Probing signals for output constrained processes

Probing input signal design for enhancing the identification of process parameters is developed for scalar input-output processes with output constraints. The sensitivity approach is used and the problem is formulated as an open loop optimal control problem. The solution is via integer programming. Numerical examples illustrate the results.     

Tracking and parameter identification for model reference adaptive control

We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.     

A model reference adaptive technique of identification for discrete systems

A rapidly convergent algorithm based on a model reference adaptive technique for the identification of the unknown parameters of a noise free discrete dynamical system is presented. This algorithm is based on a geometric interpretation of the Kudva-Narendra scheme. The algorithm does not require any a priori information about the system. An example is included to demonstrate the strength of the algorithm.     

Constrained dynamical systems,robust model reference adaptive control,and unreliable reference signals

ABSTRACT

In this paper, we address the robust control design problem for nonlinear dynamical systems tracking unreliable reference signals. Specifically, we present robust model reference adaptive control laws that guarantee uniform ultimate boundedness of the trajectory tracking error for nonlinear plants that are affected by matched, unmatched, and parametric uncertainties, and are subject to constraints on the state space and the measured output. These control laws guarantee satisfactory results even in case the reference trajectory or the reference output signal do not verify the given constraints and hence, may draw the plant's trajectory or measured output outside their constraint sets. A numerical example involving the attitude control of a spacecraft illustrates the feasibility of the theoretical results presented.     

An output error identification interpretation of model reference adaptive control

The task of discrete model reference adaptive control is manipulated into an output error identification problem. Strictly causal output error identifiers recently developed as adaptive recursive filters are therefore proposed for unique provision of a model reference adaptive control algorithm relying only on input-output data.     

基于系统辨识的模型参考自适应控制

介绍了荷兰Twente大学控制工程系研发的先进控制实验平台的基本组成、功能和基于机电一体化设计软件20-sim。以先进控制演示仪为研究对象,在20-sim环境下建立了系统的图标化数学模型,并从理论上对模型作了降阶分析。根据系统辨识的模型参考自适应控制（MRAC）原理,编制了模型参考自适应和Lyapunov求解模块。采用线性二次型调节器为控制模块,构成了完整的先进控制演示仪的MRAC控制系统。该系统仿真结果显示所建立的模型针对性强,可用于各种先进控制策略的仿真研究。     

Optimal levels of perturbation signals for nonlinear system identification

A method is developed for determining the optimal levels of multilevel perturbation signals for nonlinear system identification, using condition numbers of submatrices of the Vandermonde matrix of the input levels vector. It is applicable when the perturbation signal is applied directly to a static nonlinearity. Optimal levels can be obtained for every order of nonlinearity less than the number of levels, and in most cases the optimal levels are not all distinct. The results show that there is no advantage in using signals with more than the minimum necessary number of distinct levels, although it may be advantageous if some of the distinct levels appear more than once in the input levels vector. The optimal levels are unchanged by multiple occurrences of every level of the input levels vector during a measurement period, and they are shown to be the global optima for pseudorandom perturbation signals derived from maximum-length sequences, in which the occurrence of the zero level is one less than the occurrences of the other levels during a period.     

A model reference approach to safe controller changes in iterative identification and control

A controller change from a current controller which stabilises the plant to a new controller, designed on the basis of an approximate model of the plant and with guaranteed bounds on the stability properties of the true closed loop, is called a safe controller change. In this paper, we present a model reference approach to the determination of safe controller changes on the basis of approximate closed loop models of the plant and robust stability results in the ν-gap.     

An Addendum to "Unbiased recursive identification using model reference adaptive techniques"

It is shown in this correspondence that for recursive identifiers using "parallel" model reference adaptive systems the sufficient stability conditions which can be established depend on the rate of decrease of the adaptation gains. This result generalizes and corrects the result given in [I].     

MMSE channel estimation for two-port demodulation reference signals in new radio

Dear editor, Orthogonal frequency division multiplexing (OFDM) has been adopted in new radio (NR)[1],and multiple-port de-modulation reference signal (DMRS) has...     

Asymptotic tracking control for a class of reference signals for linear differential inclusions

Tracking control for linear differential inclusions with disturbances is investigated in this paper. Based on the convex hull Lyapunov function, a feedback control law is designed such that the output of the closed-loop system without disturbances globally asymptotically tracks the desired reference signal. Under the bounded disturbances, the tracking error is bounded. An example is given to illustrate how to make the output of the closed-loop system track a sinusoidal signal, a cosine signal and a circular signal. The simulation shows the effectiveness of the proposed method.     

Nonparametric algorithm for input signals identification in static distributed-parameter systems

In this correspondence, a nonparametric algorithm for identification of input signals in linear, static distributed-parameter systems is proposed and investigated. Integral mean-square convergence of the algorithm is proved for an infinite number of point measurements of the system state. The algorithm is a generalized version of the one recently proposed by Rutkowski [10] for nonparametric function fitting, and in a common area, the presented results are complementary.     

Simple method for model reference adaptive control

A simple method is presented for combined signal synthesis and parameter adaptation within the framework of model reference adaptive control theory. The results are obtained using a simple derivation based on an improved Lyapunov function.     

A reference model for requirements and specifications

The authors define a reference model for applying formal methods to the development of user requirements and their reduction to a behavioral system specification. The approach focuses on the shared phenomena that define the interface between the system and the environment     

A reference model for object data management

This article describes a proposed reference model that provides a conceptual framework intended to facilitate the development of more concrete standards in the area of object-oriented databases. This reference model is being developed by the Object-Oriented Databases Task Group (OODBTG). OODBTG is a subcommittee under the ANSI ASC/X3 body, and is charted to develop a reference model and recommendations which will guide future ANSI efforts to develop more concrete standards in the area of object-oriented software technology. Reprinted here is the May 1990 working draft of the reference model, as distributed at that time for public review and comment.     

Refining visualization reference model for context information

Context-awareness can be used to decrease the need for interaction with a mobile device. This is increasingly important since the functionality of mobile devices and personal digital assistants gets more and more complex while the input and output capabilities remain restricted. An important aspect of context-awareness is to present the current context to the user. We propose a model for visualizing contextual information on the mobile terminal screen. The model is a refinement of a well-known visualization reference model; it takes into account the specific characteristics of mobile use and context information. We present the design of the model in detail, and discuss its applicability for a variety of contexts and tasks by providing a full-fledged use case.     

System identification with multi-level periodic perturbation signals

Multi-level periodic perturbation signals for system identification in the frequency domain are defined. The design and properties of these signals, and the output signal processing methods used with them to reduce or eliminate the effects of noise and nonlinearity, are developed. Both pseudo-random and multi-frequency perturbation signals are considered, and an application in which these signals were used to identify the dynamics of steel strip in an industrial process is described. It is concluded that the use of these signals leads to considerable benefits, and that they are to be preferred for identification schemes of this kind.     

Frequency domain system identification using arbitrary signals

It is the common conviction that frequency domain system identification suffers from the drawback that it cannot handle arbitrary signals without introducing systematic errors. This paper shows that it is possible to deal with nonperiodic signals without any approximation and under the same assumptions as in the time domain, by estimating simultaneously some initial conditions and the system model parameters