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M-ary information processing via an optimal nonlinear detector

Chinese Physics B 2009年第12期18卷 5196-5202页

作者：李建龙 Department of Information Science & Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Network Technology

This paper presents a novel approach of M-ary baseband pulse amplitude modulated signal processing via a parameter-optimized nonlinear dynamic system. This nonlinear system usually shows the phenomenon of stochastic resonance by adding noise. To thoroughly discuss the signal processing performance of the nonlinear system, we tune the system parameters to obtain a nonlinear detector with optimal performance. For characterizing the output of the nonlinear system, the derivation of the probability of detection error is given by the system response speed and the probability density function of the nonlinear system output. By varying the noise intensity with fixed system parameters, the phenomenon of stochastic resonance is shown and by tuning the system parameters with fixed noise, the probability of detection error is minimized and the nonlinear system is optimized. The detection performance of the two cases is compared with the theoretical probability of detection error, which is validated by numerical simulation.

关键词： nonlinear detector stochastic resonance tuning parameters probability of detection error

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Adaptive H∞control of polynomial Hamiltonian systems via symbolic computation:controller parameterisation

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Journal of Control and Decision 2020年第2期7卷 160-177页

作者： Zhong Cao Xiaorong Hou Wenjing Zhao School of Physics and Electronic Engineering Guangzhou UniversityGuangzhou510006People’s Republic of China Mathematical Educational Software of Engineering Technology Research Center of Guangdong Province Guangzhou510006People’s Republic of China School of Automation Engineering University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Lab Center Guangzhou UniversityGuangzhouPeople’s Republic of China

This paper deals with controller parameterisation method of adaptive H∞control for polynomial Hamiltonian systems(PHSs)with disturbances and unknown parameters.We design a simplified controller with a set of tuning parameters which can guarantee that the systems are adaptive H∞stable by using Hamiltonian function method.Then,a method for solving the set of tuning parameters of the controller with symbolic computation is presented.The proposed parameterisation method avoids solving Hamilton–Jacobi–Issacs(HJI)equations and the obtained controller is easier as compared to some existing ones.Simulation example shows that the controller is effective as it can optimise adaptive H∞control by adjusting tuning parameters.All these results are expected to be of use in the study of adaptive H∞control for nonlinear systems with disturbances and unknown parameters.

关键词： Symbolic computation polynomial Hamiltonian systems(PHSs) tuning parameters adaptive H∞control

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Evidence of parameter-induced aperiodic stochastic resonance with fixed noise

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Chinese Physics B 2007年第2期16卷 340-345页

作者：李建龙 Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China

Stochastic resonance （SR） is based on the cooperative effect between the stochastic dynamical system and the external forcing. As is well known, the cooperative effect is produced by adding noises. In this paper, we show the evidence that by changing the system parameters and the signal intensity, a nonlinear system in the presence of an input aperiodic signal can yield the cooperative effect, with the noise fixed. To quantify the nonlinear system output, we determine the theoretical bit error rate （BER）. By numerical simulation, the validity of the theoretical derivation is checked. Besides, we show that parameter-induced SR is more realizable than SR via adding noises, especially when the noise intensity exceeds the resonance level, or when the characteristic of the noise is not known.

关键词： aperiodic stochastic resonance tuning parameters bit error rate

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Parameter-induced stochastic resonance with a periodic signal

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Chinese Physics B 2006年第12期15卷 2867-2871页

作者：李建龙徐博侯 Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Department of Mechanics State Key Laboratory of Fluid Power Transmission and Control Zhejiang University Hangzhou 310027 China

In this paper conventional stochastic resonance （CSR） is realized by adding the noise intensity. This demonstrates that tuning the system parameters with fixed noise can make the noise play a constructive role and realize parameter- induced stochastic resonance （PSR）. PSR can be interpreted as changing the intrinsic characteristic of the dynamical system to yield the cooperative effect between the stochastic-subjected nonlinear system and the external periodic force. This can be realized at any noise intensity, which greatly differs from CSR that is realized under the condition of the initial noise intensity not greater than the resonance level. Moreover, it is proved that PSR is different from the optimization of system parameters.

关键词： tuning parameters stochastic resonance signal-to-noise ratio

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Multi-MPC control of Hammerstein systems with an included-angle-based weighting method

Multi-MPC control of Hammerstein systems with an included-an...

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第37届中国控制会议

作者： Jingjing Du Lei Zhang Jun Feng Chen College of Internet of Things Engineering Hohai University College of Internet of Things Nanjing University of Posts and Telecommunications

Multi-model predictive control（Multi-MPC） of Hammerstein systems is developed using an included angle based weighting method,which takes advantage of the special structure of the Hammerstein models.The proposed weighting method can be calculated offline and has merely one tuning parameter.Thus it is comparatively intuitive and simple.What is more,it can coordinate local controllers effectively.A Lab-tank system which can be modeled into a Hammerstein model is investigated.Comparisons have been made with the proposed weighting method and traditional methods.

关键词： Included angle weighting multi-MPC Hammerstein systems tuning parameters

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