Learning-based adaptive control and analysis for nonlinear systems with state-dependent constraint

Analysis of Nonlinear Systems with Stochastic Input and Stochastic Parameters—II

Nonlinear [formula omitted]-gain verification for nonlinear systems

Theory of nonlinear systems

Analysis of fast-sampled non-linear systems: generalised frequency response functions for δ-operator models

The identification of block-oriented systems represented by interconnections of linear dynamic systems and non-memory nonlinear elements have been of great importance for control and for analysis of biological nonlinear systems. In this study, two new algorithms to obtain structural models of nonlinear systems using numerically-given (experimentally obtained) Volterra kernels are proposed. One is to determine the parallel factorable base model; a sum of subsets, each of which has a parallel structure of linear dynamic systems whose outputs are multiplied. Another is called the Sm model; linear dynamic systems and nonlinear power elements in cascade. The results of numerical experiments showed that both methods could be practically useful for identifications and analysis of nonlinear systems.

We present recent advances towards an algebraic framework for the analysis and control of nonlinear systems with delays. These results are applied to obtain results regarding the notion of integrability. Finally, we apply these results to obtain a constructive characterization of the equivalence of a given system to the so-called triangular form.

An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive control in a unified framework, presenting the major results at a moderate mathematical level, suitable for MSc students and engineers with undergraduate degrees. Topics covered include introduction to nonlinear systems; state space models; describing functions for common nonlinear components; stability theory; feedback linearization; adaptive control; nonlinear observer design; backstepping design; disturbance rejection and output regulation; and control applications, including harmonic estimation and rejection in power distribution systems, observer and control design for circadian rhythms, and discrete-time implementation of continuous-time nonlinear control laws.

Theory of nonlinear control

In this work we study the design and analysis for nonlinear discrete time active control system with boring charter. It is shown that most analysis result for continuous time nonlinear system can be extended to the discrete time case. In previous studies, a method of nonlinear Model Following Control System (MFCS) was proposed by Okubo (1985). In this study, the method of nonlinear MFCS will be extended to nonlinear discrete time system with boring charter. Nonlinear systems which are dealt in this study have the property of norm constraints ║ƒ (v (k))║≤α + β║v (k)║^γ , where α≥0, β≥0, 0≤γ≤1. When 0≤γ<1. It is easy to extend the method to discrete time systems. But in the case γ = 1 discrete time systems, the proof becomes difficult. In this case, a new criterion is proposed to ensure that internal states are stable. We expect that this method will provide a useful tool in areas related to stability analysis and design for nonlinear discrete time systems as well.

On the application of diakoptics to nonlinear systems

SummaryAt the present time, there is a great deal of interest in the adaptive control of systems with rapidly varying parameters. Such problems arise in different forms and in many disciplines including finance, sociology, biology, and engineering. In general, these problems are intractable mathematically and the time variations have to be classified in some form to obtain rigorous results. In this paper, we consider the adaptive identification and control of linear systems with periodically varying parameters (referred to as linear time‐varying periodic (LTP) systems). The class of systems with known periodic parameters has been investigated widely since the pioneering work of Floquet and lends itself to rigorous mathematical analysis. However, very little was known until the early years of the last decade concerning the adaptive control of such systems. The problem was introduced by Xu and explored in detail by Narendra and Zhiling in 2006 and 2009, respectively. In 2012, a new method was introduced in adaptive control by Han and Narendra and is referred to as second‐level adaptation. In this paper, the adaptive identification and control of LTP systems using second‐level adaptation is discussed.

In this paper, we introduce a new type of Lyapunov functions in a general framework particularly suitable for the analysis and control of systems with noise and uncertainty. These Lyapunov functions may depend on parameters possibly satisfying differential equations. The main differences with respect to classical Lyapunov functions and classical tools for the design of composite Lyapunov functions are discussed through examples. A design tool for the design of composite filtered Lyapunov functions is given, and examples show improvements over existing literature.

Although nonlinear systems with a DC term are quite often found, very little work has been done about the important effects that the DC term produces on the nonlinear system response.

One traditional approach in the analysis and design of nonlinear control systems is a first order approximation by a linear system. A new approach is to use nonlinear change of coordinates and feedback to construct linear approximations that are accurate to second and higher orders. However, the algebraic calculations required to obtain these aproximations are somewhat lengthy. In this paper, the theoretical framework for finding such change of coordinates for a nonlinear system are described. A software package that symbolically solves these transformations is currently being prepared.

Describes the similarity between two nonlinear control systems, shown in a large number of physical systems of interest in many engineering applications. By utilising similar information, robust controllers for large-scale nonlinear similar composite systems with uncertain parameters are proposed. The results obtained show that the similar structure of composite systems possesses a holographic property and can simplify the analysis and design of nonlinear systems.