频域内LESO和HGTD的稳定性分析

在频域内提出了线性扩张状态观测器和高增益跟踪微分器的稳定性研究方法.推导出线性扩张状态观测器和高增益跟踪微分器的稳定性条件以及估计精度,可以证明频域内的分析结果与时域内的分析结果一致.根据估计误差计算结果,提出了两种提高估计精度的方法,并通过幅频特性分析和数值仿真验证了两种方法的可行性.     

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation北大核心CSCD

为解决模型参数不确定与外界干扰影响下,四旋翼无人机飞控作业中姿态与轨迹跟踪精度下降,反应迟缓的问题,利用拓展Kalman滤波应对非线性系统问题出色的适应能力和噪声抑制能力,对四旋翼状态信息进行初步估算来抑制高频信号干扰,从而降低了扩张状态观测器的估计负担.同时,与扩张状态观测器联合估计由系统不确定性参数与外界扰动联合组成的“总扰动”,使系统对于精确模型的依赖性降低,并利用扰动估计的微分值进行前馈补偿,以提高对突变扰动的跟踪精度,克服了突变干扰下的相位滞后现象.综合联合观测器、带前馈补偿的LESO及带误差补偿的PD控制律,形成了一种利用拓展Kalman滤波与前馈补偿后的扩张状态观测器联合观测扰动,能较大程度抑制高频噪声和突变扰动的改进型自抗扰控制器.仿真与实验结果表明,联合观测器能有效地减小观测误差幅值且能超前校正观测相位滞后,从而更好地得到更精确的状态信息,改进型自抗扰控制器能更好地满足四旋翼飞行器快速反应、高效稳定的控制要求,精准高效地完成复杂轨迹跟踪.     

一类不确定随机非线性系统的采样扩张状态观测器设计与观测误差的收敛性分析

实际系统中普遍存在各种干扰和不确定性因素,并且对控制系统的性能造成负面影响,因此设计对干扰和不确定性具有优异估计性能的观测器显得尤为重要.针对一类具有采样输出的不确定随机非线性系统,设计相应的采样扩张状态观测器用于在线估计不可量测状态和影响系统性能的随机总干扰.所估计的随机总干扰包含系统内部未建模动态、统计特性未知的外部有界噪声干扰以及不确定性因素的非线性耦合作用.在每个采样区间内,设计一个输出预估器用于估计实际输出,相应的输出估计值用于采样扩张状态观测器的设计.证明了所设计的采样扩张状态观测器对不可量测状态和随机总干扰的观测误差的均方收敛性.最后,一个具体仿真实例用于证实理论结果.     

冷轧机压下伺服系统的输出反馈有限时间控制研究

针对冷轧机压下电液伺服系统存在非线性、参数不确定性和负载干扰等特点,提出了基于扩张状态观测器的输出反馈有限时间控制方法.利用扩张状态观测器对系统的状态变量进行观测,解决了电液伺服系统的速度和加速度难以实际测量的难题,并对未知函数及扰动具有鲁棒性.依据有限时间控制理论并结合反步构造法设计了有限时间控制器,并证明了系统的全局稳定性.仿真表明:扩张状态观测器能够快速准确的观测系统的状态;基于扩张状态观测器的有限时间控制方法能使系统快速、准确地跟踪指定位置目标,并具有很强的鲁棒性.     

离散时间模糊控制系统的新型状态观测器设计

针对离散时间模糊控制系统的状态估计问题,设计出一种新颖的多时刻依赖型模糊状态观测器.与已有结果不同,所设计的模糊状态观测器设计不仅依赖于当前时刻的模糊隶属函数,还依赖于过去时刻的模糊隶属函数;这些特性为状态估计设计引入更多的自由度,可以得到保守性显著降低的模糊状态观测器.此外,针对衍生出多时刻模糊求和问题,还提出了一种新型松弛变量技术来进一步降低模糊状态估计问题的保守性.特别地,本文所设计的模糊观测器属于可求解的一组线性矩阵不等式,所以具有较好的可实现性.最后,通过一组仿真实验来验证本文结果的优越性.     

自抗扰控制处理边界带有干扰的具有尖端质量的Timoshenko梁方程的稳定性

讨论了边界带有不确定扰动的一维Timoshenko梁方程的边界反馈稳定性.运用的方法主要是自抗扰控制,其主要思想是通过构造常数高增益扩张状态观测器将扰动在线估计并在反馈控制中实时消除.最后通过李雅普诺夫方法证明了闭环系统具有实用稳定性.     

基于扩张状态观测器的广义预测控制

针对线性对象,提出了一种基于扩张状态观测器的广义预测控制算法.首先,分离出对象中异于标准积分串联型的部分,把它作为扩张状态,再利用观测器对其进行实时估计和补偿,将原始对象近似转化为积分串联型.然后,针对积分串联型这种特殊的对象设计广义预测控制.这既降低了预测控制对于模型的敏感性,同时又极大简化了计算,取消了控制的增量形式以提高系统的鲁棒性但依然保持了闭环无静差特性.通过内模原理得到了闭环特征多项式.仿真结果验证了所提算法对于对象的参数摄动具有很强的动态性能鲁棒性.     

基于自抗扰控制器的CNC雕刻机控制系统轮廓误差控制

针对CNC雕刻机控制系统因时变时延影响轮廓跟踪精度的问题,设计了基于自抗扰控制的单轴轨迹跟踪控制器和基于非线性PID(NLPID)的轮廓误差补偿控制器.首先,针对单轴轨迹跟踪控制,将时变时延引起的不确定性处理成系统总干扰的一部分,设计扩张状态观测器(ESO),对系统内外总干扰和系统状态进行实时估计,并设计误差补偿控制律,实现系统干扰的估计和补偿,得到良好的单轴轨迹跟踪控制性能.然后,根据轮廓误差估计值,设计基于NLPID的轮廓误差补偿控制器,对系统轮廓误差实时补偿,实现了良好的轮廓跟踪控制性能.最后,通过实验验证了所提方法的有效性.     

基于自抗扰控制的直升机航向控制方法

针对直升机航向动力学包含输入非线性、时变参数和主-尾旋翼之间的强耦合的特性,传统的控制方法很难实现良好的性能.提出了一种自抗扰控制器的设计方法,并设计了扩张状态观测器,对参数不确定性和外部干扰进行估计,并实时补偿.与常规控制相比,在保证闭环稳定性的同时,能够应对模型参数的不确定性以及扰动,能够进一步使跟踪误差满足期望精度.并针对实际模型直升机实验平台航向动力学模型,在参数不确定性和主旋翼有扰动情况下,仿真结果验证了该方法的有效性.     

基于扩张状态观测器的机电伺服系统饱和补偿与自适应滑模控制

针对带有未知摩擦力矩与输入饱和约束的机电伺服系统,提出了一种基于扩张状态观测器的伺服系统自适应滑模控制策略.首先,根据微分中值定理将非光滑饱和函数转化为仿射形式.其次,设计非线性扩张状态观测器并采用极点配置法确定观测器参数,用于补偿未知摩擦、饱和等非线性项以及外部干扰的影响.然后,通过结合参数自适应律和滑模理论,设计自适应滑模控制器,保证系统输出快速稳定地跟踪期望信号且改善抖振问题.仿真对比结果验证了所提方法的优越性.     

Evolutionary structural optimisation using an additive algorithm

The evolutionary structural optimisation (ESO) method has been under continuous development since 1992. Traditionally, the method was conceived from the engineering perspective that the topology and shape of structures were naturally conservative for safety reasons and therefore contained an excess of material. To move from the conservative design to a more optimum design would therefore involve the removal of material. Thus the ESO method started from a design space much bigger than the optimum and the final topology or shape emerged by a process of removal of unwanted/inefficient/lowly stresses material. The original algorithms allowed for two forms of evolution. One was there the understressed material could be removed from anywhere in the allowable design space, and with compensation for checker-boarding this produces an optimum topology under the prescribed environments. The second form only allows removal from the surface or parts of the surface (called nibbling in the ESO lexicon); this produces a Min–Max situation where the maximum surface stress is reduced to a minimum. It has been demonstrated that the ESO process produces a surface that is an iso-stress contour thus satisfying the Min–Max optimality criterion. The present paper addresses the opposite evolutionary process whereby the structure evolves from a base which is the minimum structural form required to carry the load regardless of the magnitude of the stress levels. Material is added in the proximity of high stress to ameliorate its effect and hence the final structural form emerges. Only singly connected regions are formed in the present analysis and thus the additive ESO process is the opposite of the nibbling SO, mentioned above, that produces optimum surface shapes. The paper presents a brief background to the current state of structural optimisation research. This is followed by a discussion of the strategies for the additive ESO (AESO) algorithm and two examples are presented.     

Valuing executive stock options: A quadratic approximation

This paper develops a continuous-time model for valuing executive stock options (ESOs) with features of early exercise, delayed vesting and forfeiture. Applying the quadratic approximation established for valuing American options into ESOs, we obtain an explicit formula for the fair ESO value at its grant date. We show that the approximation formula is consistent with the exact results for two special cases either with no dividend or infinite maturity, and also that the perpetual value for the latter case gives an upper bound of the ESO value. To see the performance of the formula, we numerically examine it with benchmark results generated by a binomial-tree model for some particular cases. Numerical experiments show that there is a complementary relation between the vesting and trading periods with respect to exit rate of ESO holders.     

Seleção de topologias ótimas de estruturas elásticas 2D com restrição de tensão – via Smooth Evolutionary Structural Optimization

This paper approaches the topology optimization problems in plane linear elasticity considering the minimization of the volume with restriction of the stress employing an index of performance for monitoring the meeting of the optimum region. It is used for this purpose the classical evolutionary structural optimization, or ESO ‐ evolutionary structural optimization. This procedure is based on systematic and gradual removal of the elements with lower stress compared with the maximum stress of the structure. This procedure also known as a process “hard‐kill”. It is proposed a variant of the ESO method, called SESO ‐ Smoothing ESO, which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it has no longer influence in the structure, so its removal is performed smoothly. That is, their removal is done smoothly, reducing the values of the constitutive matrix of the element as if it were in the process of damage. A new performance index for the monitoring of this evolutionary process smoothed is proposed herein. The applications of ESO and SESO are made with the finite element method, but considering a high order triangular element based on the free formulation. Finally, it is implemented a spatial filter in terms of stress control, which was associated with SESO technique proved to be very stable and efficient in eliminating the formation of the checkerboard.     

Capturing k-ary existential second order logic with k-ary inclusion–exclusion logic

In this paper we analyze k-ary inclusion–exclusion logic, INEX[k], which is obtained by extending first order logic with k-ary inclusion and exclusion atoms. We show that every formula of INEX[k] can be expressed with a formula of k-ary existential second order logic, ESO[k]. Conversely, every formula of ESO[k] with at most k-ary free relation variables can be expressed with a formula of INEX[k]. From this it follows that, on the level of sentences, INEX[k] captures the expressive power of ESO[k].We also introduce several useful operators that can be expressed in INEX[k]. In particular, we define inclusion and exclusion quantifiers and so-called term value preserving disjunction which is essential for the proofs of the main results in this paper. Furthermore, we present a novel method of relativization for team semantics and analyze the duality of inclusion and exclusion atoms.     

The expressive power of k-ary exclusion logic

In this paper we study the expressive power of k-ary exclusion logic, EXC[k], that is obtained by extending first order logic with k-ary exclusion atoms. It is known that without arity bounds exclusion logic is equivalent with dependence logic. By observing the translations, we see that the expressive power of EXC[k] lies in between k-ary and ($k+1$)-ary dependence logics. We will show that, at least in the case when $k=1$, both of these inclusions are proper.In a recent work by the author it was shown that k-ary inclusion-exclusion logic is equivalent with k-ary existential second order logic, ESO[k]. We will show that, on the level of sentences, it is possible to simulate inclusion atoms with exclusion atoms, and in this way express ESO[k]-sentences by using only k-ary exclusion atoms. For this translation we also need to introduce a novel method for “unifying” the values of certain variables in a team. As a consequence, EXC[k] captures ESO[k] on the level of sentences, and we obtain a strict arity hierarchy for exclusion logic. It also follows that k-ary inclusion logic is strictly weaker than EXC[k].Finally we use similar techniques to formulate a translation from ESO[k] to k-ary inclusion logic with an alternative strict semantics. Consequently, for any arity fragment of inclusion logic, strict semantics is strictly more expressive than lax semantics.     

GDM在经理人股票期权计划中的应用

在实施ESO计划所需股票总数确定的前提下,根据每个受益人股票期权授予数量受多因素影响的特点,用群体决策方法进行了研究,确定了每个受益人所得股票期权的数量.     

Does stock return predictability affect ESO fair value?

Executive Stock Options (ESOs) are modified American options that cannot be valued using standard methods. With a few exceptions, the literature has discussed the ESO fair value by assuming unpredictable stock returns which are not supported by the available empirical evidence. In this paper we obtain the fair value of American ESOs when stock returns are predictable and, specifically, driven by the trending Ornstein–Uhlenbeck process of Lo and Wang (1995). We solve the executive’s portfolio allocation problem for a simple buy-and-hold strategy when his wealth can be distributed between a risk-free asset and a market portfolio. This problem is jointly solved with the executive’s optimal exercise policy. We find that executives tend to wait longer the higher the predictability, independently of the composition of executive’s asset menu. We have also analyzed the implications under the FAS123R proposals for the ESO fair value and found that, even for low autocorrelations, there is a meaningful mispricing when unpredictable returns are erroneously assumed.     

On various aspects of evolutionary structural optimization for problems with stiffness constraints

An evolutionary structural optimization (ESO) method for problems with stiffness constraints which is capable of performing simultaneous shape and topology optimization has been recently presented. This paper discusses various aspects of this method such as influences of the element removal ratio, the mesh size and the element type on optimal designs.     

Valuation of employee stock options using the exercise multiple approach and life tables

Employee stock options (ESOs) are common in performance-based employee remuneration. Financial reporting standards such as IFRS2 and AASB2 require public corporations to report on the cost of providing ESOs, and mandate the incorporation of voluntary and involuntary early exercise. In this paper we extend the exercise multiple approach of Hull and White (2004) and decompose the attrition unadjusted voluntary exercise ESO into a gap call option and two partial-time barrier options. We use exit probabilities obtained from empirically determined multiple decrement or life tables to model involuntary early exercise or forfeiture. We provide a new analytic valuation formula which expresses the ESO value in terms of a portfolio of exotic European bivariate power options and which correctly accounts for both voluntary exercise and employee attrition. Recent approaches seek to model employee attrition using a constant hazard rate. Our approach uses an empirically driven actuarial method for incorporating employee attrition in the valuation.