Investment and innovation over the long wave

The purpose of this paper is to provide a simple explanation of investment and innovation patterns over long wave. The analysis has two elements. One is the recognition that when innovations are embodied in new capital equipment, rapid innovation would be a natural consequence of the faster rates of investment required to generate an upturn in the pace of economic activity and slower innovation would be an equally natural consequence of the slower rates of investment bringing about the downturn. The second element is Moss's previous demonstration (in Markets and Macroeconomics) that rational economic and business behaviour ensures that plant and equipment will not generally be transfered amongst very different activities in the short run and, in addition, that some, but not all, capital goods (such as machine tools) will be required directly and indirectly in the production of all commodities. This macroeconomic structure gives rise to multiplier-accelerator interactions and financial flows that explain the investment and, hence, the innovation patterns found over long waves. In consequence, the scope for science and technology policy as a macroeconomic policy instrument is more restricted than has been suggested by analyses predicated on the existence of technological trajectories suggested and paradigms.     

An improvement of QFT plant template generation for systems with affinely dependent parametric uncertainties

Quantitative feedback theory (QFT) is a powerful design technique for robust feedback control systems with plant uncertainties. In applying QFT to design robust feedback control systems, the generation of plant templates is an essential step. For a system with affinely dependent parameters and the parameter domain is a box, it is well known that the boundary of a plant template is included in the image of the set of edges of the parameter domain box. One can obtain the plant template from the image of the set of edges. However, this approach to the generation of the plant template leads to heavy computational burden since it wastes much computational effort computing the images of points on edges which lie in the interior of the plant template. In this paper, an efficient algorithm is proposed to identify, from an edge of the parameter domain box, the set of parameter points whose image lies in the interior of the plant template. The computational burden for generating the plant template thus can be obviously reduced by eliminating the identified sets of parameter points in the plant template generation procedure. Numerical examples are included to illustrate the efficiency of the algorithm.     

Bifurcation with regard to combined interaction parameter in a life energy system dynamic model of two components with multiple delays

Bifurcation theory is commonly used to study the dynamical behavior of ecosystems. It involves the analysis of points in the parameter space where the stability of the system changes qualitatively. The type of bifurcation that associates equilibria with periodic solution is called Hopf bifurcation. In this paper, a life energy system dynamic model of two components with multiple delays is presented. It is shown that the interaction parameters of the delayed ecosystem play a fundamental role in classifying the rich dynamics and bifurcation phenomena. Regarding the combined interaction parameter as a bifurcation parameter, the bifurcation values in the parameter plane are displayed. The direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by applying the normal form theory and the center manifold theorem. Moreover, the amplitudes of oscillations always increase as the interaction parameters increase, while the robustness of periods occurs as the interaction parameters vary. From an ecological point of view, the existence of Hopf bifurcation expresses periodic oscillatory behavior of the life energy system.     

基于逆系统的变轨迹迭代学习控制

针对一类未知非线性时变系统,本文提出一种不同次迭代运行过程中期望轨迹可变的迭代学习控制算法。该算法利用高斯径向基网络逼近系统逆的未知参数,并采用迭代学习的方式修正网络逼近的系数,然后结合变结构技术设计控制律。收敛性分析表明,随着迭代次数的增加,逼近系数与最佳系数的差异逐渐减小。最后,在机械臂上的仿真验证了算法的有效性。     

Further analysis of global robust stability of neural networks with multiple time delays

This paper deals with the problem of the global robust asymptotic stability of the class of dynamical neural networks with multiple time delays. We propose a new alternative sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point under parameter uncertainties of the neural system. We first prove the existence and uniqueness of the equilibrium point by using the Homomorphic mapping theorem. Then, by employing a new Lyapunov functional, the Lyapunov stability theorem is used to establish the sufficient condition for the asymptotic stability of the equilibrium point. The obtained condition is independent of time delays and relies on the network parameters of the neural system only. Therefore, the equilibrium and stability properties of the delayed neural network can be easily checked. We also make a detailed comparison between our result and the previous corresponding results derived in the previous literature. This comparison proves that our result is new and improves some of the previously reported robust stability results. Some illustrative numerical examples are given to show the applicability and advantages of our result.     

迭代学习控制系统的鲁棒性分析

讨论了在偏离，状态输出扰动和非线性扰动同时存在的干扰环境中运行的迭代学习控制系统的鲁棒性问题。通过更精确的误差渐近界估计，结合迭代学习控制算法中的开环和闭环方案，给出了算法的鲁棒性条件，以及算法收敛性所要求的渐近干扰条件。     

Reachability and stabilization of scheduled steady-states for LPV single-input systems

The aim of this work is characterizing the class of LPV systems that admit steady-state trajectories depending exclusively on the scheduling parameter. In particular, it will be shown that only certain parameter dependent steady-state profiles are admissible and can be reached by means of a suitable control input. Furthermore, the asymptotic stability and the stabilization of such steady-states is investigated using Lyapunov-based techniques. Extensive numerical simulations illustrate and corroborate the theoretical results.     

Robust synchronization of uncertain Markovian jump complex dynamical networks with time-varying delays and reaction–diffusion terms via sampled-data control

This paper is concerned with the problem of robust synchronization of a class of complex dynamical networks with time-varying delays and reaction–diffusion terms. To reflect most of the dynamical behaviors of the system, the parameter uncertainties are considered. A sampled-data controller with m stochastically varying sampling periods whose occurrence probabilities are given constants is considered. The control objective is that the trajectories of the system by designing suitable control schemes track the trajectories of the system with sample-data control. It is shown that, through Lyapunov stability theory, the proposed sample-data controllers are successful in ensuring the achievement of robust synchronization of complex dynamical networks even in the case of uncertainity and Markovian jumping parameters. By utilizing the Lyapunov functional method, Jensen’s inequality, Wirtinger’s inequality and lower bounds theorem, we establish a sufficient criterion such that, for all admissible parameter uncertainties, the complex dynamical network is robustly synchronized. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.     

Coordinated decentralized sliding mode MRAC with control cost optimization for a class of nonlinear systems

In this paper, we develop an approach for solving the problem of sliding mode decentralized adaptive state-feedback tracking with continuous control actions for a class of uncertain nonlinear dynamical systems. In addition to the traditional asymptotic zero error tracking specification in the sliding mode decentralized model reference adaptive control (MRAC) problem formulation, here an additional requirement is specified explicitly in the problem statement. The tracking objective is described by a set of admissible reference trajectories, called a performance tube. The input signal to the reference model, selected within specified bounds, is used as a design parameter. The best reference trajectory is found by solving an additional optimization problem whose criterion penalizes the variance of the control signal.     

An adaptive total generalized variational model for speckle reduction in ultrasound images

In this paper, we propose an adaptive total generalized variation model for removing the speckle noise in ultrasound image. The existence and uniqueness of the minimizers to the proposed model are established. Furthermore, we design an alternating minimization algorithm to solve numerically the model. By incorporating the generalized cross validation technique, the regularization parameter of the model can be updated during the iterations of the designed algorithm. Finally, experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with that of the other testing methods.     

Statistical information based two-layer model predictive control with dynamic economy and control performance for non-Gaussian stochastic process

In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method.     

Fault detection of discrete-time T–S fuzzy affine systems based on piecewise Lyapunov functions

This paper investigates the problem of robust fault detection for a class of discrete-time nonlinear systems, which are represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties. The objective is to design an admissible fault detection filter guaranteeing the asymptotic stability of the resulting residual system with prescribed performances. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the fault detection filter implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, the results are formulated in the form of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.     

Robust stable pole-placement-based adaptive control of continuous linear systems with two parametrical estimation schemes

This paper deals with the pole-placement-type robust adaptive control of continuous linear systems in the presence of bounded noise and a common class of unmodeled dynamics provided that two estimation schemes are used in parallel. Both estimation schemes are introduced in order to minimize the plant identification error by selecting, as plant parameter estimates, a convex combination of both parameter estimates which leads to the selection of one of the estimation schemes, via a switching rule, on time intervals of at least a minimum prefixed residence duration. The weights of the individual parameter vector estimates are provided at each time by an optimization or suboptimization scheme for a quadratic loss function of the possibly filtered tracking error and/or control input. The robust stability of the overall adaptive scheme is ensured by an adaptation relative dead zone which takes into account the contribution of the unmodeled dynamics and bounded noise. The basic results are derived for two different estimation strategies which have either a shared regressor with the plant or individual regressors for the input contribution and its contributed derivatives. In this second case, the plant input is obtained from a similar convex combination rule as the one used for the estimators in the first approach. An extension of the basic strategies is also pointed out including a combined use of the (sub) optimization scheme with a supervisor of past measures for the on-line calculation of the estimator weights in the convex combination. Finally, the extension of the scheme for the use of any number of parametrical estimators is focused on.     

An Upper Bound for the Singular Parameter in a Stable,Singularly Perturbed System

This paper considers the problem of stability of a singularly perturbed system and of finding an upper bound for the parameter when the order of the system changes as a result of parameter perturbation. By means of the contraction mapping technique, conditions have been derived for determining explicitly the range of parameter perturbation such that both bounded-input-bounded-output and asymptotic stabilities are insured. In addition, bounds of the state and output of the singularly perturbed system can be found. Two examples are given to illustrate the application and significance of the results.     

贵州亚热带地区特有野生观赏植物的濒危原因及保护对策

根据5a的实地调查和综合前人研究成果,贵州亚热带地区的特有野生观赏植物资源有12科14属14种。对资源种类、分布、濒危原因和群落结构进行了分析,提出了该类植物资源的保护对策。     

渗透胁迫诱导的植物体内信号及相关基因克隆研究--国家重点基础研究发展规划项目建议依据、背景与思路

重点介绍了国际上在渗透胁迫引发的植物信号及其传递途径、渗透胁迫诱导相关基因克隆及转基因研究方面的最新进展,总结了近年来国内在这一领域内开展的工作及取得的成绩,最后讨论了今后研究的策略,提出了适合我国国情的研究思路和内容,并展望了可能取得创新成果的领域和方向。     

一种基于外部密钥和密文反馈的混沌密码新算法

提出了一种基于逐段线性混沌系统的图像加密算法。通过对外部密钥的映射,分别产生系统的初值和参数值,根据图像大小经16个逐段线性映射迭代出足够的密钥流,并引入密文反馈机制,对图像像素值逐个进行多轮异或加密。实验表明,这种加密算法使密文对明文和密钥都充分敏感,加密图像像素值具有类随机均匀分布特性。而且算法具有密钥空间大、加密速度快的优越性。     

A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

This paper presents a stochastic multi-parameters divergence method for online parameter optimization of fractional-order proportional–integral–derivative (PID) controllers. The method is used for auto-tuning without the need for exact mathematical plant model and it is applicable to diverse plant transfer functions. The proposed controller tuning algorithm is capable of adaptively responding to parameter fluctuations and model uncertainties in real systems. Adaptation skill enhances controller performance for real-time applications. Simulations and experimental observations are carried on a prototype helicopter model to confirm the performance improvements obtained by the online auto-tuning of fractional-order PID structure in laboratory conditions.     

Towards alternative trajectories? Reconfigurations in the Dutch electricity regime

In the early 2000s Dutch electricity companies are increasingly investing in technologies that enable them to replace coal with biomass. Replacing large amounts of coal (up to 40%) requires the companies to invest in technological trajectories alternative to the ones they have supported over the past decades. This paper aims to understand why these incumbent firms in the Dutch electricity regime are developing alternatives. The second aim of the paper is to provide a way for assessing the potential of innovations. The paper does so by bringing together insights from literature on (socio-) technical regimes and insights from literature on technological and market niches (strategic niche management). The main conclusion is that both niche processes (at the level of experimenting with alternatives) and changes in the incumbent regime are necessary for understanding the innovation journey of a new technology. A two-by-two matrix is developed that can be used for both analysis and governance purposes.     

Robust adaptive tracking with an additional plant identifier for a class of nonlinear systems

In this paper, we develop two new model reference adaptive control (MRAC) schemes for a class of nonlinear dynamic systems that is robust with respect to an uncertain state (output) dependent nonlinear perturbations and/or external disturbances with unknown bounds. The design is based on a controller parametrization with an adaptive integral action. Two types of adaptive controllers are considered—the state feedback controller with a plant parameter identifier, and the output feedback controller with a linear observer.