Probabilistic criteria of structural stochastic optimal controls

A family of probabilistic criteria for stochastic optimal controls is developed in the context of physical stochastic optimal control scheme of structures. A physical form of the control policy is firstly conducted in conjunction with the classical optimal control theory, specifically, an LQR control. In order to obtain the optimal weighting matrices included in the control gain, two classes of probabilistic criteria, in accordance with the objective structural performance, are then proposed, including single-objective criteria, of which the statistics and tail details of probability density of equivalent extreme-value vectors of interest are involved, and multi-objective criteria, of which the ensemble-expectation and exceedance probability of equivalent extreme-value processes in the sense of performance and energy trade-off are involved. A linear single-degree-of-freedom structural system subjected to random ground motion is investigated for illustrative purpose. The results indicate that the effectiveness of response control hinges on the physical meanings of the probabilistic criteria. The implementation of control criteria related to the exceedance probability with multiple constraints results in a more economic and more accurate control effectiveness than that of control criteria related to statistics with single constraint. The exceedance probability criterion in energy trade-off sense accommodates system performance to a better trade-off between response reductions and control requirements, which is also included in the comparative study against other control criteria currently in use. The former, meanwhile, provides accurate reliabilities of system quantities simultaneously, while other control criteria fail to do so. It is thus the primary criterion of structural performance controls. Following that, a randomly base-excited eight-storey shear frame controlled by active tendons is investigated as a numerical example. The numerical results reveal that using the advocated probabilistic criterion, the structural stochastic optimal control operates efficiently and achieves a desirable objective performance.     

Semi-active control of wind excited building structures using MR/ER dampers

A semi-active control strategy for building structures subject to wind loading and controlled by MR/ER dampers is proposed. The power spectral density (PSD) matrix of the fluctuating part of wind velocity vector is diagonalized in the eigenvector space. Each element of the diagonalized PSD matrix is modeled as a set of second-order linear filter driven by white noise. A Bingham model for MR/ER dampers is used. The forces produced by MR/ER dampers are split into passive and active parts and the passive part is combined with structural damping forces. A set of partially averaged Itô equations for controlled modal energies are derived by applying the stochastic averaging method for quasi-integrable-Hamiltonian systems. The optimal control law is then determined by using the stochastic dynamical programming principle and the cost function is so selected that the optimal control law can be implemented by the MR/ER dampers. The response of semi-active controlled structures is predicted by using the reduced Fokker–Planck–Kolmogorov equation associated with fully averaged Itô equations of the controlled structures. A comparison with clipped linear quadratic Gaussian (LQG) control strategy, for an example, shows that the proposed semi-active control strategy for MR/ER dampers is superior to clipped LQG control strategy.     

Minimax optimal control of uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback

A minimax optimal control strategy for uncertain quasi-integrable Hamiltonian systems with time-delayed bounded feedback control is proposed. First, a quasi-integrable Hamiltonian system with time-delayed bounded control forces and uncertain excitation and system parameters is converted into a set of Itô stochastic differential equations without time delay. Then, the partially averaged Itô stochastic differential equations for the energy processes are derived by using the stochastic averaging method for quasi-integrable Hamiltonian systems. For these equations together with an appropriate performance index, a worst-case optimal control strategy is derived via solving a stochastic differential game problem. The worst-case disturbances and the optimal bounded controls are obtained by solving a Hamilton–Jacobi–Isaacs (HJI) equation. Finally, two examples are worked out in detail to illustrate the application and effectiveness of the proposed method.     

Nonlinear stochastic optimal control of Preisach hysteretic systems

The nonlinear stochastic optimal control of Preisach hysteretic systems is studied, and the control procedure is illustrated with an example of the single-degree-of-freedom Preisach system. The Preisach hysteretic system subjected to a stochastic excitation is first replaced by an equivalent non-hysteretic nonlinear stochastic system with displacement-amplitude-dependent damping and stiffness, by using the generalized harmonic balance technique. Then, the relationship between the displacement amplitude and total system energy is established, and the equivalent damping and stiffness coefficients are expressed as functions of the system energy. The averaged Itô stochastic differential equation for the system energy as one-dimensional controlled diffusion process, is derived by using the stochastic averaging method of energy envelope. For the semi-infinite time-interval ergodic control, the dynamical programming equation is obtained based on the stochastic dynamical programming principle, and is solved to yield the optimal control force. Finally, the Fokker–Planck–Kolmogorov equation associated with the averaged Itô equation is established, and the stationary probability density of the system energy is obtained, from which the variances of the controlled system response and the optimal control force are predicted and the control efficacy is evaluated. Numerical results show that the proposed control strategy for Preisach hysteretic systems is very effective and efficient.     

Phase synchronization of switchings in stochastic and chaotic bistable systems

We study synchronization of switching processes in stochastic and chaotic bistable systems driven by a periodic signal in terms of phase synchronization. By introduction of instantaneous phases of transitions between metastable states and of the periodic forcing we show explicitly the effect of phase locking. The dynamics of phase difference appears to be qualitatively equivalent to that of a synchronized classical self-sustained oscillator. We have found that the degree of phase coherence between the input signal and the response estimated employing the effective diffusion constant is maximal at an optimal noise level in a stochastic bistable system or at an optimal value of a control parameter in a purely deterministic case. We also consider the effect of mutual synchronization of the switching processes in coupled stochastic and chaotic bistable systems.     

宽带激励下非线性振动系统响应的最小化控制

将一种基于广义谐和函数的随机平均法和随机动态规划原理相结合，提出了一种非线性随机最优控制方法，可以为受宽带激励的单自由度强非线性振动系统设计最优控制规律，以使得系统的稳态响应最小化。方法中的随机平均法用来得到受控系统位移幅值的Ito随机微分方程；用随机动态规划原理为系统稳态响应最小化建立动态规划方程；在控制力为有界的条件下，从动态规划方程中可以导出最优控制规律；通过求解FPK方程得到受控系统的响应。本文用一个具体的例子阐述了这一控制方法的实施过程。     

基于非线性随机最优控制的大跨度斜拉桥地震响应Benchmark问题研究

大跨度斜拉桥Benchmark问题的振动控制研究是当前国际结构控制研讨会的重要议题之一。以美国Bill Emerson Memorial斜拉桥第二阶段Benchmark模型为研究对象,在非线性随机动力学与控制的拟哈密顿理论体系框架下,运用基于随机平均法和随机动态规划原理的非线性随机最优（NSO）控制策略,对地震作用下的Benchmark模型进行MATLAB仿真分析。将最优控制力和性能评价指标与线性二次型Gauss（LQG）控制的计算结果进行对比,得出非线性随机最优控制策略能够更加有效地抑制斜拉桥的地震响应,提高结构的动力稳定性和抗震能力,具有更好的控制效果,对实际桥梁工程的振动控制具有较强的指导意义和适用价值。     

高速列车悬挂系统连续混合控制策略的设计与仿真分析

在传统开关型半主动控制方法的基础上,提出了一种线性连续型天棚-加速度阻尼(skyhook-acceleration driven damping,SH-ADD)控制算法,用于提高半主动悬架在中高频区域内的控制效果。以国内某型高速动车组列车为原型,建立了两自由度1/4车横向动力学模型,用于设计改进的线性连续型SH-ADD半主动控制策略。以乘坐舒适性为控制目标,分别采用单频谐波激励和宽频轨道不平顺激励进行仿真,对不同控制策略作用下的系统振动特性进行对比分析。采用整车模型对控制算法进行了舒适度验证。研究结果表明:相比改进前的控制策略,新型半主动控制策略在高频范围内的振动控制效果更好。     

Stochastic response determination of nonlinear structural systems with singular diffusion matrices: A Wiener path integral variational formulation with constraints

The Wiener path integral (WPI) approximate semi-analytical technique for determining the joint response probability density function (PDF) of stochastically excited nonlinear oscillators is generalized herein to account for systems with singular diffusion matrices. Indicative examples include (but are not limited to) systems with only some of their degrees-of-freedom excited, hysteresis modeling via additional auxiliary state equations, and energy harvesters with coupled electro-mechanical equations. In general, the governing equations of motion of the above systems can be cast as a set of underdetermined stochastic differential equations coupled with a set of deterministic ordinary differential equations. The latter, which can be of arbitrary form, are construed herein as constraints on the motion of the system driven by the stochastic excitation. Next, employing a semi-classical approximation treatment for the WPI yields a deterministic constrained variational problem to be solved numerically for determining the most probable path; and thus, for evaluating the system joint response PDF in a computationally efficient manner. This is done in conjunction with a Rayleigh-Ritz approach coupled with appropriate optimization algorithms. Several numerical examples pertaining to both linear and nonlinear constraint equations are considered, including various multi-degree-of-freedom systems, a linear oscillator under earthquake excitation and a nonlinear oscillator exhibiting hysteresis following the Bouc–Wen formalism. Comparisons with relevant Monte Carlo simulation data demonstrate a relatively high degree of accuracy.     

结构随机振动混合控制整体优化

建立了与随机振动主动控制等价的随机振动混合控制系统方程,选取混合控制中主动外加能源指标函数最小为优化目标,实现了在物理空间的随机振动混合控制整体优化;在对结构模态参数灵敏度分析的基础上,给出了模态空间内实现混合控制整体优化的方法。对受零均值平稳白噪声激励的建筑结构实施混合控制整体优化方案,计算结果表明当被动控制部分合理设计时,主动控制部分所需外加能源指标将显著下降。     

受宽带随机激励的最优反馈控制

文章提出了基于平稳响应的随机最优反馈控制策略，研究了受宽带随机激励的Hamilton系统的最优反馈控制。首先应用拟Hamilton系统随机平均法建立平均后的Ito随机微分方程；接着求解相应的Fokker—Plank—Kol—mogorov（FPK）方程得到系统精确平稳解；然后将控制力的均方值和Hamilton能量均值结合作为系统控制指标，得出最优反馈控制规律；最后用两个例子详细说明了这一控制方法的实施过程的有效性，数字模拟结果与理论结果完全吻合。     

基于单边饱和拉索的结构振动最优控制

拉索作动器已被引入结构振动控制中,但作动器具有单边饱和的非线性特点,因此,拉索控制难以直接采用要求控制无约束的LQG对结构振动进行控制律设计。针对拉索的单边饱和特点,提出了一种用分段性能指标代替原有的二次型性能指标的最优控制方法。将拉索的单边饱和特性考虑在内并以合适的抑制速度对结构振动进行控制。并证明了直接用LQG方法对结构振动进行控制律设计,通过拉索控制器取单边和饱和之后作用于结构也是一种最优控制。同时证明了加观测器的最优控制系统对结构振动控制是稳定的。对一悬臂梁振动进行数值仿真控制,结果验证了该最优控制率的可行性。     

Compromise design of stochastic dynamical systems: A reliability-based approach

This paper presents a procedure for obtaining compromise designs of structural systems under stochastic excitation. In particular, an effective strategy for determining specific Pareto optimal solutions is implemented. The design goals are defined in terms of deterministic performance functions and/or performance functions involving reliability measures. The associated reliability problems are characterized by means of a large number of uncertain parameters (hundreds or thousands). The designs are obtained by formulating a compromise programming problem which is solved by a first-order interior point algorithm. The sensitivity information required by the proposed solution strategy is estimated by an approach that combines an advanced simulation technique with local approximations of some of the quantities associated with structural performance. An efficient Pareto sensitivity analysis with respect to the design variables becomes possible with the proposed formulation. Such information is used for decision making and tradeoff analysis. Numerical validations show that only a moderate number of stochastic analyses (reliability estimations) has to be performed in order to find compromise designs. Two example problems are presented to illustrate the effectiveness of the proposed approach.     

Linear random vibration by stochastic reduced‐order models

A practical method is developed for calculating statistics of the states of linear dynamic systems with deterministic properties subjected to non‐Gaussian noise and systems with uncertain properties subjected to Gaussian and non‐Gaussian noise. These classes of problems are relevant as most systems have uncertain properties, physical noise is rarely Gaussian, and the classical theory of linear random vibration applies to deterministic systems and can only deliver the first two moments of a system state if the noise is non‐Gaussian. The method (1) is based on approximate representations of all or some of the random elements in the definition of linear random vibration problems by stochastic reduced‐order models (SROMs), that is, simple random elements having a finite number of outcomes of unequal probabilities, (2) can be used to calculate statistics of a system state beyond its first two moments, and (3) establishes bounds on the discrepancy between exact and SROM‐based solutions of linear random vibration problems. The implementation of the method has required to integrate existing and new numerical algorithms. Examples are presented to illustrate the application of the proposed method and assess its accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

Direct control method for improving stability and reliability of nonlinear stochastic dynamical systems

Optimal control for improving the stability and reliability of nonlinear stochastic dynamical systems is of great significance for enhancing system performances. However, it has not been adequately investigated because the evaluation indicators for stability (e.g. maximal Lyapunov exponent) and for reliability (e.g. mean first-passage time) cannot be explicitly expressed as the functions of system states. Here, a unified procedure is established to derive optimal control strategies for improving system stability and reliability, in which a physical intuition-inspired separation technique is adopted to split feedback control forces into conservative components and dissipative components, the stochastic averaging is then utilized to express the evaluation indicators of performances of controlled system, the optimal control strategies are finally derived by minimizing the performance indexes constituted by the sigmoid function of maximal Lyapunov exponent (for stability-based control)/the reciprocal of mean first-passage time (for reliability-based control), and the mean value of quadratic form of control force. The unified procedure converts the original functional extreme problem of optimal control into an extremum value problem of multivariable function which can be solved by optimization algorithms. A numerical example is worked out to illustrate the efficacy of the optimal control strategies for enhancing system performance.     

Unconstrained Minimization Approaches to Control Determination of a Spreadable System

The optimization of processes described by non-linear distributed parameter systems is investigated. In general, the complexity of such a problem prevents the implementation of classical deterministic optimization methods. In fact, for global optimization problems as well as for problems involving very large number of variables for which the reduction of the computational time is crucial, these methods can be inefficient. In this framework, several minimization algorithms (deterministic, stochastic, evolutionary) are compared. An application to optimal control determination is presented for a class of distributed parameter systems called spreadable systems.     

车辆非平稳行驶状态下的半主动悬架控制

在建立车辆非平稳行驶路面激励模型基础上,应用LQG最优控制对车辆非平稳行驶状态下的悬架进行了半主动控制研究,并与平稳行驶状态下的控制进行比较,分析了悬架性能指标的权重系数对悬架性能的影响。研究表明:车辆在加速行驶时,半主动悬架的车身加速度、车轮动载荷与无控制时相比有一定减小,悬架动挠度在非平稳态行驶状态下的控制响应与无控制时相比有明显的滞后。     

随机最优控制下矩形薄板受面内随机参数激励的首次穿越研究

根据建立了四边简支受控矩形薄板受面内高斯白噪声激励的振动模型,并用Galerkin变分法将其化简为二自由度常微分非线性动力学方程。又利用拟不可积Hamilton系统平均理论将方程等价为一个一维的Ito随机过程,随后结合随机动态规划方法,得到了使系统可靠性最大的随机最优控制策略。最后建立了受控系统的条件可靠性函数所满足的Backward Kolmogorov(BK)方程,根据初始条件和边界条件得出数值结果。数值结果表明,随机最优控制对系统的可靠性提升有明显作用     

股票价格服从跳跃-扩散过程套期保值问题的随机LQ框架

在连续时间金融模型中,一般认为股票(风险资产)的价格的随机扰动为标准的Brown运动,然而在现实中,当有重大信息出现时会对股票的价格产生冲击,使其呈现不连续的跳跃,即股票价格表现为一种跳跃-扩散过程,文中将随机LQ控制模型推广到系统状态的跳-扩过程的随机LQ控制,通过引入跳-扩的Riccati方程而得到最优的反馈控制,然后,运用该框架去处理金融中未定权益的套期保值问题,得到了最优套期保值策略。