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.     

Persistency of excitation for continuous-time systems — Time-domain approach

This paper presents the time-domain approach to the analysis of the convergence of continuous-time adaptive control and estimation algorithms. The time-domain definition of persistently exciting signals is introduced and the convergence of estimation algorithms is established in the cases of open-loop and closed-loop systems. An application of the persistency of excitation theory to the design of a globally stable adaptive pole-placement controller is given.     

How exciting can a signal really be?

The rate of parameter convergence in a number of adaptive estimation schemes is related to the smallest eigenvalue of the average information matrix determined by the regression vector. Using a very simple examples, we illustrate that the input signals that maximize this minimum eigenvalue may be quite different from the input signals that optimize more classical input design criteria, e.g. D-optimal criterion.     

A note on persistency of excitation

We prove that if a component of the response signal of a controllable linear time-invariant system is persistently exciting of sufficiently high order, then the windows of the signal span the full system behavior. This is then applied to obtain conditions under which the state trajectory of a state representation spans the whole state space. The related question of when the matrix formed from a state sequence has linearly independent rows from the matrix formed from an input sequence and a finite number of its shifts is of central importance in subspace system identification.     

Explicit convergence rates for MRAC-type systems

It is well known since many years ago that for the linear time-varying system , with A Hurwitz, and B(t) bounded and globally Lipschitz, it is necessary and sufficient for global exponential stability, that B(t) satisfy the so-called persistency of excitation condition. In this note, we revisit this question and provide explicit bounds for the convergence rate and on the overshoot of the transient behaviour of the solutions e(t), θ(t) as functions of the richness of B(·). We believe that the result that we present is useful since knowing convergence rates aids in the construction of converse Lyapunov functions. Moreover, the type of systems that we study here appear for instance in model reference adaptive control (MRAC).     

Uniform stabilization for linear systems with persistency of excitation: the neutrally stable and the double integrator cases

Consider the controlled system dx/dt = Ax + α(t)Bu where the pair (A, B) is stabilizable and α(t) takes values in [0, 1] and is persistently exciting, i.e., there exist two positive constants μ, T such that, for every t ≥ 0, ${\int_t^{t+T}\alpha(s){\rm d}s \geq \mu}Consider the controlled system dx/dt = Ax + α(t)Bu where the pair (A, B) is stabilizable and α(t) takes values in [0, 1] and is persistently exciting, i.e., there exist two positive constants μ, T such that, for every t ≥ 0, . In particular, when α(t) becomes zero the system dynamics switches to an uncontrollable system. In this paper, we address the following question: is it possible to find a linear time-invariant state-feedback u = Kx, with K only depending on (A, B) and possibly on μ, T, which globally asymptotically stabilizes the system? We give a positive answer to this question for two cases: when A is neutrally stable and when the system is the double integrator. Notation  A continuous function is of class , if it is strictly increasing and is of class if it is continuous, non-increasing and tends to zero as its argument tends to infinity. A function is said to be a class -function if, for any t ≥ 0, and for any s ≥ 0. We use |·| for the Euclidean norm of vectors and the induced L ₂-norm of matrices.     

传递函数估计误差的硬界及其渐近性质

本文讨论带遗忘因子的最小二乘法估计传递函数的误差硬界及其渐近性质.在噪声有界等一定假定下,当样本个数趋向无限时,误差硬界收敛于复平面内的一个圆.     

Optimal finite-dimensional recursive identification in a polynomial output mapping class

A time-varying polynomial output equation expressing an implicit dependence of the observed signal on the parameter vector admits a finite-dimensional recursive representation for the optimal on-line estimator. Both continuous and discrete observations result in the differential systems which give the estimate. Deterministic exponentially weighted least-squares is applied. Exponential stability of the identifier and convergence to the true parameter value are shown. Two examples are presented. A continuous application originates in a pH-control process. The discrete identification is applied in a dynamic ARMA-type model which is quadratic in the parameter.     

Matrosov theorem for parameterized families of discrete-time systems

A version of Matrosov's theorem for parameterized discrete-time time-varying systems is presented. The theorem is a discrete-time version of the continuous-time result in Loria et al., 2002 (δ-persistency of excitation: a necessary and sufficient condition for uniform attractivity, 2002, submitted for publication). Our result facilitates controller design for sampled-data nonlinear systems via their approximate discrete-time models. An application of the theorem to establishing uniform asymptotic stability of systems controlled by model reference adaptive controllers designed via approximate discrete-time plant models is presented.     

Robust stability of uncertain stochastic differential delay equations

In this paper we first discuss the robust stability of uncertain linear stochastic differential delay equations. We then extend the theory to cope with the robust stability of uncertain semi-linear stochastic differential delay equations. We shall also give several examples to illustrate our theory.     

Persistency of excitation and performance of deterministic learning

Recently, a deterministic learning theory was proposed for locally-accurate identification of nonlinear systems. In this paper, we investigate the performance of deterministic learning, including the learning speed and learning accuracy. By analyzing the convergence properties of a class of linear time-varying (LTV) systems, explicit relations between the persistency of excitation (PE) condition (especially the level of excitation) and the convergence properties of the LTV systems are derived. It is shown that the learning speed increases with the level of excitation and decreases with the upper bound of PE. An optimal learning speed is shown to exist. The learning accuracy also increases with the level of excitation, in particular, when the level of excitation is large enough, locally-accurate learning can be achieved to the desired accuracy, whereas low level of PE may result in the deterioration of the learning performance. This paper reveals that the performance analysis of deterministic learning can be established on the basis of classical results on stability and convergence of adaptive control. Simulation studies are included to illustrate the results.     

Exponential stability of nonlinear impulsive switched systems with stable and unstable subsystems

Exponential stability and robust exponential stability relating to switched systems consisting of stable and unstable nonlinear subsystems are considered in this study. At each switching time instant, the impulsive increments which are nonlinear functions of the states are extended from switched linear systems to switched nonlinear systems. Using the average dwell time method and piecewise Lyapunov function approach, when the total active time of unstable subsystems compared to the total active time of stable subsystems is less than a certain proportion, the exponential stability of the switched system is guaranteed. The switching law is designed which includes the average dwell time of the switched system. Switched systems with uncertainties are also studied. Sufficient conditions of the exponential stability and robust exponential stability are provided for switched nonlinear systems. Finally, simulations show the effectiveness of the result.     

Near optimal solutions to least-squares problems with stochastic uncertainty

In this paper, we consider least-squares (LS) problems where the regression data is affected by parametric stochastic uncertainty. In this setting, we study the problem of minimizing the expected value with respect to the uncertainty of the LS residual. For general nonlinear dependence of the data on the uncertain parameters, determining an exact solution to this problem is known to be computationally prohibitive. Here, we follow a probabilistic approach, and determine a probable near optimal solution by minimizing the empirical mean of the residual. Finite sample convergence of the proposed method is assessed using statistical learning methods. In particular, we prove that if one constructs the empirical approximation of the mean using a finite number N of samples, then the minimizer of this empirical approximation is, with high probability, an ε-suboptimal solution for the original problem. Moreover, this approximate solution can be efficiently determined numerically by a standard recursive algorithm. Comparisons with gradient algorithms for stochastic optimization are also discussed in the paper and several numerical examples illustrate the proposed methodology.     

On using least-squares updates without regressor filtering in identification and adaptive control of nonlinear systems

In continuous-time system identification and adaptive control the least-squares parameter estimation algorithm has always been used with regressor filtering, which adds to the dynamic order of the identifier and affects its performance. We present an approach for designing a least-squares estimator that uses an unfiltered regressor. We also consider a problem of adaptive nonlinear control and present the first least-squares-based adaptive nonlinear control design that yields a complete Lyapunov function. The design is presented for linearly parametrized nonlinear control systems in ‘normal form’. A scalar linear example is included which adds insight into the key ideas of our approach and allows showing that, for linear systems, our Lyapunov-LS design with unfiltered regressor, presented in the note for unnormalized least-squares, can also be extended to normalized least-squares.     

Identification of the gain system with quantized observations and bounded persistent excitations

System identification with quantized observations and persistent excitations is a fundamental and difcult problem.As the first step,this paper takes the gain system for example to investigate the identification with quantized observations and bounded persistently exciting inputs.Firstly,the identification with single threshold quantization is considered.A projection recursive algorithm is proposed to estimate the unknown parameter.By use of the conditional expectation of quantized observations with respect to the estimates,the algorithm is shown to be both mean-square and almost surely convergent.The upper bound of the convergence rate is also obtained,which has the same order as the one of the optimal estimation in the case where the system output is exactly known.Secondly,for the multi-threshold quantization,the identification algorithm is similarly constructed and its asymptotic property is analyzed.Using a multi-linear transformation,the optimal scheme of quantization values and thresholds is given.A numerical example is simulated to demonstrate the efectiveness of the algorithms and the main results obtained.