Driven dynamics of time-varying linear systems

A high order multiplication perturbation method for linear time-varying dynamic system is presented. Firstly the time domain is dispersed with small time interval and the coefficient matrix of the dynamic system is decomposed into a “large amount” and a “small amount” in each time interval. One order perturbation system is then presented from the original dynamic system by a variable transformation which is also a linear time-varying dynamic system. The coefficient matrix of the new system is also decomposed into a “large amount” and a “small amount” and then higher order perturbation system can be obtained by another variable transformation. The final perturbation system can be solved exactly after abandoning the “small amount” of the coefficient matrix in the final system, and then the answer to the original problem can be determined through a series of inverse transform. Since the transfer matrix is the product of a series of exponential matrix which can be calculated accurately by the precise time integration method, so the proposed method has fine accuracy, efficiency and stability. The proposed method actually is a high order symplectic conservative perturbation method for the Hamiltonian system. The examples show that the proposed method can also give good results even though a large time step selected, and with the increase of the perturbation number, the perturbation solutions can tend to exact solutions rapidly.

Chang transformation was introduced for decoupling the slow and fast dynamics of a singularly perturbed Linear Time Invariant (LTI) system, and it was subsequently extended to Linear Time-Varying (LTV) systems under the slowly-varying assumption by way of frozen-time eigenvalues. This paper extends Chang transformation from slowly-varying LTV systems to LTV systems, when the singularly perturbed system has a semi-proper coefficient matrix A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">22</sub> (t) and LTV system ẇ(t) = A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">22</sub> (t)w(t) is exponentially stable. Instead of using frozen-time eigenvalues based on slowly time-varying conditions, PD-eigenvalues for LTV systems are employed to characterize the exponential stability, thereby circumventing the slowly-varying constraint. Our results provide a larger bound on epsilon, which is a gauge on the validity of the Chang transformation, in some situations than using previous techniques. We have also recast the original Chang transformation so as to provide more insight into the decoupled subsystems. The insight will be useful in subsequent investigation on estimate of the Singular Perturbation Margin (SPM) for LTV systems. An equivalence relationship has recently been established for LTI system between the SPM and the Phase Margin (PM). The new results in this paper will facilitate the development of a PM-type stability margin metric for LTV systems, and for nonlinear, time-varying systems in the future.

A simple interconnected power system with periodic load disturbance is considered, an energy storage based adaptive controller is proposed to output tracking and disturbance completely rejection for the system. Since the periodic load disturbance is uncertain with both amplitude and frequency, by constructing a new exosystem, the output tracking and disturbance rejection problem is solved with output regulation theory. By designing an internal model for the complicated system and introducing an appropriate transformation, the output regulation problem is changed into the stabilization problem of a new augmented system. With the help of adaptive control technique, the designed dynamic energy storage controller can solve stabilization problem of the augmented systems as well as the output tracking and disturbance rejection problem for the original system. Finally, the simulation results show the effectiveness of the proposed control strategy.

Fuzzy stable inversion-based output tracking for nonlinear non-minimum phase system and application to FAHVs

The purpose of this paper is to derive necessary and sufficient conditions for target path controllability (TPC) of time-varying dynamical systems, and to relate the results to those of Brockett and Mesarović in 1965. It is shown that conditions for TPC in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W^{p}_{1}</tex> by means of L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> -controls are easy to verify, but are very strong. TPC implies that the number of target variables is less than or equal to the number of control variables.

The paper considers non-local control and optimal control problems for a linear time-varying dynamic system described by an ordinary differential equation with undivided multipoint conditions and with an integral condition imposed on the components of the state vector in some part of the time interval of the system operation. For the problem of optimal control, it is assumed that the quality criterion is specified over the whole time interval of system operation and has the meaning of the norm of some normed space. Conditions for complete controllability of a dynamic system with imposed non-local conditions are formulated. Conditions are obtained under which a solution to the control problem with such nonlocal conditions exists, and this solution is constructed. A constructive approach to solving control problems and a method for constructing an optimal control function are proposed. Continuity and non-uniqueness of the control function are shown. A solution to a specific control problem is given as an illustration.

We study the problem of identification and output tracking for uncertain strict-feedback systems where the unknown part of the system depends only on the output. The unknown nonlinear terms in the system description are not linearly parametrized, but it is assumed that the optimal parameters that characterize a neural net based approximation to the nonlinearities lie within a known compact set. The proposed controllers, with full state and derivative information, guarantee the boundedness of all signals in the closed-loop system. Also, at the expense of increased control effort, the output tracking error can be driven to an arbitrarily small neighborhood of the origin arbitrarily fast. Moreover, by specifying an appropriate reference signal, the satisfaction of a relevant persistency of excitation condition is ensured, and hence an adequate identification of the uncertain system is achieved.

The problem of output tracking is addressed in this article by a reformulation in terms of a two-point boundary value problem for the plant's zero dynamics, which is allowed to be unstable. On this basis, a hybrid feedback regulator ensuring output tracking is then designed. The robustness properties of the proposed regulator as well as connections with periodic (cheap) optimal control, output regulation, and possible extensions to the nonlinear setting are also discussed.

For linear time-varying single-input single-output systems, the paper treats the problem of designing an output feedback controller resulting in an (internally) stable closed-loop system that tracks a given reference signal. The design is accomplished by augmenting the given system to include an internal model of the reference signal. The internal model reduces to the usual form when the given plant is time invariant. An explicit construction for the internal model is given in the case of a step reference.

ABSTRACTIn this paper, we describe a constructive test to decide whether a given linear time-varying (LTV) differential system admits a stabilising compensator for the control tasks of tracking, disturbance rejection or model matching and construct and parametrise all of them if at least one exists. In analogy to the linear time-invariant (LTI) case, the ring of stable rational functions, noncommutative in the LTV situation, and the Kučera–Youla parametrisation play prominent parts in the theory. We transfer Blumthaler's thesis from the LTI to the LTV case and sharpen, complete and simplify the corresponding results in the book ‘Linear Time-Varying Systems’ by Bourlès and Marinescu.

Periodic Motions for Estimation of the Attraction Domain in the Wheeled Robot Stabilization Problem

In this paper, an effective method is proposed called the “Dynamic Routh’s stability Criterion (DRSC)”, which is developed using the Dynamic Pole Motion (DPM) approach (DPMA). This innovative technique extends the classical Routh’s stability criterion—traditionally limited to linear time-invariant (LTI) systems—to encompass a broader range of systems, including linear time-varying and nonlinear dynamic systems. By incorporating the behavior of pole trajectories over time, the proposed method offers a more comprehensive framework for analyzing system stability in more complex and realistic scenarios. DPM approach describes the notion of dynamic poles (DP) in a three dimensional ‘g-plane’, which is an extension to the two dimensional ‘s-plane’. The s-domain approach is based upon Laplace transform and has been used for linear time-invariant (LTI) system only. This novel g-plane framework is versatile enough to be applied to both linear and nonlinear dynamic systems. For the stability of dynamic systems, the DP must present in the left-hand side (LHS) of the g-plane. For nonlinear systems, the locations of DP are the function of system states; and these systems states are the function of initial conditions with amplitude and frequency (AnF) of input signals. Therefore, in nonlinear systems, stability is influenced not only by the initial conditions but also by the AnF of the input signals. For example, for given amplitude of the input signal, system may be unstable at low frequency; however, it may become stable at high frequency or vice-versa. These stability conditions are illustrated by several examples.

A technique for determining the dynamic response characteristics of time-varying linear systems is presented. A model composed of a parallel connection of filters whose impulse responses are orthogonalized exponential functions is used. The input to the system is fed to each of the filters. The coefficients of linear regression of the system output on each of the filter outputs are determined. Time-varying characteristics are measured by determining successively the coefficients from short samples of the input and output signals of the system. Many of the statistical properties of regression coefficients are known and can be used to estimate the length of sample of input and output signals required to determine the coefficients with given variance. The measurement technique has been implemented on a high speed digital computer. Results obtained by applying the technique to measurement of a variety of digitally simulated filters and to measurement of human pilot control characteristics are presented.

In this paper, new matrix inequality conditions on the terminal weighting matrices are proposed for linear continuous time-varying systems. Under these conditions, non-increasing and non-decreasing monotonicities of the saddle point value of a dynamic game are shown to be guaranteed. It is proved that the proposed terminal inequality conditions ensure the closed-loop stability of the receding horizon H/sub /spl infin// control (RHHC). The stabilizing RHHC guarantees an H/sub /spl infin// norm bound of the close-loop system. The proposed terminal inequality conditions for the monotonicity of the saddle point value and the closed-loop stability include most well-known existing terminal conditions as special cases. The results for time-invariant systems are obtained correspondingly from those in the time-varying case.

In this paper, adaptive optimal control is proposed for linear discrete time-varying (LDTV) systems subject to unknown system dynamics. The idea of the method is a direct application of the Q-learning adaptive dynamic programming for time-varying systems. In order to derive the optimal control policy, an actor-critic structure is constructed and the time-varying least square method is adopted for parameter adaptation. The derived control policy robustly stabilizes the time-varying system and guarantees an optimal control performance. As no particular system information is required throughout the process, the proposed method provides a feasible solution to a large variety of applications. The validity of the proposed method is verified through simulation studies.