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Your search keyword '"Peng, Zhaoxia"' showing total 198 results
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198 results on '"Peng, Zhaoxia"'

2. Robust Event Triggering Control for Lateral Dynamics of Intelligent Vehicles with Designable Inter-event Times

Author

Chu, Xing, Liu, Zhi, Mao, Lei, Jin, Xin, Peng, Zhaoxia, and Wen, Guoguang

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this brief, an improved event-triggered update mechanism (ETM) for the linear quadratic regulator is proposed to solve the lateral motion control problem of intelligent vehicle under bounded disturbances. Based on a novel event function using a clock-like variable to determine the triggering time, we further introduce two new design parameters to improve control performance. Distinct from existing event-based control mechanisms, the inter-event times (IETs) derived from the above control framework are designable, meaning that the proposed ETM can be deployed on practical vehicle more easily and effectively. In addition, the improved IETs-designable ETM features a global robust event-separation property that is extremely required for practical lateral motion control of vehicle subject to diverse disturbances. Theoretical analysis proves the feasibility and stability of the proposed control strategy for trajectory tracking under bounded disturbances. Finally, simulation results verify the theoretical results and show the advantages of the proposed control strategy., Comment: 5pages, 4 figures

Published
2022

3. Novel Nussbaum-Type Function based Safe Adaptive Distributed Consensus Control with Arbitrary Unknown Control Direction

Author

Qiao, Dan, Peng, Zhaoxia, Wen, Guoguang, and Huang, Tingwen

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Existing Nussbaum function based methods on the consensus of multi-agent systems require (partial) identical unknown control directions of all agents and cause dangerous dramatic control shocks. This paper develops a novel saturated Nussbaum function to relax such limitations and proposes a Nussbaum function based control scheme for the consensus problem of multi-agent systems with arbitrary non-identical unknown control directions and safe control progress. First, a novel type of the Nussbaum function with different frequencies is proposed in the form of saturated time-elongation functions, which provides a more smooth and safer transient performance of the control progress. Furthermore, the novel Nussbaum function is employed to design distributed adaptive control algorithms for linearly parameterized multi-agent systems to achieve average consensus cooperatively without dramatic control shocks. Then, under the undirected connected communication topology, all the signals of the closed-loop systems are proved to be bounded and asymptotically convergent. Finally, two comparative numerical simulation examples are carried out to verify the effectiveness and the superiority of the proposed approach with smaller control shock amplitudes than traditional Nussbaum methods.

Published
2022

9. A unified framework of fully distributed adaptive output time-varying formation control for linear multi-agent systems: an observer viewpoint

Author

Jiang, Wei, Peng, Zhaoxia, Wen, Guoguang, and Rahmani, Ahmed

Subjects
Computer Science - Systems and Control
Abstract

This paper presents a unified framework of time-varying formation (TVF) design for general linear multi-agent systems (MAS) based on an observer viewpoint from undirected to directed topology, from stabilization to tracking and from a leader without input to a one with bounded input. The followers can form a TVF shape which is specified by piecewise continuously differential vectors. The leader's trajectory, which is available to only a subset of followers, is also time-varying. For the undirected formation tracking and directed formation stabilization cases, only the relative output measurements of neighbors are required to design control protocols; for the directed formation tracking case, the agents need to be introspective (i.e. agents have partial knowledge of their own states) and the output measurements are required. Furthermore, considering the real applications, the leader with bounded input case is studied. One main contribution of this paper is that fully distributed adaptive output protocols, which require no global information of communication topology and do not need the absolute or relative state information, are proposed to solve the TVF control problem. Numerical simulations including an application to nonholonomic mobile vehicles are provided to verify the theoretical results., Comment: 21 pages

Published
2018

10. Distributed consensus of linear MASs with an unknown leader via a predictive extended state observer considering input delay and disturbances

Author

Jiang, Wei, Peng, Zhaoxia, Rahmani, Ahmed, Hu, Wei, and Wen, Guoguang

Subjects
Computer Science - Systems and Control
Abstract

The problem of disturbance rejection/attenuation for constant-input delayed linear multi-agent systems (MASs) with the directed communication topology is tackled in this paper, where a classic model reduction technique is introduced to transform the delayed MAS into the delay-free one. First, when the leader has no control input, a novel adaptive predictive extended state observer (ESO) using only relative state information of neighboring agents is designed to achieve disturbance-rejected consensus tracking. The stabilization analysis is presented via the Lyapunov function and sufficient conditions are derived in terms of linear matrix inequalities. Then the result is extended to the disturbance-attenuated case where the leader has bounded control input which is only known by a portion of followers. Finally, two numerical examples are presented to illustrate the effectiveness of proposed strategies. The main contribution focuses on the design of adaptive predictive ESO protocols with the fully distributed property., Comment: 18 pages

Published
2018

13. Cyclo‐Bis(Acyl)phosphine Oxides as Efficient Photoinitiators for Radical Polymerization.

Author

Peng, Zhaoxia, Shen, Qi, and Chen, Xiaodan

Subjects
*PHOSPHINE oxides, *RADICALS (Chemistry), *POLYMERIZATION, *INDUSTRIAL applications, *OXIDES
Abstract

Acyclic acylphosphine oxides have been extensively utilized in industrial applications as typical photoinitiators (PIs). In this study, we demonstrate the efficiency of their cyclic analogues, cyclo‐bis(acyl)phosphine oxides (CBAPOs), as efficient PIs. We present the isolation and X‐ray crystallographic characterization of CBAPOs. These compounds exhibit a long maximum absorption wavelength ranging between 415 and 420 nm, extending well into the near 500 nm region. Radical trapping experiments suggest that CBAPOs undergo homolytic P−C(O) bond α‐cleavage. Steady‐state photolysis conducted with LED light at 390 nm, monitored by UV‐Vis spectroscopy, confirms the gradual decay of CBAPOs over time. Additionally, all CBAPOs are capable of initiating radical polymerization under irradiation at 365 nm. Notably, one of the CBAPOs (4a) demonstrates comparable photoinitiating efficiency to one of commercially available bis(acyl)phosphine oxides (Irgacure 819). [ABSTRACT FROM AUTHOR]

Published
2024
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27. Adaptive Neural Network-Based Event-Triggered SOC Observer With Application to a Stochastic Battery Model

Author

Pan, Chenyang, Peng, Zhaoxia, Yang, Shichun, Wen, Guoguang, Luo, Biao, and Huang, Tingwen

Abstract

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

Published
2024
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28. Distributed Constrained Optimal Formation Matching for Large-Scale Systems

Author

Wu, Bofan, Peng, Zhaoxia, Wen, Guoguang, Huang, Tingwen, and Rahmani, Ahmed

Abstract

In this article, we investigate a distributed constrained optimal formation matching problem for a large-scale multiagent system. A distributed formation matching algorithm for a large-scale multiagent system (DFMA-LSMAS) is proposed. The algorithm employs a distributed continuous-time strategy to deal with a minimal weight bipartite graph matching problem for the optimal matching relationship between each agent and each hole in the formation configuration. It prevents a centralized structure and the explosion of storage spaces compared with the Kuhn-Munkras algorithm. Additionally, DFMA-LSMAS utilizes a distributed parameter projection approach for the optimal location of the formation configuration subjected to a common state constraint. It reduces the growth of the auxiliary variables with the scale of the multiagent system. In the special case, an unmatched phenomenon appears which may cause the failure of DFMA-LSMAS. Therefore, a perturbation-based algorithm is provided to eliminate the influence of this phenomenon, but does not affect the optimality of the solution. Finally, simulation results are provided to verify the algorithms.

Published
2024
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29. Optimal Stealthy Linear Man-in-the-Middle Attacks With Resource Constraints on Remote State Estimation

Author

Zhang, Yingwen, Peng, Zhaoxia, Wen, Guoguang, Wang, Jinhuan, and Huang, Tingwen

Abstract

This article studies the impact of constrained optimal stealthy attacks on the state estimator, where man-in-the-middle attacks with a linear form can compromise innovations transmitted through a wireless network. First, a novel resource-constrained attack model is proposed, in which there are only a finite number of attack instants within a fixed interval. Second, the evolution of the estimation error covariance under attacks is obtained, and the covariance at the ultimate instant of the attack interval is regarded as the attacker’s cost function. Moreover, a relaxed condition of the strict stealthiness, named Kullback–Leibler divergence, is employed to describe the attacker’s the stealthiness metric. Third, the one-time and holistic optimization problems of stealthy attacks are solved by exploiting the Lagrange multiplier method. Then the constrained optimal attack strategies are obtained to produce the largest ultimate estimation error covariance. Finally, two simulation cases are provided to confirm the correctness of the designed attack strategies.

Published
2024
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42. Traveling Wave Solutions in a One-Dimension Theta-Neuron Model

Author

Wen, Guoguang, Yu, Yongguang, Peng, Zhaoxia, Hu, Wei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Yu, Wen, editor, He, Haibo, editor, and Zhang, Nian, editor

Published
2009
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43. Modeling Driver's Real-Time Confidence in Autonomous Vehicles.

Author

Lu, Jiayi, Yang, Shichun, Ma, Yuan, Shi, Runwu, Peng, Zhaoxia, Pang, Zhaowen, Chen, Yuyi, Feng, Xinjie, Wang, Rui, Cao, Rui, Liu, Yibing, Wang, Qiuhong, and Cao, Yaoguang

Subjects
AUTONOMOUS vehicles, MOTOR vehicle driving, CONFIDENCE, HUMAN-machine systems, SUSPICION, TEST systems
Abstract

Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver's burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers' use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called "the Virtual Confidence (VC)" by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Adaptive Neural Network-Based Prescribed-Time Observer for Battery State-of-Charge Estimation.

Author

Pan, Chenyang, Peng, Zhaoxia, Yang, Shichun, Wen, Guoguang, and Huang, Tingwen

Abstract

Convergence speed is an important indicator to evaluate the performance of state-of-charge (SOC) estimators. To improve the convergence speed, this article proposes an adaptive radial basis function neural network-based prescribed-time observer to estimate the battery SOC. First, an adaptive RBF NN is employed to approximate the nonlinear part of the battery equivalent circuit model, and the online learning process of network weight can adapt the variations in battery parameters. Then, a prescribed-time SOC observer is developed to ensure the state and weight estimation errors converge within the convergence time $T$ , which can be prescribed by users and is irrelevant on initial values. Thus, the network weight no longer needs to update when time exceeds $T$ , and the computational burden can be effectively saved. Furthermore, a switched-gain scheme with a naturally switched time $T$ is employed to simultaneously guarantee the convergence speed and estimation accuracy. An adaptive robust term is designed to compensate the approximation error and possible variations of the network weight in the steady state. Finally, the theoretical stability is proved by the Lyapunov theory, and the practical effectiveness is evaluated by experiments and simulations. [ABSTRACT FROM AUTHOR]

Published
2023
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