Pub Date : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435898
S. Hao, Hong Xue, Jinpeng Cui
This paper investigates the problem of an event-triggered based adaptive fixed-time tracking control for error-constraint nonlinear system. The fuzzy logical systems are applied to handle the unknown nonlinear functions in the control design process. Then, by incorporating transformed error into the barrier Lyapunov function, all the tracking errors can be constrained in predefined dynamic performance. Then, event-triggered mechanism is combined with backstepping technique for the purpose of reducing the communication burden. An event-triggered based adaptive controller is constructed in a fixed time. Moreover, in accordance with fixed-time theory, the proposed control scheme can ensure that all signals of the closed-loop systems are bounded. Finally, a simulation is proved that the effectiveness of control scheme.
{"title":"Fuzzy Adaptive Fixed-Time Control for Error-Constraint Nonlinear System using Event-Triggered Communication*","authors":"S. Hao, Hong Xue, Jinpeng Cui","doi":"10.1109/ICACI52617.2021.9435898","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435898","url":null,"abstract":"This paper investigates the problem of an event-triggered based adaptive fixed-time tracking control for error-constraint nonlinear system. The fuzzy logical systems are applied to handle the unknown nonlinear functions in the control design process. Then, by incorporating transformed error into the barrier Lyapunov function, all the tracking errors can be constrained in predefined dynamic performance. Then, event-triggered mechanism is combined with backstepping technique for the purpose of reducing the communication burden. An event-triggered based adaptive controller is constructed in a fixed time. Moreover, in accordance with fixed-time theory, the proposed control scheme can ensure that all signals of the closed-loop systems are bounded. Finally, a simulation is proved that the effectiveness of control scheme.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997122","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435906
Jinrui Shen, Jiahao Zheng, Xiaoping Wang
With the development of deep learning, emotion recognition tasks are more inclined to use multimodal data and adequate supervised information to improve accuracy. In this work, MMTrans-MT (Multimodal Transformer-Multitask), the framework for multimodal emotion recognition using multitask learning is proposed. It has three modules: modalities representation module, multimodal fusion module, and multitask output module. Three modalities, i.e, words, audio and video, are comprehensively utilized to carry out emotion recognition by a simple but efficient fusion model based on Transformer. As for multitask learning, the two tasks are defined as categorical emotion classification and dimensional emotion regression. Considering a potential mapping relationship between two kinds of emotion model, multitask learning is adopted to make the two tasks promote each other and improve recognition accuracy. We conduct experiments on CMU-MOSEI and IEMOCAP datasets. Comprehensive experiments show that the accuracy of recognition using multimodal information is higher than that using unimodal information. Adopting multitask learning promotes the performance of emotion recognition.
{"title":"MMTrans-MT: A Framework for Multimodal Emotion Recognition Using Multitask Learning","authors":"Jinrui Shen, Jiahao Zheng, Xiaoping Wang","doi":"10.1109/ICACI52617.2021.9435906","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435906","url":null,"abstract":"With the development of deep learning, emotion recognition tasks are more inclined to use multimodal data and adequate supervised information to improve accuracy. In this work, MMTrans-MT (Multimodal Transformer-Multitask), the framework for multimodal emotion recognition using multitask learning is proposed. It has three modules: modalities representation module, multimodal fusion module, and multitask output module. Three modalities, i.e, words, audio and video, are comprehensively utilized to carry out emotion recognition by a simple but efficient fusion model based on Transformer. As for multitask learning, the two tasks are defined as categorical emotion classification and dimensional emotion regression. Considering a potential mapping relationship between two kinds of emotion model, multitask learning is adopted to make the two tasks promote each other and improve recognition accuracy. We conduct experiments on CMU-MOSEI and IEMOCAP datasets. Comprehensive experiments show that the accuracy of recognition using multimodal information is higher than that using unimodal information. Adopting multitask learning promotes the performance of emotion recognition.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192809","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435885
Xiaolin Song, Zilin Gao, Xitao Zou, Liyuan Qi, Yuan Luo
To solve the problem of existing chaotic systems with unknown nonlinearities, enormous parameters and external disturbances, in this paper, a synchronization controller with parameter adaptive laws is proposed based on nested back-propagation neural networks and the adaptive method, where the nested back-propagation neural networks are used to approximate the unknown nonlinearities based on same experiences and the unknown parameters are estimated by the adaptive method. Then the asymptotical synchronization of the drive-response chaotic systems is synthesized via state feedback controllers and updated adaptive laws. Specifically, the nested back-propagation neural networks are developed by grouping and layering the hidden neurons using the principle of partition of unity and the state domain for modularizing the concealed layer. Finally, a numerical example is given to illustrate the effectiveness of this method.
{"title":"Synchronization control for completely unknown chaotic systems via nested back-propagation neural networks","authors":"Xiaolin Song, Zilin Gao, Xitao Zou, Liyuan Qi, Yuan Luo","doi":"10.1109/ICACI52617.2021.9435885","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435885","url":null,"abstract":"To solve the problem of existing chaotic systems with unknown nonlinearities, enormous parameters and external disturbances, in this paper, a synchronization controller with parameter adaptive laws is proposed based on nested back-propagation neural networks and the adaptive method, where the nested back-propagation neural networks are used to approximate the unknown nonlinearities based on same experiences and the unknown parameters are estimated by the adaptive method. Then the asymptotical synchronization of the drive-response chaotic systems is synthesized via state feedback controllers and updated adaptive laws. Specifically, the nested back-propagation neural networks are developed by grouping and layering the hidden neurons using the principle of partition of unity and the state domain for modularizing the concealed layer. Finally, a numerical example is given to illustrate the effectiveness of this method.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128353256","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435897
Liyuan Qi, J. Xiong, Xiaolin Song, Xinjian Ming, Kunlin Xie, Rui Wang
Research the transmission efficiency optimization problem of magnetic coupling resonant wireless charging system. According to the transmission principle of resonant wireless charging, the system multi-parameter matching problem is optimized to make the wireless charging system work in the best state. Construct an equivalent model of series-series compensation topology, and obtain three key parameters that affect the transmission efficiency of the system: frequency, distance and load resistance. Using the particle swarm optimization algorithm with compression factor, the best of the three key parameters is obtained. The matching value, compared with the system that only optimizes two key parameters, proves that this method can find the best matching value between the parameters faster and more accurately within the set feasible range, and the system transmission efficiency reaches Optimal. The effectiveness of the algorithm and the correctness of the analysis process are verified by a Matlab simulation example.
{"title":"Research and optimization of Transmission Characteristics of Magnetic Coupled Resonant Wireless Charging System","authors":"Liyuan Qi, J. Xiong, Xiaolin Song, Xinjian Ming, Kunlin Xie, Rui Wang","doi":"10.1109/ICACI52617.2021.9435897","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435897","url":null,"abstract":"Research the transmission efficiency optimization problem of magnetic coupling resonant wireless charging system. According to the transmission principle of resonant wireless charging, the system multi-parameter matching problem is optimized to make the wireless charging system work in the best state. Construct an equivalent model of series-series compensation topology, and obtain three key parameters that affect the transmission efficiency of the system: frequency, distance and load resistance. Using the particle swarm optimization algorithm with compression factor, the best of the three key parameters is obtained. The matching value, compared with the system that only optimizes two key parameters, proves that this method can find the best matching value between the parameters faster and more accurately within the set feasible range, and the system transmission efficiency reaches Optimal. The effectiveness of the algorithm and the correctness of the analysis process are verified by a Matlab simulation example.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134087327","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435892
Meimei Zang, Ying Zhu, Rushi Lan, Yining Liu, Xiaonan Luo
Emerging blockchain technology supports mutually distrustful parties to communicate safely without a trusted central entity. The security and privacy protection are prerequisites for communication in vehicular ad-hoc network (VANET). In this paper, we propose an efficient blockchain-based authentication scheme for vehicular secure communication (BAVC). BAVC introduces smart contract to restrict access to the network, which deny the access of malicious vehicles. To achieve better performance and reduce the computational cost of message processing in VANET, our proposed BAVC scheme uses Elliptic Curve Cryptography (ECC) to achieve anonymous communication of vehicles instead of using bilinear paring. We can evaluate the trustworthiness of a message according to confidence level provided by nearby vehicles which received this message. Moreover, the analysis results show that our scheme has better performance, it satisfies the security and privacy requirement with low computation cost.
{"title":"BAVC: Efficient Blockchain-Based Authentication Scheme for Vehicular Secure Communication","authors":"Meimei Zang, Ying Zhu, Rushi Lan, Yining Liu, Xiaonan Luo","doi":"10.1109/ICACI52617.2021.9435892","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435892","url":null,"abstract":"Emerging blockchain technology supports mutually distrustful parties to communicate safely without a trusted central entity. The security and privacy protection are prerequisites for communication in vehicular ad-hoc network (VANET). In this paper, we propose an efficient blockchain-based authentication scheme for vehicular secure communication (BAVC). BAVC introduces smart contract to restrict access to the network, which deny the access of malicious vehicles. To achieve better performance and reduce the computational cost of message processing in VANET, our proposed BAVC scheme uses Elliptic Curve Cryptography (ECC) to achieve anonymous communication of vehicles instead of using bilinear paring. We can evaluate the trustworthiness of a message according to confidence level provided by nearby vehicles which received this message. Moreover, the analysis results show that our scheme has better performance, it satisfies the security and privacy requirement with low computation cost.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131088235","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435877
Yingxuan Zhu, Chuang Gao, Yonghui Yang, Xin Liu
In this paper, the problem of fixed-time adaptive tracking control for strict-feedback nonlinear systems is studied. By introducing a new tunnel constraint variable, a new adaptive and practical fixed-time controller is constructed based on fuzzy control, fixed-time Lyapunov stability theory and backstepping algorithm. Fuzzy logic system is introduced to approximate the unknown term of the system. Theoretical analysis shows that under the control strategy, and the tracking error converges to a small neighborhood of the origin within fixed-time intervals, where the convergence time is independent of the initial state of the system. At the same time, all the signals of the closed-loop system are bounded. Simulation results show that the proposed controller is effective.
{"title":"Fixed-Time Adaptive Fuzzy Funnel Control for Strict-Feedback Nonlinear Systems","authors":"Yingxuan Zhu, Chuang Gao, Yonghui Yang, Xin Liu","doi":"10.1109/ICACI52617.2021.9435877","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435877","url":null,"abstract":"In this paper, the problem of fixed-time adaptive tracking control for strict-feedback nonlinear systems is studied. By introducing a new tunnel constraint variable, a new adaptive and practical fixed-time controller is constructed based on fuzzy control, fixed-time Lyapunov stability theory and backstepping algorithm. Fuzzy logic system is introduced to approximate the unknown term of the system. Theoretical analysis shows that under the control strategy, and the tracking error converges to a small neighborhood of the origin within fixed-time intervals, where the convergence time is independent of the initial state of the system. At the same time, all the signals of the closed-loop system are bounded. Simulation results show that the proposed controller is effective.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132082929","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435909
Hongyan Chen, Hai-Lin Liu, Fangqing Gu, Lei Chen
Decomposition-based evolutionary multiobjective algorithms achieve good performance for solving the problems with regular Pareto fronts. Nevertheless, the shape of the Pareto front greatly influences the performance of the algorithms. Thus, we propose a new adaptive parallel region decomposition strategy. Different from the traditional decomposition-based methods, the proposed algorithm decomposes a multiobjective optimization problem into a number of subproblems by different ideal points, but not by different weight vectors. We compare the proposed algorithm with four state-of-the-art algorithms on seven test problems with irregular Pareto fronts. Experimental results show that the proposed algorithm has superior robustness on the optimization problems with irregular Pareto fronts.
{"title":"A Mutli-objective Evolutionary Algorithm with Adaptive Parallel Region Decomposition","authors":"Hongyan Chen, Hai-Lin Liu, Fangqing Gu, Lei Chen","doi":"10.1109/ICACI52617.2021.9435909","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435909","url":null,"abstract":"Decomposition-based evolutionary multiobjective algorithms achieve good performance for solving the problems with regular Pareto fronts. Nevertheless, the shape of the Pareto front greatly influences the performance of the algorithms. Thus, we propose a new adaptive parallel region decomposition strategy. Different from the traditional decomposition-based methods, the proposed algorithm decomposes a multiobjective optimization problem into a number of subproblems by different ideal points, but not by different weight vectors. We compare the proposed algorithm with four state-of-the-art algorithms on seven test problems with irregular Pareto fronts. Experimental results show that the proposed algorithm has superior robustness on the optimization problems with irregular Pareto fronts.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121709856","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435902
Liyan Xu, Lili Bai, Haijie Bao, Jing-qing Jiang
Artificial bee colony algorithm (ABC) is a swarm intelligence algorithm, which simulates the intelligent behavior of bee colony. ABC algorithm has achieved good performance in solving multivariable optimization problems. But ABC convergent slowly and is easy to fall into local extremum. These lead to the low accuracy of the optimal solution. In order to increase the accuracy of the parameters identification of solar cell model, an improved artificial bee colony algorithm (IABC) is proposed. In the stage of employed bee and onlooker bee, the bees have a 50% probability to update the position guided by global best honey source after neighborhood search. Meanwhile a full dimensional neighborhood search is employed to improve the search efficiency. The experimental results show that the convergence speed and the accuracy of the parameters are improved. It provides a new method for parameter identification of solar cell model.
{"title":"Parameter Identification of Solar Cell Model Based on Improved Artificial Bee Colony Algorithm","authors":"Liyan Xu, Lili Bai, Haijie Bao, Jing-qing Jiang","doi":"10.1109/ICACI52617.2021.9435902","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435902","url":null,"abstract":"Artificial bee colony algorithm (ABC) is a swarm intelligence algorithm, which simulates the intelligent behavior of bee colony. ABC algorithm has achieved good performance in solving multivariable optimization problems. But ABC convergent slowly and is easy to fall into local extremum. These lead to the low accuracy of the optimal solution. In order to increase the accuracy of the parameters identification of solar cell model, an improved artificial bee colony algorithm (IABC) is proposed. In the stage of employed bee and onlooker bee, the bees have a 50% probability to update the position guided by global best honey source after neighborhood search. Meanwhile a full dimensional neighborhood search is employed to improve the search efficiency. The experimental results show that the convergence speed and the accuracy of the parameters are improved. It provides a new method for parameter identification of solar cell model.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125153257","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435859
Yi Chen, Shuo Chen, Manlin Xuan, Qiuzhen Lin, Wenhong Wei
Intrusion detection system (IDS) plays a significant role to secure our privacy data, which can avoid various threats from Internet. There are more and more research studies to use convolutional neural networks (CNNs) as IDSs. However, it is still very challenging on how to develop a reliable and effective IDS by using CNNs. Thus, this paper suggests an evolutionary convolutional neural network (ECNN) as an IDS. It is a first try to use multiobjective immune algorithm to simultaneously optimize the accuracy and weight parameters of CNNs. Such that, our method can obtain various CNN models with different detection accuracies and complexities. The users can select their preferences based on their security requirements and hardware conditions. A number of experiments have been conducted on the NSL-KDD and UNSW-NB datasets to study the capability and performance of the proposed method. When compared to some state-of-the-art algorithms, the experimental results show that our method can obtain a higher detection accuracy.
{"title":"Evolutionary Convolutional Neural Network: An Application to Intrusion Detection","authors":"Yi Chen, Shuo Chen, Manlin Xuan, Qiuzhen Lin, Wenhong Wei","doi":"10.1109/ICACI52617.2021.9435859","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435859","url":null,"abstract":"Intrusion detection system (IDS) plays a significant role to secure our privacy data, which can avoid various threats from Internet. There are more and more research studies to use convolutional neural networks (CNNs) as IDSs. However, it is still very challenging on how to develop a reliable and effective IDS by using CNNs. Thus, this paper suggests an evolutionary convolutional neural network (ECNN) as an IDS. It is a first try to use multiobjective immune algorithm to simultaneously optimize the accuracy and weight parameters of CNNs. Such that, our method can obtain various CNN models with different detection accuracies and complexities. The users can select their preferences based on their security requirements and hardware conditions. A number of experiments have been conducted on the NSL-KDD and UNSW-NB datasets to study the capability and performance of the proposed method. When compared to some state-of-the-art algorithms, the experimental results show that our method can obtain a higher detection accuracy.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127210086","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 : 2021-05-14DOI: 10.1109/ICACI52617.2021.9435880
Haiyang Jiang, Ming Chen, Huanqing Wang
A fast finite-time fuzzy funnel control method is proposed for strict-feedback nonlinear systems. Based on the funnel boundary constraint functions, finite-time Lyapunov stability theory and backstepping technology, the sufficient conditions and design steps are given, which guarantee that the all the signals of the closed-loop system are semi-global practically fast finite-time stable. The proposed method can ensure that the tracking error can converge to the prescribed region of the funnel constraint functions, at the same time, the prescribed performance are achieved, such as convergence speed and overshoot. At last, a simulation example illustrates the effectiveness of the proposed approach.
{"title":"Fast Finite-Time Adaptive Control for Strict Feedback Nonlinear Systems","authors":"Haiyang Jiang, Ming Chen, Huanqing Wang","doi":"10.1109/ICACI52617.2021.9435880","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435880","url":null,"abstract":"A fast finite-time fuzzy funnel control method is proposed for strict-feedback nonlinear systems. Based on the funnel boundary constraint functions, finite-time Lyapunov stability theory and backstepping technology, the sufficient conditions and design steps are given, which guarantee that the all the signals of the closed-loop system are semi-global practically fast finite-time stable. The proposed method can ensure that the tracking error can converge to the prescribed region of the funnel constraint functions, at the same time, the prescribed performance are achieved, such as convergence speed and overshoot. At last, a simulation example illustrates the effectiveness of the proposed approach.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126905020","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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