Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.      

Analysis of discrete-time piecewise affine and hybrid systems

In this paper, we present various algorithms both for stability and performance analysis of discrete-time piece-wise affine (PWA) systems. For stability, different classes of Lyapunov functions are considered and it is shown how to compute them through linear matrix inequalities that take into account the switching structure of the systems. We also show that the continuity of the Lyapunov function is not required in discrete time. Moreover, the tradeoff between the degree of conservativeness and computational requirements is discussed. Finally, by using arguments from the dissipativity theory for nonlinear systems, we generalize our approach to analyze the l₂-gain of PWA systems.     

The concave-convex procedure

The concave-convex procedure (CCCP) is a way to construct discrete-time iterative dynamical systems that are guaranteed to decrease global optimization and energy functions monotonically. This procedure can be applied to almost any optimization problem, and many existing algorithms can be interpreted in terms of it. In particular, we prove that all expectation-maximization algorithms and classes of Legendre minimization and variational bounding algorithms can be reexpressed in terms of CCCP. We show that many existing neural network and mean-field theory algorithms are also examples of CCCP. The generalized iterative scaling algorithm and Sinkhorn's algorithm can also be expressed as CCCP by changing variables. CCCP can be used both as a new way to understand, and prove the convergence of, existing optimization algorithms and as a procedure for generating new algorithms.     

A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

This paper develops a fully distributed hybrid control framework for distributed constrained optimization problems. The individual cost functions are non-differentiable and convex. Based on hybrid dynamical systems, we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization methods. The proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump set. With the semistability theory of hybrid dynamical systems, the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different solutions. Numerical simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.      

Robust stability analysis of discrete-time systems using geneticalgorithms

We reduce stability robustness analysis for linear, time-invariant, discrete-time systems to a search problem and attack the problem using genetic algorithms. We describe the problem framework and the modifications that needed to be made to the canonical genetic algorithm for successful application to robustness analysis. Our results show that genetic algorithms can successfully test a sufficient condition for instability in uncertain linear systems with nonlinear polynomial structures. Three illustrative examples demonstrate the new approach     

On uniform asymptotic stability of time-varying parameterized discrete-time cascades

Recently, a framework for controller design of sampled-data nonlinear systems via their approximate discrete-time models has been proposed in the literature. In this paper, we develop novel tools that can be used within this framework and that are useful for tracking problems. In particular, results for stability analysis of parameterized time-varying discrete-time cascaded systems are given. This class of models arises naturally when one uses an approximate discrete-time model to design a stabilizing or tracking controller for a sampled-data plant. While some of our results parallel their continuous-time counterparts, the stability properties that are considered, the conditions that are imposed, and the the proof techniques that are used, are tailored for approximate discrete-time systems and are technically different from those in the continuous-time context. A result on constructing strict Lyapunov functions from nonstrict ones that is of independent interest, is also presented. We illustrate the utility of our results in the case study of the tracking control of a mobile robot. This application is fairly illustrative of the technical differences and obstacles encountered in the analysis of discrete-time parameterized systems.     

离散时间系统零动态的研究现状和未来挑战

在数字控制系统的分析与设计中, 零动态是一个被广泛关注的重要概念, 近年来取得了诸多新的理论与方法进展. 本文首先描述了离散时间系统零动态理论的研究背景和研究意义, 同时简要介绍了离散时间系统零动态理论所涉及到的3个相关问题, 如: 信号的采样与重建、连续时间系统的等价离散时间系统模型以及在离散连续时间系统过程中所需要的工具(q算子和±算子). 其次, 立足现有文献, 针对离散零动态的特点, 从线性离散时间系统和非线性离散时间系统两个方面全面而深入地介绍了近年来离散零动态研究工作的进展. 最后分析了零动态在数字控制系统分析与设计中的局限性以及出现的挑战性课题, 并指明未来工作的研究方向.     

Steepest descent algorithms for neural network controllers andfilters

A number of steepest descent algorithms have been developed for adapting discrete-time dynamical systems, including the backpropagation through time and recursive backpropagation algorithms. In this paper, a tutorial on the use of these algorithms for adapting neural network controllers and filters is presented. In order to effectively compare and contrast the algorithms, a unified framework for the algorithms is developed. This framework is based upon a standard representation of a discrete-time dynamical system. Using this framework, the computational and storage requirements of the algorithms are derived. These requirements are used to select the appropriate algorithm for training a neural network controller or filter. Finally, to illustrate the usefulness of the techniques presented in this paper, a neural network control example and a neural network filtering example are presented.     

单参数离散时间非线性系统的可镇定性定理

本文对基本的离散时间非线性单参数随机系统建立了可镇定性定理. 该定理推进了文献[1]的结果, 进一步完 善了关于离散时间自适应控制的反馈能力刻画. 离散时间单参数系统可镇定的一个重要非线性临界常数是4, 用以刻画 关于幂函数类系统的反馈能力. 而作为本文定理的应用, 本文对一类典型的单参数离散时间非线性随机系统发现了新的 可镇定临界常数2.     

Solution of discrete-time optimal control problems on parallel computers

We describe locally-convergent algorithms for discrete-time optimal control problems which are amenable to multiprocessor implementation. Parallelism is achieved both through concurrent evaluation of the component functions and their derivatives, and through the use of a parallel solver which solves a linear system to find the step at each iteration.. Results from an implementation on the Alliant FX/8 are described.     

A Control-Theoretic Approach to the Design of Zero Finding Numerical Methods

In this paper, it is shown how standard iterative methods for solving linear and nonlinear equations can be designed from the point of view of control. Appropriate choices of control Lyapunov functions (CLFs) lead to both continuous and discrete-time versions of the Newton-Raphson and conjugate gradient algorithms as well as new variants.     

Small-gain theory for stability and control of dynamical networks: A Survey

This paper provides a personal account of the small-gain theory as a tool for stability analysis, control synthesis, and robustness analysis for interconnected uncertain systems. A milestone in modern control theory is the development of a transformative stability criterion known as the classical small-gain theorem proposed by George Zames in 1966, that surpasses Lyapunov theory in that there is no need to construct Lyapunov functions for the finite-gain stability of feedback systems. Under the small-gain framework, a feedback system composed of two finite-gain stable subsystems remains finite-gain stable if the loop gain is less than one. Despite its apparent simplicity at first sight, Zames’s small-gain theorem plays a crucial role in the development of linear robust control theory. Borrowing techniques in modern nonlinear control, especially Sontag’s notion of input-to-state stability (ISS), the first generalized, nonlinear ISS small-gain theorem proposed by one of the authors in 1994 overcomes the two shortcomings of Zames’s small-gain theorem. First, the use of nonlinear gains allows to consider strongly nonlinear, interconnected systems. Second, the role of initial conditions is made explicit so that both internal Lyapunov stability and external input-output stability can be studied in a unified framework. In this survey paper, we first review early developments in the nonlinear small-gain theory for interconnected systems of various types such as continuous-time systems, discrete-time systems, hybrid systems and time-delay systems, along with applications in robust nonlinear control. Then, we describe how to obtain a network small-gain theory for large-scale dynamical networks that are comprised of more than two interacting nonlinear systems. Constructive methods for the generation of Lyapunov functions for the total network are presented as well. Finally, this paper discusses how the network/nonlinear small-gain theory can be applied to obtain innovative solutions to quantized and event-based nonlinear control problems, that are important for the development of a complete theory of controlling cyber-physical systems subject to communications and computation constraints.     

A note on input-to-state stabilization for nonlinear sampled-datasystems

We provide a framework for the design of L_∞ stabilizing controllers via approximate discrete-time models for sampled-data nonlinear systems with disturbances. In particular, we present sufficient conditions under which a discrete-time controller that input-to-state stabilizes an approximate discrete-time model of a nonlinear plant with disturbances would also input-to-state stabilize (in an appropriate sense) the exact discrete-time plant model     

Fault detection for nonlinear discrete-time systems via deterministic learning

Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions (i.e., the internal dynamics) underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function (RBF) networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.     

Symbolic computation of exact solutions expressible in hyperbolic and elliptic functions for nonlinear PDEs

Algorithms are presented for the tanh- and sech-methods, which lead to closed-form solutions of nonlinear ordinary and partial differential equations (ODEs and PDEs). New algorithms are given to find exact polynomial solutions of ODEs and PDEs in terms of Jacobi’s elliptic functions.For systems with parameters, the algorithms determine the conditions on the parameters so that the differential equations admit polynomial solutions in tanh, sech, combinations thereof, Jacobi’s sn or cn functions. Examples illustrate key steps of the algorithms.The new algorithms are implemented in Mathematica. The package PDESpecialSolutions.m can be used to automatically compute new special solutions of nonlinear PDEs. Use of the package, implementation issues, scope, limitations, and future extensions of the software are addressed.A survey is given of related algorithms and symbolic software to compute exact solutions of nonlinear differential equations.     

Discrete-time delayed standard neural network model and its application

The research on the theory and application of artificial neural networks has achieved a great success over the past two decades. Recently, increasing attention has been paid to recurrent neural networks, which are rich in dynamics, highly parallelizable, and easily implementable with VLSI. Due to these attractive features, RNNs have widely been applied to system identification, control, optimization and associative memories[1]. Stability analysis, which is critical to any applications of R…     

Correlation analysis of a discrete-time flexible arrival process

In this paper, a flexible discrete-time arrival process is introduced and its correlation properties are analyzed. The arrival process is the so-called batch-on/off model, an extension of the original on/off source used in the context of ATM networks. In the batch-on/off model, a group of arrivals may be generated at any given active slot. General distributions are assumed for the three input random variables characterizing the process: busy and idle periods, and batch size. The analysis focuses on two related processes: the process of counts and the sequence of interarrival times. For each process, an exact closed-form expression of its complete autocorrelation function is obtained. Explicit algorithms are provided to compute both autocorrelation functions, which are numerically evaluated for different distributions of the busy and idle periods and the batch size. The results provided in this paper reveal the analytical tractability of these models which, in addition to their flexibility, makes them very suitable for the performance evaluation of discrete-time communication systems and for general research in the area of queuing theory.