Probability‐weighted nonlinear stochastic optimal control strategy of quasi‐integrable Hamiltonian systems with uncertain parameters

The nonlinear stochastic optimal control problem of quasi‐integrable Hamiltonian systems with uncertain parameters is investigated. The uncertain parameters are described by using a random vector with λ probability density function. First, the partially averaged Itô stochastic differential equations are derived by using the stochastic averaging method for quasi‐integrable Hamiltonian systems. Then, the dynamical programming equation is established based on stochastic dynamical programming principle. By minimizing the dynamical programming equation with respect to control forces, the optimal control forces can be derived, which are functions of the uncertain parameters. The final optimal control forces are then determined by probability‐weighted average of the obtained control forces with the probability density of the uncertain parameters as weighting function. The mean control effectiveness and mean control efficiency are used to evaluate the proposed control strategy. The robustness of the proposed control is measured by using the ratios of the variation coefficients of mean control effectiveness and mean control efficiency to the variation coefficients of uncertain parameters. Finally, two examples are given to illustrate the proposed control strategy and its effectiveness and robustness. Copyright © 2014 John Wiley & Sons, Ltd.     

Observer based repetitive learning control for a class of nonlinear systems with non‐parametric uncertainties

In this paper, a repetitive learning control (RLC) scheme is developed for a class of nonlinear systems to handle an output tracking problem, where two state observers are introduced concurrently to estimate the unavailable control system and reference states information. The estimation of reference state information is because of the lack of reference internal model in the RLC design. By virtue of the periodicity of reference signals and the associated learning capability in control mechanism, the involved unstructured nonlinear uncertainties can be handled. The Lyapunov‐like energy function method is adopted to facilitate the learning control design as well as property analysis thus achieve the asymptotical convergence of errors in state observation and output tracking simultaneously. Moreover, owing to the robustification of the learning controller that is addressed by incorporating projection, the proposed control scheme would be applicable in practice. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed RLC law. Copyright © 2014 John Wiley & Sons, Ltd.     

地下水污染风险评价研究进展

鉴于地下水污染风险评价是地下水环境风险评价的重要组成部分,也是地下水污染防控的重要环节,为全面了解地下水污染风险评价的研究现状,在综合国内外文献和资料的基础上,对地下水污染风险评价的相关研究成果进行总结与评述。首先,对地下水污染风险的概念进行回顾,并就其属性特征与评价历程进行概述。其次,重点介绍了地下水污染风险评价的主要研究内容与具体评价方法。最后,分析了当前地下水污染风险评价中存在的问题,指出未来需要在地下水污染风险评价的理论体系、地下水污染风险评价的不确定性、地下水污染风险区划以及风险决策管理等方面加强研究。     

Exact Sampling of a Linear Interval Predictor

Recent advances in the design of interval observers have made it possible to ensure the non‐divergence of the computed state bounds from the stability of LTI systems under bounded inputs, with no need for additional monotony assumptions. Time‐varying changes of coordinates can be used to that purpose. Most of the related works result in either continuous‐time or discrete‐time interval dynamics. This paper proposes a constructive algorithm to compute the exact sampled response of a linear interval predictor under bounded inputs, gives a stability equivalence result and discusses the design of interval observers. The exact sampling requires held input bounds but the uncertain input itself needs not to be held. A numerical example exhibiting an oscillatory behavior illustrates the main results.     

基于 SOLT 校准技术的S参数测量不确定度评定磁

目前,常用的 S 参数测量不确定度评定方法中均未考虑 S 参数相关性的问题,论文给出了一种2GHz ～18GHz 频率范围内 S 参数测量不确定度的评定方法,该方法从校准件的定义出发,基于 SOLT 校准技术对矢量网络分析仪进行自校准,将校准件引入的不确定度通过矢量网络分析仪传递给被测件（DUT ）,通过使用蒙特卡洛仿真的方法（MCM ）得到矢量网络分析仪测量 DUT S 参数的测量不确定度,评定过程中考虑了相关性问题,提高了 S 参数测量不确定度的可靠性和合理性。     

Stochastic stability and robust stabilization of semi‐Markov jump linear systems

The semi‐Markov jump linear system (S‐MJLS) is more general than the Markov jump linear system (MJLS) in modeling some practical systems. Unlike the constant transition rates in the MJLS, the transition rates of the S‐MJLS are time varying. This paper focuses on the robust stochastic stability condition and the robust control design problem for the S‐MJLS with norm‐bounded uncertainties. The infinitesimal generator for the constructed Lyapunov function is first derived. Numerically solvable sufficient conditions for the stochastic stability of S‐MJLSs are then established in terms of linear matrix inequalities. To reduce the conservativeness of the stability conditions, we propose to incorporate the upper and lower bounds of the transition rate and meanwhile apply a new partition scheme. The robust state feedback controller is accordingly developed. Simulation studies and comparisons demonstrate the effectiveness and advantages of the proposed methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

一种考虑径流预报及其不确定性的水库优化调度模型

鉴于纯随机径流描述或确定性径流预报的水库(群)优化调度模型,未考虑径流预报及其不确定性,导致优化计算结果与水库实际运行情况存在较大差异.本文提出了一种利用后验的径流状态转移概率和径流预报的可预测性概率来描述径流预报及其不确定性的优化调度模型.依据二滩水电站径流及其预报的实际状况,考虑不同预见期的径流预报信息,建立了考虑...     

基于贝叶斯模型加权平均方法的水文模型不确定性分析

贝叶斯模型加权平均（BMA）方法是通过综合几个模型预报值的后验分布来推断预报量的更可靠概率分布分析工具。它不仅能提供一个综合的预报值,还能提供一个综合的预报区间。本文采用3个水文模型,统一用SCE-UA算法率定参数,得到3组不同的预报值用于BMA方法的综合,着重分析比较BMA和单个模型的预报不确定性区间,来检验贝叶斯模型加权平均方法是否能提高预报的可靠性。结果表明,BMA方法不仅能提高预报精度,还能推求出性质更为优良的预报区间,提高预报的可靠性。