圆形区域内鲁棒极点配置优化

本文基于和声搜索算法将精确极点配置的思想推广到圆形区域极点配置,通过对圆域内极点全局优化的方法,实现圆域内鲁棒极点配置问题.首先,基于几何原理描述极点在圆形区域内的位置信息,确定了在圆形区域内动态选择极点的规则,可以保证算法在圆形区域内动态选取极点.然后,对圆形区域内的极点,基于精确鲁棒极点配置的思想,利用和声搜索算法优化摄动或不确定性的谱范数上界,从而得到一组允许闭环系统具有更大摄动或不确定性的极点及相应的状态反馈控制器.最后,通过实例进行仿真,仿真结果表明本文采用和声搜索算法全局优化动态选择极点方法得到的闭环系统具有更好的鲁棒性.     

Pole assignment in a specified circular region using a bilinear transformation onto the unit circle

An algorithm for pole assignment in a specified circular region using a bilinear transformation onto the unit circle is considered, A Lyapunov type test is developed to check that all the eigenvalues of a discrete system are within a circle in the complex plane. Pole placement in a circular region by LQ state feedback design is investigated for discrete linear systems. Perturbation bounds are derived, using bilinear transformation, for all closed-loop poles of a perturbed discrete system to remain inside a specified circular region. Illustrative examples are included     

不确定离散广义系统的D稳定鲁棒控制

研究了具有圆盘区域极点约束的一类不确定离散广义系统的鲁棒控制问题.首先,研 究了控制输入项不含扰动的不确定离散广义系统,提出了广义二次D镇定的概念,基于矩阵不 等式和广义Riccati方程,给出了一种广义二次D镇定器的设计方法,所得到的结论能够实现研 究目标;然后,讨论了控制输入项含有扰动的不确定离散广义系统,在一定的假设条件下,给出 了期望状态反馈增益阵的存在条件及其解析表达式.最后,用数值示例说明所给方法的有效性 及可行性.     

一类不确定线性系统鲁棒状态估计器设计

本文对一类不确定线性系统给出了一种鲁棒状态估计器设计方法，其主要思想是对允许的系统扰动，通过设计估计器参数矩阵使增广系统的极点位于左半复平面指定圆形区内或指定直线的左侧，且稳态误差协方差阵不超过给定上限，给出了鲁棒估计器存在条件及其解的一般表达式。     

Determining dynamic output feedback of the least order for SIMO systems to locate poles in a specified region†

This paper presents a new theory for determining dynamic output feedback of the least order for a linear, time-invariant, controllable and observable SIMO system, such that poles of the closed-loop system lie in a given region Γ in the complex plane. When some poles have prespecified stationary locations in the complex plane, dynamic output feedback is obtained in the sense that the remaining poles lie in Γ This theory applies to continuous-time as well as discrete-time systems. Here a design algorithm for complete system realization and an illustrative example are given.     

A novel global harmony search algorithm for task assignment problem

The objective of task assignment problem (TAP) is to minimize the sum of interprocessor communication and task processing costs for a distributed system which subjects to several resource constraints. We use a novel global harmony search algorithm (NGHS) to solve this problem, and the NGHS algorithm has demonstrated higher efficiency than the improved harmony search algorithm (IHS) on finding the near optimal task assignment. We also devise a new method called normalized penalty function method to tradeo^® the costs and the constraints. A large number of experiments show that our algorithm performs well on finding the near optimal task assignment, and it is a viable approach for the task assignment problem.     

Active probing based Internet service fault management in uncertain and noisy environment

In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, challenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases： fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm （GAPSA）, which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm （FDPSA）, which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic （FPTS）. Simulation results prove the validity and efficiency of our approach.     

Hinfinity fuzzy control design for nonlinear singularly perturbed systems with pole placement constraints: an LMI approach.

This paper considers the problem of designing an H infinity fuzzy controller with pole placement constraints for a class of nonlinear singularly perturbed systems. Based on a linear matrix inequality (LMI) approach, we develop an H infinity fuzzy controller that guarantees 1) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and 2) the closed-loop poles of each local system to be within a pre-specified LMI stability region. In order to alleviate the ill-conditioned LMIs resulting from the interaction of slow and fast dynamic modes, solutions to the problem are given in terms of linear matrix inequalities which are independent of the singular perturbation, epsilon. The proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard, but also to nonstandard singularly perturbed non-linear systems. A numerical example is provided to illustrate the design developed in this paper.     

冷连轧机厚度和张力系统的多变量解耦鲁棒控制

针对冷连轧过程中多变量、强耦合、不确定的厚度和张力系统,提出了基于不变性原理解耦的H∞混合灵敏度鲁棒控制策略.建立了厚度和张力系统的动态耦合模型,应用不变性解耦原理实现了对厚度和张力系统的解耦,以减弱系统的耦合影响,并针对系统动态模型的参数摄动以及外部扰动等不确定性,采用H∞混合灵敏度方法设计了鲁棒控制器以保证系统的鲁...     

一种飞控系统不确定性建模方法及其鲁棒控制

针对大包线飞行控制系统, 本文通过摄动参数标称化及多个工作点的数据拟合, 并将拟合误差视为范数有界不确定性, 给出了一种不确定模型描述方法, 以降低系统描述的保守性. 基于二次稳定性准则, 运用线性矩阵不等式的处理方法, 得到了大包线飞控系统动态输出反馈控制器的存在条件和设计方法. 进一步, 通过合适的匹配摄动参数和求解凸优化问题, 以参数向量2--范数的形式给出了闭环系统保持鲁棒稳定所允许的参数最大摄动范围估计方法, 建立了系统摄动参数与二次稳定性之间的联系. 最后通过对飞行控制系统的算例仿真, 验证了文中方法的有效性和可行性.     

指定圆形极点配置区域输出无偏的输出反馈协方差控制器设计

系统输出无偏性及稳态状态协方差是随机系统的两个重要稳态性能指标，系统的动态品质主要决定于系统的闭环极点位置。本文给出的输出反馈协方差控制器能确保系统：１）闭环极点位于指定圆形配置区域；２）稳态协方差满足既定的上限约束；３）稳态输出无偏。     

Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution

Building business processes by Web services in cloud computing has become the hotspot of service applications. Due to the complexity and uncertainty of business environment, QoS violations of service processes often take place at run-time. To rapidly recover from failures and minimize their impacts on the original execution plan of service processes, dynamic service selection is urgently needed once potential QoS violations are detected. However, existing research works do not fully investigate QoS constraints and inter-service correlations, as well as the breach penalty caused by service adjustment. In this paper, we present a new cooperative coevolutionary approach for dynamic service selection with QoS constraints and inter-service correlations. First, a novel formal model for the dynamic service selection problem with QoS constraints and inter-service correlations is presented. Second, a Double Information based Cooperative Coevolutionary algorithm (DICC) is proposed which uses Potter’s cooperative coevolutionary framework and provides both local and global knowledge for the dynamic service selection optimization. Finally, we develop a prototype system to apply our approach and adopt different test cases to show that our DICC approach performs more effectively and efficiently than existing algorithms.     

A novel global harmony search algorithm for reliability problems

Inspired by the swarm intelligence of particle swarm, a novel global harmony search algorithm (NGHS) is proposed to solve reliability problems in this paper. The proposed algorithm includes two important operations: position updating and genetic mutation with a small probability. The former enables the worst harmony of harmony memory to move to the global best harmony rapidly in each iteration, and the latter can effectively prevent the NGHS from trapping into the local optimum. Based on a large number of experiments, the proposed algorithm has demonstrated stronger capacity of space exploration than most other approaches on solving reliability problems. The results show that the NGHS can be an efficient alternative for solving reliability problems.     

多执行器-传感器网络协作环境监测和治理

多执行器-传感器网络在环境监测、污染治理方面有着得天独厚的优势. 本文以大气污染为例, 讨论了其在搜寻污染源、释放中和剂方面的应用. 采用更简化的质心维诺分割方法使网络中信息不确定性总和达到局部最优, 同时设计了可与其线性叠加或切换的一致性算法, 以实现各执行器任务负载或载荷比的平均分配. 所引入了一致性算法对质心维诺分割所得到的节点配置产生扰动, 能够进一步降低网络中信息的不确定性. 在仿真实例中, 与单纯使用质心维诺分割配置节点位置的方法进行了比较.     

Actuator fault diagnosis: an adaptive observer-based technique

This paper presents a novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. Under the assumption that the system state observer can be designed such that the observation error is strictly positive real (SPR), an adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for the general system to improve robustness. The method is demonstrated through its application to a simulated second-order system     

Performance improvement by optimal reset dynamic output feedback control based on model predictive strategy

This paper proposes a systematic approach for designing a reset dynamic output feedback controller (DOFC). The reset controller is designed in three steps. First, the D-stability approach is used to design the gain matrices of the DOFC for the base system such that the poles of the closed-loop system are placed in a predefined region. This region is chosen based on the control objectives. Moreover, the sufficient conditions for existence of base controller are formulated in terms of linear matrix inequalities. Second, the reset times are derived by applying genetic algorithm to minimize a novel cost function of certain transient performances. Third, an optimization problem based on model prediction strategy is solved for finding the after reset value at each reset time. The proposed method is applied to two different systems: continuous stirred tank reactor and distillation column. The effectiveness of the proposed reset controller is shown by comparing the results with other approaches.     

Orthogonal variant moments features in image analysis

Moments are statistical measures used to obtain relevant information about a certain object under study (e.g., signals, images or waveforms), e.g., to describe the shape of an object to be recognized by a pattern recognition system. Invariant moments (e.g., the Hu invariant set) are a special kind of these statistical measures designed to remain constant after some transformations, such as object rotation, scaling, translation, or image illumination changes, in order to, e.g., improve the reliability of a pattern recognition system. The classical moment invariants methodology is based on the determination of a set of transformations (or perturbations) for which the system must remain unaltered. Although very well established, the classical moment invariants theory has been mainly used for processing single static images (i.e. snapshots) and the use of image moments to analyze images sequences or video, from a dynamic point of view, has not been sufficiently explored and is a subject of much interest nowadays. In this paper, we propose the use of variant moments as an alternative to the classical approach. This approach presents clear differences compared to the classical moment invariants approach, that in specific domains have important advantages. The difference between the classical invariant and the proposed variant approach is mainly (but not solely) conceptual: invariants are sensitive to any image change or perturbation for which they are not invariant, so any unexpected perturbation will affect the measurements (i.e. is subject to uncertainty); on the contrary, a variant moment is designed to be sensitive to a specific perturbation, i.e., to measure a transformation, not to be invariant to it, and thus if the specific perturbation occurs it will be measured; hence any unexpected disturbance will not affect the objective of the measurement confronting thus uncertainty. Furthermore, given the fact that the proposed variant moments are orthogonal (i.e. uncorrelated) it is possible to considerably reduce the total inherent uncertainty. The presented approach has been applied to interesting open problems in computer vision such as shape analysis, image segmentation, tracking object deformations and object motion tracking, obtaining encouraging results and proving the effectiveness of the proposed approach.     

一种全局和声搜索算法求解绝对值方程

绝对值方程Ax-|x|=b是一个不可微的NP-hard问题。在假设矩阵A的奇异值大于1时, 给出了一个求解绝对值方程的全局和声搜索算法。新的和声搜索算法使用了位置更新和小概率变异策略, 实验结果表明, 该算法具有较强的全局搜索能力, 且收敛快、数值稳定性好、参数少等优点, 是求解绝对值方程的一种有效算法。     

Approximate finite‐time control for a class of uncertain nonlinear systems with dynamic compensation

In this work, a new robust nonlinear feedback control method with dynamic active compensation is proposed; the active control method has been applied to an integral series of finite‐time single‐input single‐output nonlinear system with uncertainty. In further tracking the closed‐loop stability and nonlinear uncertainty, an extended state observer has been employed to solve the immeasurability and nonlinear uncertainty within a nonlinear system. A singular perturbation theory has been used to solve for the finite‐time stability of the closed‐loop system; furthermore, numerical simulations showed that the robust nonlinear feedback controller tracked the uncertainty in a nonlinear Duffing‐type oscillator and has proven the effectiveness of the approximate finite‐time control strategy proposed. By using an approximate finite‐time control approach with active compensation, the uncertainty in a nonlinear system could be accurately estimated and controlled. Copyright © 2017 John Wiley & Sons, Ltd.     

An efficient model predictive controller with pole placement

For model predictive control (MPC) of constrained systems, enlarging the feasible region is usually in conflict with improving the dynamic performance. To resolve the conflict, we proposed an efficient model predictive controller with pole placement for a class of discrete-time linear systems. By specifying a group of circular regions that contain the desired closed-loop poles, appropriate terminal weighting matrices and local controllers are calculated to construct a time-varying terminal convex set, which is a significant constraint for the online optimization problem. During the online optimization, the size of the terminal convex set can adjust itself according to the actual state at each sampling time. In this way, a large initial feasible region can be achieved while maintaining the good dynamic performance. An illustrative example is used to show the effectiveness of the proposed approach.