基于不完全转移率的连续Markov 跳变奇异系统的H∞ 控制

研究一类不完全转移率信息的Markov跳变奇异系统的H∞控制问题,提出连续Markov跳变奇异系统的新型有界实引理,并将其推广到不完全转移率条件.进一步设计H∞状态反馈控制器,使得闭环系统在转移率部分未知的条件下随机可容许,且满足H∞性能H.所得结论涵盖了奇异矩阵模态依赖情形,且表示为严格线性矩阵不等式形式,利于工程实现.最后,通过仿真算例表明了所提出方法的有效性和优越性.     

Finite‐time sliding mode observer for uncertain nonlinear systems based on a tunable algebraic solver

In this paper, a finite‐time sliding mode observer for nonlinear systems with unknown inputs is proposed. The observer is based on a method for the solution of time‐varying algebraic equations. This algebraic solver is shown to converge in finite time by means of Lyapunov analysis; furthermore, a way to tune it so that it converges after a user‐defined amount of time is presented. Through the use of this technique and sliding mode differentiators, the state variables and unknown inputs of a class of nonlinear systems, which do not need to be affine in the inputs, can be estimated without the explicit use of state transformations. Both the algebraic solver and the proposed observer are illustrated through simulation examples. Copyright © 2017 John Wiley & Sons, Ltd.     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.     

基于深度神经网络的多指灵巧手抓取手势优化

基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。     

Fault accommodation for uncertain linear systems with measurement errors

This paper investigates the observer based fault accommodation (FA) problem for linear systems with state perturbations and measurement errors. A novel intermediate estimator is proposed to estimate the state and the faults simultaneously, where the effect of state perturbations and measurement errors can be effectively eliminated by fully exploiting the design freedom of some specified parameters. Based on the estimation, a fault tolerant control (FTC) scheme is synthesized. It is proved that the states of the resulting closed loop system are uniformly ultimately bounded with an explicit bound. Simulation examples verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Model Predictive Control of Glucose Concentration Based on Signal Temporal Logic Specifications with Unknown-Meals Occurrence

Abstract

The glycemia regulation is a significant challenge in the Artificial Pancreas (AP) scenario. Several control systems have been developed in the last years, many of them requiring meal announcements. Therefore, if the patients skip the meal announcement or make a mistake in the estimation of the amount of carbohydrates, the control performance will be negatively affected. In this extended version of our previous work, we present a Model Predictive Controller (MPC) for the AP in which the meal is treated as a disturbance to be estimated by an Unknown Input Observer (UIO). The MPC constraints are expressed in terms of Signal Temporal Logic (STL) specifications. Indeed, in the AP some requirements result in hard constraints (in particular, absolutely avoid hypoglycemia and absolutely avoid severe hyperglycemia) and some other in soft constraints (avoid a prolonged hyperglycemia) and STL is suitable for expressing such requirements. The achieved results are obtained using the BluSTL toolbox, which allows to synthesize model predictive controllers with STL constraints. We report simulations showing that the proposed approach, avoiding unnecessary restrictions, provides safe trajectories in correspondence of higher unknown disturbance.     

一个数列有界性的新证明

利用均值不等式,给出数列{（1＋1/n）^n}有界性证明的新方法,使重要极限的证明得以简化。     

Fault Detection for Nonlinear Systems with Unknown Input

This paper extends the problem of fault detection for linear discrete‐time systems with unknown input to the nonlinear system. A nonlinear recursive filter is developed where the estimation of the state and the input are interconnected. Unknown input which can be any type of signal was obtained by least‐squares unbiased estimation and the state estimation problem is transformed into a standard unscented Kalman filter (UKF) problem. By testing the mean of the innovation process, a real‐time fault detection approach is proposed. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

积分C半群的Laplace逆变换

以积分C半群生成定理的Laplace刻划为基础,利用积分半群的性质,推导出指数有界积分半群的一种表达形式——Laplace逆变换形式.利用泛函分析的基本理论得到了两个关于Laplace逆变换形式的相关结果.     

基于心理账户的金融期权定价模型

给出了基于心理账户的三种期权定价模型，对该三种模型和基于无套利期权定价模型各自得到的结果作了比较分析，结果表明基于心理账户的三种期权定价模型得到的价格与股票价格运动实际概率有关，符合人们依据直觉的投资习惯；而基于无套利期权定价模型与股票价格运动实际概率无关，不符合现实情况．