A method for parameter estimation of a class of non-linear distributed systems under noisy observations

A method is presented for estimating an unknown parameter of a distributed parameter system which depends on the system state. The system considered is modelled by a class of non-linear partial differential equations of a parabolic type. Noisy observations are assumed to be taken through an arbitrary number of sensors allocated on the spatial region. First, the explicit form of the stationary solution of the state equation is discussed. Second, use is made of the maximum likelihood approach to obtain the optimal estimate of the unknown parameter. Consistency properties w.p.1 of the optimal estimate obtained are also shown. Finally, results of digital simulation experiments are included to support the theoretical aspects.     

Adaptive open loop control for a class of distributed parameter systems

The real time implementation of closed loop control laws obtained by the minimization of a quadratic criterion with finite time intervals is a difficult problem, if we consider computing time and the eventual determination of the state variables. It would seem useful therefore to define, for complex systems such as distributed parameter systems, a control type which unites the advantages of open loop control, with its relative simplicity of implementation, and those of closed loop control, with its ability to reduce perturbations. It is for this reason that we use the principle of adaptive open loop control. In this paper, we describe the principles of such a method and results obtained from one example studied by numerical computation or hybrid simulation in real time.     

Finite difference implementation of distributed parameter filters

The numerical problems associated with the full three-dimensional (spatial) distributed parameter filters demand a novel technique that maintains accuracy and efficiency for the complex partial differential Riccati equation governing the filter variance. The filters can be successfully implemented by using numerical operators that can, in various ways, be factored. Use of the finite difference method having this property increases the accuracy and efficiency of the implementation.

This paper outlines a time-splitting technique for the numerical implementation of distributed parameter filters. The paper provides rationales to the square root and U-D factorizations of the distributed parameter filter by means of the time-splitting finite difference method.     

基于防碰撞的移动SAN 对分布参数系统的控制

在考虑移动执行器动力学行为和防碰撞的条件下,研究在控制分布参数系统过程中执行器的移动策略。移动传感-执行器网络中的传感器对分布参数系统进行测量,由执行器对空间分布过程执行相应的控制信号以控制分布参数系统。基于Lyapunov稳定性理论,对每个执行器设计一种新的移动策略,实现每个移动执行器对分布参数系统的协同控制,并保证各移动执行器之间不会发生碰撞。仿真实例表明了所提出方法的有效性。     

Parameter identification for a class of abstract nonlinear parabolic distributed parameter systems

This paper studies the parameter identification problem of nonlinear abstract parabolic distributed parameter systems via variational method ^[1]. Based on the fundamental optimal control theory and the transposition method studied in ^[2], the existence of optimal parameter is proved, and the necessary condition for the optimal parameter is established.     

H∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

This paper addresses the problem of H_∞ boundary control for a class of nonlinear stochastic distributed parameter systems expressed by parabolic stochastic partial differential equations (SPDEs) of Itô type. A simple but effective H_∞ boundary static output feedback (SOF) control scheme with collocated boundary measurement is introduced to ensure the local exponential stability in the mean square sense with an H_∞ performance. By using the semigroup theory, the disturbance‐free closed‐loop well‐posedness analysis is first given. Then, based on the SPDE model, a general linear matrix inequality based H_∞ boundary SOF control design is provided via Lyapunov technique and infinite‐dimensional infinitesimal operator, such that the disturbance‐free closed‐loop system is locally exponentially stable in the mean square sense and the H_∞ performance of disturbance attenuation can also be achieved in the presence of disturbances. Finally, simulation results on a stochastic Fisher‐Kolmogorov‐Petrovsky‐Piscounov equation illustrate the effectiveness of the proposed method.     

基于分布参数模型的高炉炉温预测

高炉炉温的高低直接影响了高炉的生产稳定性以及炼铁过程中的低能耗、高质量等性能指标.本文采用分布参数系统思想建立了偏微分方程预测控制模型.首先,使用普通最小二乘方法分两种不同方案对模型进行参数估计并对他们的仿真结果进行比较分析,然后,使用易适应的参数估计方法(flexibleleast squaremethod,FLS)对模型进行变系数估计处理.此方法能够体现系数的时变性,提高模型的准确度.最后,从仿真效果图中展示了本方法对硅含量预报的结果无论在命中率上还是精确度上的优势和有效性.     

Selection of outputs for distributed parameter systems by identifiability analysis in the time-scale domain

A method of sensor location selection is introduced for distributed parameter systems. In this method, the sensitivities of spatial outputs to model parameters are computed by a model and transformed via continuous wavelet transforms into the time-scale domain to characterise the shape attributes of output sensitivities and accentuate their differences. Regions are then sought in the time-scale plane wherein the wavelet coefficient of an output sensitivity surpasses all the others’ as indication of the output sensitivity’s distinctness. This yields a comprehensive account of identifiability each output provides to the model parameters as the basis of output selection. The proposed output selection strategy is demonstrated for a numerical case of pollutant dispersion by advection and diffusion in a two-dimensional area.     

Trajectory planning for quasilinear parabolic distributed parameter systems based on finite-difference semi-discretisations

In this contribution, a flatness-based approach is considered for the solution of the trajectory planning problem for quasilinear parabolic distributed parameter systems (DPS) by making use of finite-difference semi-discretisations. It is shown that the method yields solutions which are equivalent to results known from the infinite-dimensional trajectory planning for a certain class of quasi-linear parabolic DPS. Furthermore, the methodology being proposed can also be applied to systems with general analytic nonlinearities. As analytical convergence results are not available in this case, a numerical test criterion for the convergence behaviour is suggested.     

A self-tuning regulator for distributed parameter systems

The control of distributed parameter systems with constant, but unknown parameters is considered. A weighted average of the distributed output on the spatial domain is defined as a new variable and is used to generate the control. The parameters of the model are estimated using recursive least squares estimation. The control is obtained using a minimum variance strategy based on the estimated parameters. Distributed disturbances and measurement noise are allowed to be present. Measurements at a finite number of points in the spatial domain are used in obtaining a discrete-time model. From the simulation of a one-sided heating diffusion process the self-tuning regulator is shown to have attractive characteristics and hence can be recommended for practical on-line control of distributed parameter systems.     

Some recent applications of distributed parameter systems theory—A survey

A survey of some recent applications of distributed parameter systems theory is presented. The practical areas discussed range from process control problems in an industrial plant to the identification, monitoring and control of air and water quality in our environment. Some new, promising areas of application are discussed along with suggestions for future research emphasis.     

Modal consensus observers for distributed parameter systems

This article proposes a new type of a consensus protocol for the synchronization of distributed observers in systems governed by parabolic partial differential equations. Addressing the goal of sharing useful information among distributed observers, it delves into the details governing the modal decompositions of distributed parameter systems. Assuming that two different groups of sensors are available to provide process information to the two distributed observers, the proposed modal consensus design ensures that only useful information is transmitted to the requisite modal components of each of the observers. Without any consensus protocol, the observers capture different frequency content of the spatial process in differing degrees, as it relates to the concept of modal observability. Their modal components exhibit different learning behavior toward the process state. In the extreme case, it turns out that certain modal components of the distributed observers occasionally behave as naïve observers. To ensure that, both collectively and modal componentwise, the observers agree both with the process state and with each other, a modal component consensus protocol is proposed. Such a consensus protocol is mono-directional and provides only useful information necessary to the appropriate modal component of the distributed filters that behaves as a naïve modal observer. This protocol, when abstracted and applied to different state decompositions can be viewed as mono-directional projections of information transmitted and received by the participating distributed observers. Detailed numerical studies of advection PDE in one and two spatial dimensions are included to elucidate the details of the proposed modal consensus observers.     

Consistent recursive parameter estimation of partial differential equation models

This article introduces a new residual-based recursive parameter estimation algorithm for linear partial differential equations. The main idea is to replace the unmeasurable noise variables by estimates of the noise and to compute recursively both the model parameters and the noise estimates. It is proven that under some mild assumptions the estimated parameters converge to the true values with probability one. Numerical examples that demonstrate the effectiveness of the proposed approach are also provided.     

退化高阶抛物型分布参数系统的迭代学习控制

研究一类高阶分布参数系统的迭代学习控制问题,该类系统由退化高阶抛物型偏微分方程构成.根据系统所满足的性质,基于P型学习算法构建得到迭代学习控制器.利用压缩映射原理,证明该算法能使得系统的输出跟踪误差于L~2空间内沿迭代轴方向收敛于零.最后,仿真算例验证了算法的有效性.     

Necessary and sufficient conditions for strong stability of distributed parameter systems

In this paper we propose definitions for strong stabilizability and strong detectability of infinite-dimensional control systems. We show that these definitions are appropriate by showing that they can be used to give necessary and sufficient conditions for the strong stability of a system in terms of its input-output stability. As an application, we discuss the strong stability of the feedback interconnection of two strongly stabilizable and strongly detectable systems.     

基于Haar函数的分布参数系统辨识

借助于正交函数序列对分布参数系统进行辨识，是一种妊之有效的辨识方法。     

Well-posed problem of nonlinear singular distributed parameter systems and nonlinear GE-semigroup

According to the well-posed problem of nonlinear singular distributed parameter systems, first of all, the nonlinear GE-semigroup induced by a continuous （possibly nonlinear） operator is introduced in Banach space, which is a generalization of GE-semigroup （i.e., generalized operator semigroup）, and the properties of nonlinear GE-semigroup are discussed; and then the existence, uniqueness and constructive expression for the strong solution of nonlinear singular distributed parameter system are discussed by nonlinear GE-semigroup; at last, the exponential stability of nonlinear singular distributed parameter system is studied by using nonlinear GE-semigroup, functional analysis and operator theory in Banach space.