected and identified effectively.
{"title":"Quality Analysis of high-density polyethylene based on Intelligent Vision Detection","authors":"Jianchun Jiang, Xu-hui Zhan, Yangyang Liu, Chong Tang, Jianan Wang, Jianwei Liu","doi":"10.1109/IAI55780.2022.9976537","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976537","url":null,"abstract":"High-density polyethylene (HDPE) are colorless and transparent particles, which are critical raw materials of many plastic products. HDPE particles with defects would affect the quality of final products and the economic benefits of enterprises. At present, there is lack of methods to identify defective HDPE particles quickly and efficiently. To address above problems, intelligent vision detection is introduced into the quality analysis of HDPE, and a set of quality analysis and detection schemes of HDPE are designed in this paper. Firstly, for obtaining better imaging quality, analysis and selection of the background color of the detection scenario is conducted. Secondly, particle conveying and photographing sensing strategy is designed for upgrading production line. Thirdly, intelligent detection of defective particles based on YOLO is merged into the analysis system. According to the experiment results, the blue color is selected as the optimal background. The recognition accuracy reaches 99.39% with the blue background color samples, thus defect particles of HDPE could be detected and identified effectively.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845029","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}
Instead of the well-known three laws of robotics that seem difficult to be applied to solving the trolley problems in the context of frame problems, this paper proposes algebraic modeling of the trolley problems on a Boolean multivalued logic so that we can analyze psychologically any knowledge simply by quasi-optimizing the truth values of logic formulae for inference in a class of Boolean algebra. Some simulation results suggest a possibility that, by introducing an atom that takes the truth values of directly killing person(s), we can control the utilitarian over-rationalization of sacrificing person(s) on AI machines.
{"title":"Algebraic Modeling of Trolley Problems on a Boolean Multivalued Logic","authors":"Jiaqi Peng, Rintaro Mizutani, Kujira Suzuki, Akira Midorikawa, Hisashi Suzuki","doi":"10.1109/IAI55780.2022.9976864","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976864","url":null,"abstract":"Instead of the well-known three laws of robotics that seem difficult to be applied to solving the trolley problems in the context of frame problems, this paper proposes algebraic modeling of the trolley problems on a Boolean multivalued logic so that we can analyze psychologically any knowledge simply by quasi-optimizing the truth values of logic formulae for inference in a class of Boolean algebra. Some simulation results suggest a possibility that, by introducing an atom that takes the truth values of directly killing person(s), we can control the utilitarian over-rationalization of sacrificing person(s) on AI machines.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496571","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976657
Fei Yan, G. Gu
This paper addresses the issue of full actuation control for a class of nonlinear systems, commonly seen in engineering applications. The class of nonlinear systems involves unknown and uncertain parameters, rendering the design of feed-back controllers very challenging, especially for the full actuation control. To tackle the design issue in the presence of parameter uncertainties, the asymptotic full actuation control is proposed, aimed at achieving the full actuation control asymptotically. We first develop an adaptive control algorithm, reminiscent to the well-known backstepping control, to achieve the asymptotic global stabilization for the class of nonlinear systems, in the absence of convergence for the parameter estimates to their respective true values. The well-known recursive least-squares algorithm is then employed to estimate system parameters via sampling the output and other system signals. The asymptotic convergence of the estimates to the true system parameters and hence the asymptotic full actuation are then shown to hold for the class of nonlinear systems under some mild assumptions.
{"title":"Asymptotic Full Actuation Control for A Class of Nonlinear Systems","authors":"Fei Yan, G. Gu","doi":"10.1109/IAI55780.2022.9976657","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976657","url":null,"abstract":"This paper addresses the issue of full actuation control for a class of nonlinear systems, commonly seen in engineering applications. The class of nonlinear systems involves unknown and uncertain parameters, rendering the design of feed-back controllers very challenging, especially for the full actuation control. To tackle the design issue in the presence of parameter uncertainties, the asymptotic full actuation control is proposed, aimed at achieving the full actuation control asymptotically. We first develop an adaptive control algorithm, reminiscent to the well-known backstepping control, to achieve the asymptotic global stabilization for the class of nonlinear systems, in the absence of convergence for the parameter estimates to their respective true values. The well-known recursive least-squares algorithm is then employed to estimate system parameters via sampling the output and other system signals. The asymptotic convergence of the estimates to the true system parameters and hence the asymptotic full actuation are then shown to hold for the class of nonlinear systems under some mild assumptions.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129534417","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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