A new Gaussian approximate (GA) filter for nonlinear systems with one-step randomly delayed measurement and correlated noise is proposed in this paper. Firstly, a general framework of Gaussian filter is designed under...
A new Gaussian approximate (GA) filter for nonlinear systems with one-step randomly delayed measurement and correlated noise is proposed in this paper. Firstly, a general framework of Gaussian filter is designed under Gaussian assumption on the conditional density. Then, the implementation of Gaussian filter is transformed into the approximation of the Gaussian weighted integral in the proposed frame. Secondly, a new cubature Kalman filtering(CKF)algorithm is developed on the basis of the spherical-radial cubature rule. The efficiency and superiority of the proposed method are illustrated in the numerical examples.
In this paper, the stability of Amplidyne Electrical systems (AESs) with a time-varying delay is studied. Firstly, the model of AESs with a time-varying delay is established. Secondly, an augmented Lyapunov-Krasovskii...
In this paper, the stability of Amplidyne Electrical systems (AESs) with a time-varying delay is studied. Firstly, the model of AESs with a time-varying delay is established. Secondly, an augmented Lyapunov-Krasovskii functional (LKF) is constructed. Then, a less conservative delay-dependent stability criterion for AESs with a time-varying delay is obtained by utilizing the generalized reciprocally convex combination and an advanced negative-determination quadratic function lemma. Finally, the superiority and effectiveness of the proposed criterion is verified by a numerical example.
This paper proposes a distributed optimization algorithm based on alternating direction method of multipliers (ADMM) for the distributed optimization problem of multi-agent systems, called ADMM with adaptive penalty t...
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We exploit the snapping-through buckling phenomenon exhibited by hierarchical DCB structures to devise a mechanical metamaterial with a unique deformation mode, encompassing multi-stability, multi-path, multi-platform...
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This paper is concerned with the stability of discrete-time networked controlsystems with network induced delay and malicious packet dropout. Firstly, network induced delay and malicious packet dropout are analyzed, ...
This paper is concerned with the stability of discrete-time networked controlsystems with network induced delay and malicious packet dropout. Firstly, network induced delay and malicious packet dropout are analyzed, and the data packet dropout is converted into the change rate of time delay. Secondly, the functional of time delay and change rate of time delay is constructed, and some summation terms are generated when calculating the functional forward difference. Moreover, the auxiliary-function-based summation inequality and reciprocally convex matrix inequality are used to estimate the resulting summation terms. Then, a less conservative stability criterion for discrete networked systems with network delay and data packet loss is established. Finally, the validity of the proposed stability criterion is illustrated by a numerical example.
As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel *** management strategy(EMS)is playing a key role to improve the energy eff...
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As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel *** management strategy(EMS)is playing a key role to improve the energy efficiency of this *** paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two *** high-level establishes a parallel system first,which includes a real powertrain system and an artificial ***,the synthesized data from this parallel system is trained by a bidirectional LSTM *** lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)*** is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined ***,real vehicle testing is implemented and relevant experiment data is collected and *** results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
In this paper, the master-slave synchronization issue of chaotic Lur’ e systems with time-varying-delay feedback control is investigated. Firstly, the synchronization problem of chaotic system is transformed into the...
In this paper, the master-slave synchronization issue of chaotic Lur’ e systems with time-varying-delay feedback control is investigated. Firstly, the synchronization problem of chaotic system is transformed into the stability problem of chaotic synchronization error system, which is studied based on Lyapunov-Krasovskii functional (LKF) method. Secondly, a novel augmented LKF with more cross terms that related to time-varying delay is proposed. Based on the application of the relaxation integral inequality and the reciprocally convex matrix inequality, an improved synchronization criterion is derived by using the cubic function negative-determination lemma. Finally, a numerical simulation example demonstrates the effectiveness and advantages of the proposed methods.
作者:
Jiali HaoYa ZhangSchool of Automation
Southeast University and with Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China
This paper studies the consensus Kalman filtering algorithm with distributed attack detection for reducing the effects of false data injection attacks in wireless sensor networks. The FDI attacks are randomly injected...
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ISBN:
(数字)9781728177090
ISBN:
(纸本)9781728177106
This paper studies the consensus Kalman filtering algorithm with distributed attack detection for reducing the effects of false data injection attacks in wireless sensor networks. The FDI attacks are randomly injected into communication channels or sensors with certain probabilities, which undermines the accuracy of the transmission data and the accuracy of measurement data respectively. χ 2 detector is applied, and if the received data is determined to be attacked, it is omitted in consensus Kalman filtering. It is proved that if the FDI attack is randomly injected into communication channels, under the consensus Kalman filtering algorithm with adaptive weighting protocol and attack detection, the estimation errors of the sensor network can be bounded in probability. If the FDI attack is randomly injected into sensors, a probability condition on the attacks is given to guarantee the estimation errors of the sensor network bounded in mean square sense. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.
This paper is concerned with $H_{\infty}$ performance state estimation of static neural networks with a time-varying delay. First, a PI estimator with exponential term is used to estimate neuron states based on outp...
This paper is concerned with $H_{\infty}$ performance state estimation of static neural networks with a time-varying delay. First, a PI estimator with exponential term is used to estimate neuron states based on output measurement. Second, an augmented Lyapunov-Krasovskii functional (LKF) containing delay-product-type non-integral terms and single integral terms is constructed by introducing negative definite terms. After that, a criterion with less conservatism is derived based on extended reciprocally convex matrix inequality. Finally, a numerical example is provided to reveal the effectiveness of the proposed approach.
As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solut...
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ISBN:
(数字)9781728144429
ISBN:
(纸本)9781728144436
As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.
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