Optimal design for manipulation of random consensus over discrete information in networked systems

In distributed and cooperative systems, the network structure is determinant to the success of the strategy adopted to solve complex tasks. Those systems are primarily governed by consensus protocols whose convergence is intrinsically dependent on the network topology. Most of the consensus algorithms deal with continuous values and perform average-based strategies to reach cohesion over the exchanged information. However, many problems demand distributed consensus over countable values, that cannot be handled by traditional protocols. In such a context, this work presents an approach based on semidefinite programming to design the optimal weights of a network adjacency matrix, in order to control the convergence of a distributed random consensus protocol for variables at the discrete-space domain, based on the voter model. As a second contribution, this work uses Markov theory and the biological inspiration of epidemics to find out a dynamical spreading model that can predict the information diffusion over this discrete consensus protocol. Also, convergence properties and equilibrium points of the proposed model are presented regarding the network topology. Finally, extensive numerical simulations evaluate the effectiveness of the proposed consensus algorithm, its spreading model, and the approach for optimal weight design.     

化学发展中的理论与计算

理论与计算对现代化学的发展起着至关重要的作用。通过电子结构计算我们可以获得体系的各种性质。电子结构理论与计算的发展方向是提高对某些复杂体系的计算精度,同时提高计算效率使处理更大的体系成为可能。对三原子反应,反应动力学计算已经可以精确地考虑量子效应。多原子气相反应和复杂体系的动力学行为是目前的研究难点。统计力学与分子模拟面临的最大挑战是构造普适精确的分子力场与粗粒化模型,以及多尺度模拟方法。近年来,我国的理论与计算化学发展很快,同时也面临很大的机遇与挑战。     

Nonlinear distributed-parameter filtering using The Forker-Planck equation approach

Recently the optimal filtering problem for nonlinear distributed-parameter systems was treated using various statistical approaches. This paper treats the same problem by using the Fokker-Planck-Kolmogorov equation technique. The arguments used are rather heuristic but the purpose of the paper is mainly to motivate further studies in the area of stochastic differential equations of the distributed-parameter type. An extension is then given to cover the case where the system equations contain unknown stochastic dynamic parameters. The results are illustrated by two practical examples. As a by-product a derivation of the distributed-parameter Fokker-Planck-Kolmogorov equations is given. These equations are useful not only for filtering purposes but also for other probabilistic considerations of distributed-parameter systems.     

Event-triggered fully distributed asymptotic consensus for leaderless multiple Euler-Lagrange systems

In this paper, the fully distributed consensus for a class of multiple Euler-Lagrange systems is investigated, where the protocol is designed under the event-triggered control framework and the dynamics of Euler-Lagrange systems are heterogeneous. Since only local information interactions at triggered instants can be used and the Euler-Lagrange systems are of relatively complex dynamics, it is challenging to achieve asymptotic consensus without using any global information (such as the Laplacian matrix information). By skillfully integrating the adaptive control, distributed control and event-triggered control techniques, a novel protocol is proposed for the investigated multiple Euler-Lagrange systems. It is proven that the asymptotic consensus can be achieved by the developed protocol. By a numerical example, the effectiveness of the developed protocol is illustrated.     

A new method for designing distributed reduced-order functional observers of interconnected time-delay systems

This paper reports a new method for designing distributed reduced-order functional observers of a class of interconnected systems with time delays. The systems under consideration belong to a class of large-scale systems where each system is formed by a number of interconnected subsystems. Moreover, the interconnections and the states of the local subsystems are subject to heterogeneous time delays. The novel contribution of this paper lies in the development of new coordinate state transformations, which are used to transform the interconnected subsystems into decoupled subsystems. Most significantly, each decoupled subsystem does not contain any time delay in the state vector. Moreover, each decoupled subsystem is expressed in an observable canonical form, with time delays only appearing in the inputs and outputs of the system. Due to this novel structure, a reduced-order functional observer for each decoupled subsystem can be easily designed to estimate the unmeasurable local state vector. The designed observers for the local subsystems do not need to exchange the state estimates amongst themselves, and therefore, each observer for each local subsystem can be designed independently. Because of the state transformations, the designed observers have a more general structure than any of the existing distributed functional observers available in the literature. Numerical examples are given to illustrate the effectiveness and advantages of our results.     

Adaptive event-triggering distributed filter of positive Markovian jump systems based on disturbance observer

This paper presents an adaptive event-triggered filter of positive Markovian jump systems based on disturbance observer. A new adaptive event-triggering mechanism is constructed for the systems. A positive disturbance observer is designed for the systems to estimate the disturbance. A distributed output model of each subsystem of positive Markovian jump systems is introduced. Then, an adaptive event-triggering distributed filter is designed by employing stochastic copositive Lyapunov functions. All presented conditions are solvable in terms of linear programming. Under the designed disturbance observer and the distributed filter, the corresponding error system is stochastically stable. The filter design approach is also developed for discrete-time positive Markovian jump systems. The contribution of the paper lies in that: (i) A new adaptive event-triggering mechanism is established for positive systems, (ii) A positive disturbance observer is designed for the disturbance of positive Markovian jump systems, and (iii) The designed distributed filter can guarantee the stochastic stability of the error while existing filters in literature only achieve the stochastic gain stability of the error. Finally, two examples are given to illustrate the effectiveness of the proposed design.     

Distributed-parameter control in function space

Optimal control of distributed-parameter systems is studied from a function space point of view. The adjoint-space technique of Balakrishnam is applied to solve a general class of linear distributed-parameter final-value problems in Hilbert space. The minimum-time control problem with multi-norm constraints for a general class of approximately controllable systems is treated, using the technique of Sarachik and Kranc, through the application of Hölder's inequality. Finally, a generalized Hölder's inequality for more than two Banach spaces is given, which is useful for solving a certain class of nonlinear control problems by the same methods. Two examples are included which illustrate the theory.     

A maximum principle approach to optimal control for one-dimensional hyperbolic systems with several state variables

The maximum principle developed by Sloss et al. [Optimal control of structural dynamic systems in one space dimension using a maximum principle, J. Vibr. Control 11 (2005) 245–261] is used to determine the optimal control functions for a class of one-dimensional distributed parameter structures. The distributed parameter structures are governed by systems of fourth order hyperbolic equations with constant coefficients. A quadratic performance index is formulated as the cost functional of the problem and can be used to represent the energy of the structure and the force spent in the control process. The developed maximum principle establishes a theoretical foundation for the solution of the optimal control problem and relates the optimal control vector to an adjoint variable vector. The method of solution is outlined which involves reducing the original problem to a system of ordinary differential equations. The solution of the general problem is given and a structural control problem is solved to illustrate the solution procedure. The effectiveness of the proposed control solution is shown by comparing the behavior of controlled and uncontrolled systems.     

Optimization-based adaptive neural sliding mode control for nonlinear systems with fast and accurate response under state and input constraints

The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.     

基于粒子群优化算法的结构系统辨识

针对一般智能理论辨识方法在结构系统辨识中存在的问题,提出一种基于粒子群优化算法(PSO)的辨识方法。用粒子群中的粒子表征结构物理参数,以最大似然准则为粒子群优化算法的适应度函数,建立了结构系统的辨识模型。数值仿真分析表明,粒子群优化算法可以精确辨识出结构系统的物理参数。     

基于组织背景的管理控制系统设计:一个理论框架

管理控制系统作为确保资源有效利用以实现组织目标的一种组织系统，随着现代企业组织背景不确定性和复杂性程度的提高，其重要性日益凸现．本文以管理控制的权变理论为研究切入点，首先总结和分析了组织背景关键变量(外部环境、技术、结构、规模、战略和文化)对管理控制系统设计的影响作用，在此基础上，根据组织背景与管理控制系统之间的关系，提出以组织背景、管理控制系统模式和管理控制系统结果为维度，构建管理控制系统设计的理论框架。这一理论框架对于我国现代企业内部管理控制系统的建立具有重要的启示意义。     

基于ISM的平台企业开放式服务创新涌现机理研究

基于系统科学相关理论,聚焦平台企业开放式服务创新涌现性影响因素和作用机制构建模型,以企业、需求市场和金融机构等为主体,利用ISM解释结构模型构建开放式服务创新平台系统复杂单元关系图,基于复杂系统及其特征进行涌现性研究.研究发现,开放式服务创新平台的涌现性与平台自身的复杂性有着密切关系,平台主体之间通过复杂的非线性关系涌现出平台新的结构、功能和特征等.从而得出强化需求导向,集产学研创于一体;以市场调节为主,政府扶持为辅;扩大对外合作,学习开放式创新新型模式的管理启示.     

Distributed fixed-time control of high-order multi-agent systems with non-holonomic constraints

In this paper, a protocol is proposed for fixed-time consensus of the high-order chained-form multi-agent systems subject to non-holonomic constraints. By employing the backstepping structure and a power integrator, the distributed fixed-time protocol is designed to guarantee that system states reach consensus before a fixed time. The fixed settling time can be calculated explicitly, and it is independent of initial conditions. The proposed protocol is applied to multi-agent wheeled mobile robots to support the theoretical result.     

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.     

图书馆分布式数据库安全技术研究

分布式数据库的开发和利用是图书馆信息资源建设和读者服务的核心内容,其安全性已成为当前图书馆界关注的热点问题。文章分析了分布式数据库安全需求;提出了安全机制分层模型;最后,设计了一种四层体系结构的分布式数据库系统,解决了传统三层体系结构在安全性上存在的不足。     

Adaptive vibration control of a flexible structure based on hybrid learning controlled active mass damping

Natural disasters such as earthquakes and strong winds will lead to vibrations in ultra-high or high-rise buildings and even the damages of the structures. The traditional approaches resist the destructive effects of natural disasters through enhancing the performance of the structure itself. However, due to the unpredictability of the disaster strength, the traditional approaches are no longer appropriate for earthquake mitigation in building structures. Therefore, designing an effective intelligent control method for suppressing vibrations of the flexible buildings is significant in practice. This paper focuses on a single-floor building-like structure with an active mass damper (AMD) and proposes a hybrid learning control strategy to suppress vibrations caused by unknown time-varying disturbances (earthquake, strong wind, etc.). As the flexible building structure is a distributed parameter system, a novel finite dimension dynamic model is firstly constructed by assumed mode method (AMM) to effectively analyze the complex dynamics of the flexible building stucture. Secondly, an adaptive hybrid learning control based on full-order state observer is designed through back-stepping method for dealing with system uncertainties, unknown disturbances and immeasurable states. Thirdly, semi-globally uniformly ultimate boundedness (SGUUB) of the closed-loop system is guaranteed via Lyapunov’s stability theory. Finally, the experimental investigation on Quanser Active Mass Damper demonstrates the effectiveness of the presented control approach in the field of vibration suppression. The research results will bring new ideas and methods to the field of disaster reduction for the engineering development.     

Robust consensus tracking based on hABC algorithm with parameters identification for uncertain nonlinear FOMASs with external disturbances

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs.     

Walsh series approach to lumped and distributed system identification

This paper considers the problem of identifying the parameters of dynamic systems from input-output records. Both lumped-parameter and distributed-parameter systems, deterministic and stochastic, are studied. The approach adopted is that of expanding the system variables in Walsh series. The key point is an operational matrix P which relates the coefficient matrix Г of the Walsh series of a given function with the coefficient matrix of its first derivative. Using this operational matrix P one overcomes the necessity to use differentiated data, a fact that usually is avoided either by integration of the data or by using discrete-time models. Actually, the original differential input-output model is converted to a linear algebraic (or regression) model convenient for a direct (or a least squares) solution. A feature of the method is that it permits the identification of unknown initial conditions simultaneously with the parameter identification. The results are first derived for single-input single-output systems and then are extended to multi-input multi-output systems. The case of non-constant parameters is treated by assuming polynomial forms. Some results are also included concerning the identification of state-space and integral equation models. The theory is supported by two examples, which give an idea of how effective the method is expected to be in the real practice.     

A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism

In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller.     

Dynamical behavior in a harvested differential-algebraic prey-predator model with discrete time delay and stage structure

A differential-algebraic model system which considers a prey-predator system with stage structure for prey and harvest effort on predator is proposed. By using the differential-algebraic system theory and bifurcation theory, the dynamic behaviors of the proposed model system with and without discrete time delay are investigated. Local stability analysis of the model system without discrete time delay reveals that there is a phenomenon of singularity induced bifurcation due to variation of the economic interest of harvesting, and a state feedback controller is designed to stabilize the proposed model system at the interior equilibrium; on the other hand, the local stability of the model system with discrete time delay is also studied. The theoretical analysis shows that the discrete time delay has a destabilizing effect in the model of population dynamics, and a phenomenon of Hopf bifurcation occurs as the discrete time delay increases through a certain threshold. Numerical simulations are carried out to show the consistency with theoretical analysis.