Pub Date : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642185
Weichen Luo, Mingming Liang, Derong Liu, Bo Zhao
In this paper, an event-triggered control (ETC) scheme for zero-sum game problems is proposed. To solve the Hamilton-Jacobi-Isaacs equation of the unknown nonlinear system, the adaptive dynamic programming method with critic-identifier architecture is utilized. In order to train the neural networks (NN) more efficiently and avoid manually selecting the corresponding initial weights, the particle swarm optimization is employed. In addition, the actuator is updated aperiodically under the event-triggered framework, thus, reducing the computational burden and saving communication resources to some extend. A novel triggering rule, which can guarantee the closed-loop system to be uniformly ultimately bounded, is developed through the Lyapunov method. Finally, the effectiveness of the proposed ETC scheme is demonstrated via a simulation study.
{"title":"Event-triggered Control for Zero-sum Games Based on Critic-identifier Architecture with Particle Swarm Optimization","authors":"Weichen Luo, Mingming Liang, Derong Liu, Bo Zhao","doi":"10.1109/ICICIP53388.2021.9642185","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642185","url":null,"abstract":"In this paper, an event-triggered control (ETC) scheme for zero-sum game problems is proposed. To solve the Hamilton-Jacobi-Isaacs equation of the unknown nonlinear system, the adaptive dynamic programming method with critic-identifier architecture is utilized. In order to train the neural networks (NN) more efficiently and avoid manually selecting the corresponding initial weights, the particle swarm optimization is employed. In addition, the actuator is updated aperiodically under the event-triggered framework, thus, reducing the computational burden and saving communication resources to some extend. A novel triggering rule, which can guarantee the closed-loop system to be uniformly ultimately bounded, is developed through the Lyapunov method. Finally, the effectiveness of the proposed ETC scheme is demonstrated via a simulation study.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116258035","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642178
Hongbing Li, Juan Wu, L. Luo, Jiang Xiong
This paper investigates the jointly transmit power and unmanned aerial vehicle (UAV)/jammer deployment optimization problem for UAV enabled jammer-assisted secure communication systems, where a jammer is exploited to assist transmitting data from a source node to a legitimate destination node in the presence of multiple eavesdroppers. To increase the secrecy energy efficiency, which is defined as the achievable secrecy rate per energy consumption unit, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power and UAV/jammer deployment. The resulting optimization problem is shown to be a non-convex and fractional optimization problem, which is challenging to solve. As such, we decompose the original problem into two sub-problems, and then, an efficient iterative algorithm is proposed by leveraging the extensive search method and Dinkelbach method in combination with successive convex approximation (SCA) techniques. Numerical simulation results show that the proposed scheme significantly improves the secrecy energy efficiency of the system.
{"title":"UAV-Aided Secure Communication With Deployment Optimization and Cooperative Jamming","authors":"Hongbing Li, Juan Wu, L. Luo, Jiang Xiong","doi":"10.1109/ICICIP53388.2021.9642178","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642178","url":null,"abstract":"This paper investigates the jointly transmit power and unmanned aerial vehicle (UAV)/jammer deployment optimization problem for UAV enabled jammer-assisted secure communication systems, where a jammer is exploited to assist transmitting data from a source node to a legitimate destination node in the presence of multiple eavesdroppers. To increase the secrecy energy efficiency, which is defined as the achievable secrecy rate per energy consumption unit, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power and UAV/jammer deployment. The resulting optimization problem is shown to be a non-convex and fractional optimization problem, which is challenging to solve. As such, we decompose the original problem into two sub-problems, and then, an efficient iterative algorithm is proposed by leveraging the extensive search method and Dinkelbach method in combination with successive convex approximation (SCA) techniques. Numerical simulation results show that the proposed scheme significantly improves the secrecy energy efficiency of the system.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125824120","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642192
Ran Zhang, Hongzong Li, Jun Wang
Index tracking is a passive investment strategy by replicating a financial market index using its constituents. In this paper, index tracking is addressed based on $k-$medoids clustering. $k-$medoids clustering is formulated as a valuation-constrained $k-$median problem to cluster index constituents. The dissimilarity coefficients among stocks are measured by using dynamic time warping. Experimental results of index tracking on four major indices are elaborated to demonstrate that the tracking performance of the proposed method with dynamic time warping is superior to that with Pearson correlation coefficients.
{"title":"Index Tracking Based on Dynamic Time Warping and Constrained k-medoids Clustering","authors":"Ran Zhang, Hongzong Li, Jun Wang","doi":"10.1109/ICICIP53388.2021.9642192","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642192","url":null,"abstract":"Index tracking is a passive investment strategy by replicating a financial market index using its constituents. In this paper, index tracking is addressed based on $k-$medoids clustering. $k-$medoids clustering is formulated as a valuation-constrained $k-$median problem to cluster index constituents. The dissimilarity coefficients among stocks are measured by using dynamic time warping. Experimental results of index tracking on four major indices are elaborated to demonstrate that the tracking performance of the proposed method with dynamic time warping is superior to that with Pearson correlation coefficients.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128882935","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642187
J. Li, Chen Peng, Yuyan Wang, Yumin Yin, Bolin Liao
Extreme Learning Machine (ELM) is a special type of single hidden layer feedforward neural network, which uses pseudo-inverse to compute weights of the output layer, and is often faster than the gradient-based methods. However, due to the random initialization of input weights and biases, the performance of this algorithm is not always stable, and is less effective in large-scale applications. Therefore, in this paper, based on an effective large-scale optimization algorithm, i.e., cooperative coevolutionary particle swarm optimization (CCPSO), an improved hybrid ELM learning algorithm, named CCPSO-ELM, is proposed, where the input weights and the hidden layer biases are optimized using CCPSO. Compared with the traditional ELM algorithm, as well as ELM optimized by traditional PSO, the CCPSO-ELM is more likely to avoid local optima, has smaller optimization errors, and is more robust against noises. The results are verified by experiments on two different types of problems, i.e., large-scale multivariate function approximation and pattern classification.
{"title":"Pre-Optimization of High Dimensional Extreme Learning Machine with Cooperative Coevolution","authors":"J. Li, Chen Peng, Yuyan Wang, Yumin Yin, Bolin Liao","doi":"10.1109/ICICIP53388.2021.9642187","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642187","url":null,"abstract":"Extreme Learning Machine (ELM) is a special type of single hidden layer feedforward neural network, which uses pseudo-inverse to compute weights of the output layer, and is often faster than the gradient-based methods. However, due to the random initialization of input weights and biases, the performance of this algorithm is not always stable, and is less effective in large-scale applications. Therefore, in this paper, based on an effective large-scale optimization algorithm, i.e., cooperative coevolutionary particle swarm optimization (CCPSO), an improved hybrid ELM learning algorithm, named CCPSO-ELM, is proposed, where the input weights and the hidden layer biases are optimized using CCPSO. Compared with the traditional ELM algorithm, as well as ELM optimized by traditional PSO, the CCPSO-ELM is more likely to avoid local optima, has smaller optimization errors, and is more robust against noises. The results are verified by experiments on two different types of problems, i.e., large-scale multivariate function approximation and pattern classification.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130475420","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642174
Yan Li, Yi Dong, Piao Fan
Aiming at the problem of large positioning control error caused by the change of starting and ending position of piezoelectric stick-slip actuators, an adaptive PID full closed loop control method based on single neuron is proposed in this paper. Firstly, the dynamic model of the spring damping system is adopted, and the LuGre friction model is introduced to represent the stick-slip relationship between the drive block and the friction rod. Then, the adaptive PID full closed-loop control scheme based on single neuron and the principles of traditional PID algorithm and variable speed integral PID control algorithm are analyzed. The proposed control method is simulated and compared with the other two methods by MATLAB software. Finally, the effectiveness of the proposed method is verified by an experimental platform. The experimental results show that the maximum positioning errors of the proposed control method and the other two methods are 180 nm, 270 nm and 280 nm, respectively, when the desired position is 1 mm in low-frequency motion. Under the same experimental conditions, the proposed control scheme shows high control accuracy when the starting and ending position changes.
{"title":"Positioning Control of Piezoelectric Stick-slip Actuators Based on Single Neuron Adaptive PID Algorithm","authors":"Yan Li, Yi Dong, Piao Fan","doi":"10.1109/ICICIP53388.2021.9642174","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642174","url":null,"abstract":"Aiming at the problem of large positioning control error caused by the change of starting and ending position of piezoelectric stick-slip actuators, an adaptive PID full closed loop control method based on single neuron is proposed in this paper. Firstly, the dynamic model of the spring damping system is adopted, and the LuGre friction model is introduced to represent the stick-slip relationship between the drive block and the friction rod. Then, the adaptive PID full closed-loop control scheme based on single neuron and the principles of traditional PID algorithm and variable speed integral PID control algorithm are analyzed. The proposed control method is simulated and compared with the other two methods by MATLAB software. Finally, the effectiveness of the proposed method is verified by an experimental platform. The experimental results show that the maximum positioning errors of the proposed control method and the other two methods are 180 nm, 270 nm and 280 nm, respectively, when the desired position is 1 mm in low-frequency motion. Under the same experimental conditions, the proposed control scheme shows high control accuracy when the starting and ending position changes.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129297870","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642222
Zanyu Tang, Liangjie Ming, Yunong Zhang, Runhao Shi
Time-varying polar decomposition becomes important and has many applications in potential fields. This paper first proposes a continuous-time model and a discrete-time algorithm of zeroing neural network (ZNN) for time-varying polar decomposition. The continuous-time polar decomposition (CTPD) model is obtained by using ZNN method. Besides, the discrete-time polar decomposition (DTPD) algorithm of ZNN is obtained by utilizing a 7-instant Zhang time discretization (ZTD) formula for being realized in digital computer readily. Finally, this paper investigates two numerical examples with different dimensions. The corresponding numerical results substantiate the relative effectiveness of the proposed continuous-time model and discrete-time algorithm of ZNN for time-varying polar decomposition.
{"title":"Time-Varying Polar Decomposition by Continuous-Time Model and Discrete-Time Algorithm of Zeroing Neural Network Using Zhang Time Discretization (ZTD)","authors":"Zanyu Tang, Liangjie Ming, Yunong Zhang, Runhao Shi","doi":"10.1109/ICICIP53388.2021.9642222","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642222","url":null,"abstract":"Time-varying polar decomposition becomes important and has many applications in potential fields. This paper first proposes a continuous-time model and a discrete-time algorithm of zeroing neural network (ZNN) for time-varying polar decomposition. The continuous-time polar decomposition (CTPD) model is obtained by using ZNN method. Besides, the discrete-time polar decomposition (DTPD) algorithm of ZNN is obtained by utilizing a 7-instant Zhang time discretization (ZTD) formula for being realized in digital computer readily. Finally, this paper investigates two numerical examples with different dimensions. The corresponding numerical results substantiate the relative effectiveness of the proposed continuous-time model and discrete-time algorithm of ZNN for time-varying polar decomposition.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142478","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642203
Yongzhi Wang, Dan Wang, Zhouhua Peng
Model predictive control guarantees superior control performance with accurate model parameters. However, the prediction accuracy may be deteriorated due to the parameter uncertainties and parameter mismatches. In this paper, an improved model predictive direct power control (MPDPC) based on online parameter identification is proposed for pulse width modulation (PWM) rectifiers with fully unknown parameters. In this paper, an adaptive Parameter estimation update law is proposed to identify the unknown parameters. Two-stage filters are applied to ensure the convergence of estimation errors. In the control part, an MPDPC based on the identified parameters is proposed to realize the desired power control objectives. Simulation results under various parameter conditions are simulated to show the effectiveness of the proposed method.
{"title":"Model Predictive Direct Power Control for PWM Rectifiers Based on Online Parameter Identification","authors":"Yongzhi Wang, Dan Wang, Zhouhua Peng","doi":"10.1109/ICICIP53388.2021.9642203","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642203","url":null,"abstract":"Model predictive control guarantees superior control performance with accurate model parameters. However, the prediction accuracy may be deteriorated due to the parameter uncertainties and parameter mismatches. In this paper, an improved model predictive direct power control (MPDPC) based on online parameter identification is proposed for pulse width modulation (PWM) rectifiers with fully unknown parameters. In this paper, an adaptive Parameter estimation update law is proposed to identify the unknown parameters. Two-stage filters are applied to ensure the convergence of estimation errors. In the control part, an MPDPC based on the identified parameters is proposed to realize the desired power control objectives. Simulation results under various parameter conditions are simulated to show the effectiveness of the proposed method.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133066261","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642164
Luyang Han, Bolin Liao, Yongjun He, Xiao Xiao
Original zeroing neural network (OZNN) can effectively solve the problem of matrix inversion. Generally, the problem of matrix inversion is solved in the noiseless environment. However, noises are common, OZNN can not solve the problem with harmonic noise interference. Therefore, the integrated enhanced zeroing neural network (IEZNN) is proposed to overcome this difficulty. IEZNN can deal with the harmonic noise interference problem when the time change slightly. But in the case of large amplitude or frequency, IEZNN has not strong ability to tolerate the noise and the convergence speed is relatively slow. Therefore, by adding a novel nonlinear activation for IEZNN, which also has the ability to suppress noise, a dual noise-suppressed ZNN (DNSZNN) is proposed to solve this problem. DNSZNN not only has good noise suppression characteristics, but also can converge in the predefined time. Finally, the experimental results demonstrate that the DNSZNN has the best robustness and convergence performance under the same external harmonic noise interference compared with the OZNN and the IEZNN.
{"title":"Dual Noise-Suppressed ZNN with Predefined-Time Convergence and its Application in Matrix Inversion","authors":"Luyang Han, Bolin Liao, Yongjun He, Xiao Xiao","doi":"10.1109/ICICIP53388.2021.9642164","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642164","url":null,"abstract":"Original zeroing neural network (OZNN) can effectively solve the problem of matrix inversion. Generally, the problem of matrix inversion is solved in the noiseless environment. However, noises are common, OZNN can not solve the problem with harmonic noise interference. Therefore, the integrated enhanced zeroing neural network (IEZNN) is proposed to overcome this difficulty. IEZNN can deal with the harmonic noise interference problem when the time change slightly. But in the case of large amplitude or frequency, IEZNN has not strong ability to tolerate the noise and the convergence speed is relatively slow. Therefore, by adding a novel nonlinear activation for IEZNN, which also has the ability to suppress noise, a dual noise-suppressed ZNN (DNSZNN) is proposed to solve this problem. DNSZNN not only has good noise suppression characteristics, but also can converge in the predefined time. Finally, the experimental results demonstrate that the DNSZNN has the best robustness and convergence performance under the same external harmonic noise interference compared with the OZNN and the IEZNN.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122864073","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642176
Xinhui Zhu, Li Zhang, Yang Shi, Jing Wang, Jian Li
Robot manipulator control is a complicated multi-tasking problem in reality. It includes not only basic tracking task, but also additional tasks, such as conquering joint angle limits, posture control. However, most existing works only consider the goal of tracking and formulate it as single-layered time-variant problems, which leads to impracticality. In this work, robot manipulator control problem is formulated as four-layered time-variant equations including linear, nonlinear equalities and inequalities. Each layer formulates one subtask: The first layer of nonlinear equality describes basic tracking task based on forward kinematics; The second layer and third layer are inequalities, which describe joint angle upper and lower limits; The last layer is a linear equality with respect to joint angle velocity, which could be designed by user to describe other task, such as posture control. To solve this complicated four-layered time-variant problem, it is converted as single-layered equation based on the zeroing neural dynamics method. Then, continuous-time solution is proposed. Furthermore, discrete-time algorithm is proposed based on a third-order time-discretization formula and continuous-time solution. Numerical experiments illustrate the effectiveness and superiority compared to existing work.
{"title":"Robot Manipulator Control via Solving Four-Layered Time-Variant Equations Including Linear, Nonlinear Equalities and Inequalities","authors":"Xinhui Zhu, Li Zhang, Yang Shi, Jing Wang, Jian Li","doi":"10.1109/ICICIP53388.2021.9642176","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642176","url":null,"abstract":"Robot manipulator control is a complicated multi-tasking problem in reality. It includes not only basic tracking task, but also additional tasks, such as conquering joint angle limits, posture control. However, most existing works only consider the goal of tracking and formulate it as single-layered time-variant problems, which leads to impracticality. In this work, robot manipulator control problem is formulated as four-layered time-variant equations including linear, nonlinear equalities and inequalities. Each layer formulates one subtask: The first layer of nonlinear equality describes basic tracking task based on forward kinematics; The second layer and third layer are inequalities, which describe joint angle upper and lower limits; The last layer is a linear equality with respect to joint angle velocity, which could be designed by user to describe other task, such as posture control. To solve this complicated four-layered time-variant problem, it is converted as single-layered equation based on the zeroing neural dynamics method. Then, continuous-time solution is proposed. Furthermore, discrete-time algorithm is proposed based on a third-order time-discretization formula and continuous-time solution. Numerical experiments illustrate the effectiveness and superiority compared to existing work.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129548755","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642210
Shiyuan Fang, Hanzhi Li, Dacheng Pei, Meixi Wu, Yichong Sun, B. Cai
This paper concerns the problem of stochastic stability and stabilization for a class of discrete-time hidden semi-Markov jump systems (HS-MJSs) with time-varying emission probability. By virtue of the hidden semi-Markov model, such stochastic switching system has shown superior capacity of describing the asynchronous phenomenon between the practical system mode and the observed one. Moreover, the proposed emission probability is time-varying and satisfies piecewise homogeneous property in this paper. Based on the presented Lyapunov function, an observed-mode-dependent controller is constructed to stabilize the closed-loop HS-MJSs. Finally, the effectiveness of the proposed control scheme has been verified by a numerical example.
{"title":"Stabilization of Discrete-Time Hidden Semi-Markov Jump Systems with Time-varying Emission Probability","authors":"Shiyuan Fang, Hanzhi Li, Dacheng Pei, Meixi Wu, Yichong Sun, B. Cai","doi":"10.1109/ICICIP53388.2021.9642210","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642210","url":null,"abstract":"This paper concerns the problem of stochastic stability and stabilization for a class of discrete-time hidden semi-Markov jump systems (HS-MJSs) with time-varying emission probability. By virtue of the hidden semi-Markov model, such stochastic switching system has shown superior capacity of describing the asynchronous phenomenon between the practical system mode and the observed one. Moreover, the proposed emission probability is time-varying and satisfies piecewise homogeneous property in this paper. Based on the presented Lyapunov function, an observed-mode-dependent controller is constructed to stabilize the closed-loop HS-MJSs. Finally, the effectiveness of the proposed control scheme has been verified by a numerical example.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129867374","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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