Dwell-time approach to input-output stability properties for a class of discrete-time dynamical systems

We consider a class of discrete-time dynamical systems that consist of switching of many linear time-invariant systems, and it can be used to cover a sampled-data version of Switched Linear Systems, which have been widely studied in the literature. For this class of dynamical systems, we establish the LMI (Linear Matrix Inequality) formulation of analyzing their input-output stability properties, including both ?-2 stability and passivity, by constraining switching signals via the concept of dwell time. The LMI formulation of ?-2 stability is applied to evaluating the closed-loop performance of an industrial refrigeration process that is regulated by several proportional-integral (PI) controllers in a switching structure.     

On controllability and observability for a class of impulsive systems

Many dynamic systems in physics, chemistry, biology, engineering, and information science have impulsive dynamical behaviors due to abrupt jumps at certain instants during the dynamical processes. These complex dynamic behaviors can be modeled by impulsive differential systems. This paper studies the controllability and observability for a class of time-varying impulsive control systems. Several sufficient and necessary conditions for state controllability and state observability of such systems are established and the corresponding criteria for time-invariant impulsive control systems are also obtained. Meanwhile, several new results associated with variation of parameters for time-varying impulsive control systems are derived.     

Structural identifiability of dynamical systems

Deterministic identifiability of finite dimensional dynamical systems is analysed using a generalized concept of structural invariants. Results applied to time-invariant bilinear and time-variable linear systems yield algebraic conditions for identifiability which are natural generalizations of the well known identifiability criteria for time-invariant linear systems.     

Finite-time behavior of inner systems

In this paper, we investigate how nonminimum phase characteristics of a dynamical system affect its controllability and tracking properties. For the class of linear time-invariant dynamical systems, these characteristics are determined by transmission zeros of the inner factor of the system transfer function. The relation between nonminimum phase zeros and Hankel singular values of inner systems is studied and it is shown how the singular value structure of a suitably defined operator provides relevant insight about system invertibility and achievable tracking performance. The results are used to solve various tracking problems both on finite as well as on infinite time horizons. A typical receding horizon control scheme is considered and new conditions are derived to guarantee stabilizability of a receding horizon controller.     

Nonlinear observers for perspective time-invariant linear systems

Perspective dynamical systems arise in machine vision, in which only perspective observation is available. This paper proposes and studies a Luenberger-type nonlinear observer for perspective time-invariant linear systems. Assuming a given perspective time-invariant linear system to be Lyapunov stable and to satisfy some sort of detectability condition, it is shown that the estimation error converges exponentially to zero. Finally, some simple numerical examples are presented to illustrate the result obtained.     

Complete memory structures for approximating nonlineardiscrete-time mappings

This paper introduces a general structure that is capable of approximating input-output maps of nonlinear discrete-time systems. The structure is comprised of two stages, a dynamical stage followed by a memoryless nonlinear stage. A theorem is presented which gives a simple necessary and sufficient condition for a large set of structures of this form to be capable of modeling a wide class of nonlinear discrete time systems. In particular, we introduce the concept of a "complete memory". A structure with a complete memory dynamical stage and a sufficiently powerful memoryless stage is shown to be capable of approximating arbitrarily wide class of continuous, causal, time invariant, approximately-finite-memory mappings between discrete-time signal spaces. Furthermore, we show that any bounded-input bounded output, time-invariant, causal memory structure has such an approximation capability if and only if it is a complete memory. Several examples of linear and nonlinear complete memories are presented. The proposed complete memory structure provides a template for designing a wide variety of artificial neural networks for nonlinear spatiotemporal processing.     

Stabilization of sets with application to multi-vehicle coordinated motion

In this paper, we develop stability and control design framework for time-varying and time-invariant sets of nonlinear dynamical systems using vector Lyapunov functions. Several Lyapunov functions arise naturally in multi-agent systems, where each agent can be associated with a generalized energy function which further becomes a component of a vector Lyapunov function. We apply the developed control framework to the problem of multi-vehicle coordinated motion to design distributed controllers for individual vehicles moving in a specified formation. The main idea of our approach is that a moving formation of vehicles can be characterized by a time-varying set in the state space, and hence, the problem of distributed control design for multi-vehicle coordinated motion is equivalent to the design of stabilizing controllers for time-varying sets of nonlinear dynamical systems. The control framework is shown to ensure global exponential stabilization of multi-vehicle formations. Finally, we implement the feedback stabilizing controllers for time-invariant sets to achieve global exponential stabilization of static formations of multiple vehicles.     

Continuity of dynamical systems: A system theoretic approach

This paper studies continuity of linear time-invariant dynamical systems, defined in terms of the system’s behavior. This concept is related to parameter continuity of associated system representations. For the case at hand, these will be autoregressive (AR) representations. The main result states that a family of linear time-invariant systems, with uniformly bounded dimension of the state space, converges if and only if the systems admit a convergent full rank (AR) representation.     

Stable inversion of LPTV systems with application in position-dependent and non-equidistantly sampled systems

Many control applications, including feedforward and learning control, involve the inverse of a dynamical system. For nonminimum-phase systems, the response of the inverse system is unbounded. For linear time-invariant (LTI), nonminimum-phase systems, a bounded, noncausal inverse response can be obtained through an exponential dichotomy. For generic linear time-varying (LTV) systems, such a dichotomy does not exist in general. The aim of this paper is to develop an inversion approach for an important class of LTV systems, namely linear periodically time-varying (LPTV) systems, which occur in, e.g. position-dependent systems with periodic tasks and non-equidistantly sampled systems. The proposed methodology exploits the periodicity to determine a bounded inverse for general LPTV systems. Conditions for existence are provided. The method is successfully demonstrated in several application cases, including position-dependent and non-equidistantly sampled systems.     

Maneuver-based motion planning for nonlinear systems with symmetries

In this paper, we introduce an approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control inputs. Motion plans are described as the concatenation of a number of well-defined motion primitives, selected from a finite library. Rules for the concatenation of primitives are given in the form of a regular language, defined through a finite-state machine called a Maneuver Automaton. We analyze the reachability properties of the language, and present algorithms for the solution of a class of motion-planning problems. In particular, it is shown that the solution of steering problems for nonlinear dynamical systems with symmetries and invariant constraints can be reduced to the solution of a sequence of kinematic inversion problems. A detailed example of the application of the proposed approach to motion planning for a small aerobatic helicopter is presented.     

A direct proof of equivalence of invertibility criteria of multi-variable linear systems†

The purpose of this paper is to give a direct algebraic proof, without invoking the existence of inverse systems, that the two criteria of invertibility of multi-variable linear time-invariant dynamical systems given by Sain and Massey (1969) are equivalent. In a dual manner, the method of this paper can be used to prove directly that the various criteria of functional reproducibility of multi-variable linear systems, which have appeared in the literature, are equivalent.     

Pinning control and controllability of complex dynamical networks

In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output (SISO) or multi-input multi-output (MIMO) dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant (LTI) systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying (LTV) node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.     

Uncertain discrete systems: uniform ultimate bounded stabilization

The stabilization problem of a class of linear time-invariant, discrete systems with additive-type uncertainties is discussed. The dynamical system contains uncertain elements that are known to belong to prescribed compact bounding intervals. In addition, the system to be corrupted by uncertain bounded inputs is considered. A two-part switching feedback controller structure is developed in order to stabilize the uncertain dynamical system. The form of the controller (a linear part + a non-, linear part) guarantees global uniform ultimate boundedness under certain hypotheses on the bounded system uncertainties, as well as on bounded admissible additive disturbances. The performance of the uncertain system under the application of the proposed controller is compared with that using a purely linear controller. The proposed system gives superior performance.     

Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes

We propose a family of kernels based on the Binet-Cauchy theorem, and its extension to Fredholm operators. Our derivation provides a unifying framework for all kernels on dynamical systems currently used in machine learning, including kernels derived from the behavioral framework, diffusion processes, marginalized kernels, kernels on graphs, and the kernels on sets arising from the subspace angle approach. In the case of linear time-invariant systems, we derive explicit formulae for computing the proposed Binet-Cauchy kernels by solving Sylvester equations, and relate the proposed kernels to existing kernels based on cepstrum coefficients and subspace angles. We show efficient methods for computing our kernels which make them viable for the practitioner. Besides their theoretical appeal, these kernels can be used efficiently in the comparison of video sequences of dynamic scenes that can be modeled as the output of a linear time-invariant dynamical system. One advantage of our kernels is that they take the initial conditions of the dynamical systems into account. As a first example, we use our kernels to compare video sequences of dynamic textures. As a second example, we apply our kernels to the problem of clustering short clips of a movie. Experimental evidence shows superior performance of our kernels. Parts of this paper were presented at SYSID 2003 and NIPS 2004.     

Some remarks on duality over a commutative ring

This paper addresses the duality problem for dynamical, linear, time-invariant systems defined over a ring. The duality principle is at the core of theoretical results for systems defined over a field, but this principle can not be applied for systems over a ring. From the definition of controlled and conditioned invariant submodules in the geometric approach, we analyze the relationships among various notions of invariance, using the concept of orthogonal submodule. These logical relations are summarized in two non-equivalent schemes.     

On the existence of periodic solutions in time-invariant fractional order systems

Periodic solutions and their existence are one of the most important subjects in dynamical systems. Fractional order systems like integer ones are no exception to this rule. Tavazoei and Haeri (2009) have shown that a time-invariant fractional order system does not have any periodic solution. In this article, this claim has been investigated and it is shown that although in any finite interval of time the solutions do not show any periodic behavior, when the steady state responses of fractional order systems are considered, periodic orbits can be detected.     

Input-to-state stability for a class of hybrid dynamical systems via averaging

Input-to-state stability (ISS) properties for a class of time-varying hybrid dynamical systems via averaging method are considered. Two definitions of averages, strong average and weak average, are used to approximate the time-varying hybrid systems with time-invariant hybrid systems. Closeness of solutions between the time-varying system and solutions of its weak or strong average on compact time domains is given under the assumption of forward completeness for the average system. We also show that ISS of the strong average implies semi-global practical (SGP)-ISS of the actual system. In a similar fashion, ISS of the weak average implies semi-global practical derivative ISS (SGP-DISS) of the actual system. Through a power converter example, we show that the main results can be used in a framework for a systematic design of hybrid feedbacks for pulse-width modulated control systems.     

On Consistency of Subspace Methods for System Identification

Subspace methods for identification of linear time-invariant dynamical systems typically consist of two main steps. First, a so-called subspace estimate is constructed. This first step usually consists of estimating the range space of the extended observability matrix. Secondly, an estimate of system parameters is obtained, based on the subspace estimate. In this paper, the consistency of a large class of methods for estimating the extended observability matrix is analyzed. Persistence of excitation conditions on the input signal are given which guarantee consistent estimates for systems with only measurement noise. For systems with process noise, it is shown that a persistence of excitation condition on the input is not sufficient. More precisely, an example for which the subspace methods fail to give a consistent estimate of the transfer function is given. This failure occurs even if the input is persistently exciting of any order. It is also shown that this problem can be eliminated if stronger conditions on the input signal are imposed.     

Persistency of excitation criteria for linear,multivariable, time-varying systems

For continuous-time, multiple-input, multiple-output, linear systems, we present conditions under which the persistency of excitation of one regression vector implies the persistency of another regression vector derived from the first via a linear, dynamical transformation. We then introduce a definition of sufficient richness for vector input signals in the form of a persistency of excitation condition on a basis regression vector. Finally we establish input conditions which guarantee the persistency of excitation of a large class of regression vectors obtained from both time-invariant and time-varying systems. The work reported was performed while both authors were at the Department of Systems Engineering, Research School of Physical Sciences, Australian National University, G.P.O. Box 4, A.C.T. 2601, Australia.