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- 저자 : Jiwei Wen (검색결과 5 건)
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Jiwei Wen,Li Peng,Sing Kiong Nguang 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.3
In this paper, the finite-time control problem is investigated for a class of discrete-time Markovian jump systems (MJLSs) with deterministic switching and time-delay. The considered systems are subject to a piecewise-constant transition probability (TP) matrix, which leads to both the deterministic switches and stochastic jumps. First, the stochastic finite-time boundedness (SFTB) and l2 gain analysis for the systems are studied by employing the average dwell time (ADT) approach. Note that a finite-time weighted l2 gain is obtained to measure the disturbance attenuation level. Then, the mode-dependent and variation-dependent controller is designed such that the resulting closed-loop systems are stochastically finite-time bounded and have a guaranteed disturbance attenuation level. Finally, a numerical example is given to verify the potential of the developed results.
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Switching Predictive Control for Continuous-time Markovian Jump Delay Systems
Jiwei Wen,Li Peng 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.3
This paper is concerned with switching model predictive control (SMPC) for continuous-time Markovianjump delay systems (MJDSs). First, a piecewise constant switching predictive controller, which only depends onthe average dwell time (ADT) switching laws rather than the jumping modes, is obtained by employing the ADTapproach under the infinite-time predictive control design framework . Such a control strategy is proposed to make atrade-off between robustness and adaptivity when the design complexity of mode-independent and mode-dependentMPC is considered. It is revealed that the SMPC can deal with MJDSs with both time varying and time invariantjump rates and cover the mode-independent MPC as a special cease. Second, the feasibility of the SMPC schemeand the mean square stability of the closed-loop MJDS are discussed by using the stochastic invariance of theellipsoid set over each sampling period. A numerical example is given to illustrate the main results.
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Prediction of Power Generation in China Using Process Neural Network
Jianghua Ge,Jiwei Wen,Chuntao Zhang,Yaping Wang,Gang Ding 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.5
The power generation prediction problem can be seen as a time series prediction problem in nature. The traditional time series prediction methods based on regression analysis do not take account into the time accumulation effect existed in the time series due to their discrete input values. This limitation causes the low prediction accuracy of the time series prediction methods based on regression analysis. To solve this problem, a power generation time series prediction model based on the process neural network is proposed. The inputs of the proposed prediction model can be continuous time-varying functions. The time accumulation effect existed in the power generation time series can be expressed and computed by the integration operator of the process neural network. The proposed prediction model is trained and the efficiency of the proposed prediction model is tested by the month power generation data form January 2001 to April 2012, and the comparison experiment results indicate that the process neural network performs better than the auto regression analysis.
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Robust H∞ Filtering for Average Dwell Time Switching Systems via a Nonmonotonic Function Approach
Yun Xie,Jiwei Wen,Li Peng 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.3
To improve the H∞ filtering performance, a non-monotonic Lyapunov function approach with N-stepahead predictive horizon is developed to design a robust H∞ filter for a discrete-time uncertain switched system. With increasing of the number N, filtering performance can be improved as well as the capability of disturbanceattenuation. However, the average dwell time (ADT) constraint of the switching law should be more critical as acost at the same time. To further relax the restriction on the switching law, the mode-dependent ADT switching isintroduced to reduce the ADT bound such that a trade-off between the switching frequency and filtering performancecan be achieved. Therefore, a co-design of the filter and switching law can be obtained by the developed approach.
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Zhihai Wu,Li Peng,Linbo Xie,Jiwei Wen 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.1
This paper is devoted to the stochastic bounded consensus tracking problems of second-order multi-agent systems, where the control input of each agent can only use the information measured at the sampling instants from its neighbors or the virtual leader with a time-varying reference state, the measurements are corrupted by random noises, and the signal sampling process induces the small sampling delay. The augmented matrix method, the probability limit theory and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. We show that the convergence of the proposed protocol simultaneously depends on the constant feedback gains, the network topology, the sampled period and the sampling delay, and that the static consensus tracking error depends on not only the above mentioned factors, but also the noise intensity and the upper bound of the velocity and the acceleration of the virtual leader. The obtained results cover no sampling delay as its one special case. Simulations are provided to demonstrate the effectiveness of the theoretical results.
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