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Input-output decoupling of nonlinear recursive systems

Ülle Kotta (2000)

Kybernetika

The input-output decoupling problem is studied for a class of recursive nonlinear systems (RNSs), i. e. for systems, modelled by higher order nonlinear difference equations, relating the input, the output and a finite number of their time shifts. The solution of the problem via regular static feedback known for discrete-time nonlinear systems in state space form, is extended to RNSs. Necessary and sufficient conditions for local solvability of the problem are proposed. This is the alternative to...

Input-to-state stability of neutral type systems

Michael I. Gil' (2013)

Discussiones Mathematicae, Differential Inclusions, Control and Optimization

We consider the system ẋ ( t ) - ∫ ₀ η d R ̃ ( τ ) ẋ ( t - τ ) = ∫ 0 η d R ( τ ) x ( t - τ ) + [ F x ] ( t ) + u ( t ) (ẋ(t) ≡ dx(t)/dt), where x(t) is the state, u(t) is the input, R(τ),R̃(τ) are matrix-valued functions, and F is a causal (Volterra) mapping. Such equations enable us to consider various classes of systems from the unified point of view. Explicit input-to-state stability conditions in terms of the L²-norm are derived. Our main tool is the norm estimates for the matrix resolvents, as well as estimates for fundamental solutions of the linear parts of the considered systems,...

Input-to-state stability with respect to measurement disturbances for one-dimensional systems

Nicolas Chung Siong Fah (2010)

ESAIM: Control, Optimisation and Calculus of Variations

We consider one-dimensional affine control systems. We show that if such a system is stabilizable by means of a continuous, time-invariant feedback, then it can be made input-to-state stable with respect to measurement disturbances, using a continuous, periodic time-varying feedback. We provide counter-examples showing that the result does not generally hold if we want the feedback to be time-invariant or if the control system is not supposed affine.

Integral control of infinite-dimensional systems in the presence of hysteresis: an input-output approach

Hartmut Logemann, Eugene P. Ryan, Ilya Shvartsman (2007)

ESAIM: Control, Optimisation and Calculus of Variations

This paper is concerned with integral control of systems with hysteresis. Using an input-output approach, it is shown that application of integral control to the series interconnection of either (a) a hysteretic input nonlinearity, an L2-stable, time-invariant linear system and a non-decreasing globally Lipschitz static output nonlinearity, or (b) an L2-stable, time-invariant linear system and a hysteretic output nonlinearity, guarantees, under certain assumptions, tracking of constant reference...

Integral equations and time varying linear systems.

Lucas Jódar (1986)

Stochastica

In this paper we study the resolution problem of an integral equation with operator valued kernel. We prove the equivalence between this equation and certain time varying linear operator system. Sufficient conditions for solving the problem and explicit expressions of the solutions are given.

Integrated Design of an Active Flow Control System Using a Time-Dependent Adjoint Method

E.J. Nielsen, W.T. Jones (2011)

Mathematical Modelling of Natural Phenomena

An exploratory study is performed to investigate the use of a time-dependent discrete adjoint methodology for design optimization of a high-lift wing configuration augmented with an active flow control system. The location and blowing parameters associated with a series of jet actuation orifices are used as design variables. In addition, a geometric parameterization scheme is developed to provide a compact set of design variables describing the wing...

Integrated design of observer based fault detection for a class of uncertain nonlinear systems

Wei Chen, Abdul Q. Khan, Muhammmad Abid, Steven X. Ding (2011)

International Journal of Applied Mathematics and Computer Science

Integrated design of observer based Fault Detection (FD) for a class of uncertain nonlinear systems with Lipschitz nonlinearities is studied. In the context of norm based residual evaluation, the residual generator and evaluator are designed together in an integrated form, and, based on it, a trade-off FD system is finally achieved in the sense that, for a given Fault Detection Rate (FDR), the False Alarm Rate (FAR) is minimized. A numerical example is given to illustrate the effectiveness of the...

Integrating Photosynthesis, Respiration, Biomass Partitioning, and Plant Growth: Developing a Microsoft Excel®-based Simulation Model of Wisconsin Fast Plant (Brassica rapa, Brassicaceae) Growth with Undergraduate Students

Y. L. Grossman, A. B. Berdanier, M. L. Custic, L. R. Feeley, S. F. Peake, A. J. Saenz, K. S. Sitton (2011)

Mathematical Modelling of Natural Phenomena

This paper demonstrates the development of a simple model of carbon flow during plant growth. The model was developed by six undergraduate students and their instructor as a project in a plant ecophysiology course. The paper describes the structure of the model including the equations that were used to implement it in Excel®, the plant growth experiments that were conducted to obtain information for parameterizing and testing the model, model performance, student responses to the modeling project,...

Intelligent decision-making system for autonomous robots

Zdzisław Kowalczuk, Michał Czubenko (2011)

International Journal of Applied Mathematics and Computer Science

The paper gives an account of research results concerning a project on creating a fully autonomous robotic decisionmaking system, able to interact with its environment and based on a mathematical model of human cognitive-behavioural psychology, with some key elements of personality psychology included. The principal idea of the paper is focused on the concept of needs, with a certain instrumental role of emotions.

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

Chunshien Li, Tai-Wei Chiang (2012)

International Journal of Applied Mathematics and Computer Science

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued...

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