Ivo Petráš

Analogue Realizations of Fractional-Order Controllers

An approach to the design of analogue circuits, implementingfractional-order controllers, is presented. The suggestedapproach is based on the use of continued fraction expansions;in the case of negative coefficients in a continued fractionexpansion, the use of negative impedance converters is proposed.Several possible methods for obtaining suitable rational appromixationsand continued fraction expansions are discussed. An exampleof realization of a fractional-order Iλ controlleris presented and illustrated by obtained measurements.The suggested approach can be used for the control of veryfast processes, where the use of digital controllers isdifficult or impossible.

Fractional order control - A tutorial

Many real dynamic systems are better characterized using a non-integer order dynamic model based on fractional calculus or, differentiation or integration of non-integer order. Traditional calculus is based on integer order differentiation and integration. The concept of fractional calculus has tremendous potential to change the way we see, model, and control the nature around us. Denying fractional derivatives is like saying that zero, fractional, or irrational numbers do not exist. In this paper, we offer a tutorial on fractional calculus in controls. Basic definitions of fractional calculus, fractional order dynamic systems and controls are presented first. Then, fractional order PID controllers are introduced which may make fractional order controllers ubiquitous in industry. Additionally, several typical known fractional order controllers are introduced and commented. Numerical methods for simulating fractional order systems are given in detail so that a beginner can get started quickly. Discretization techniques for fractional order operators are introduced in some details too. Both digital and analog realization methods of fractional order operators are introduced. Finally, remarks on future research efforts in fractional order control are given.

Fractional order systems : modeling and control applications

Fractional Order Systems Fractional Order PID Controller Chaotic Fractional Order Systems Field Programmable Gate Array, Microcontroller and Field Programmable Analog Array Implementation Switched Capacitor and Integrated Circuit Design Modeling of Ionic Polymeric Metal Composite

Two direct Tustin discretization methods for fractional-order differentiator/integrator

This paper deals with fractional calculus and its approximate discretization. Two direct discretization methods useful in control and digital filtering are presented for discretizing the fractional-order differentiator or integrator. Detailed mathematical formulae and tables are given. An illustrative example is presented to show the practically usefulness of the two proposed discretization schemes. Comparative remarks between the two methods are also given.

Using Fractional Order Adjustment Rules and Fractional Order Reference Models in Model-Reference Adaptive Control

This paper investigates the use of Fractional Order Calculus (FOC) inconventional Model Reference Adaptive Control (MRAC) systems. Twomodifications to the conventional MRAC are presented, i.e., the use offractional order parameter adjustment rule and the employment offractional order reference model. Through examples, benefits from theuse of FOC are illustrated together with some remarks for furtherresearch.

Fractional-Order Memristor-Based Chua's Circuit

This express brief deals with the memristor-based Chuas circuit. For the first time, the fractional-order model for such system is presented. A numerical solution of the fractional-order memristor-based Chuas equations is derived for simulations. The dynamical behavior and stability analysis of this system are described and investigated as well.

Modelling heat transfer in heterogeneous media using fractional calculus

This paper presents the results of modelling the heat transfer process in heterogeneous media with the assumption that part of the heat flux is dispersed in the air around the beam. The heat transfer process in a solid material (beam) can be described by an integer order partial differential equation. However, in heterogeneous media, it can be described by a sub- or hyperdiffusion equation which results in a fractional order partial differential equation. Taking into consideration that part of the heat flux is dispersed into the neighbouring environment we additionally modify the main relation between heat flux and the temperature, and we obtain in this case the heat transfer equation in a new form. This leads to the transfer function that describes the dependency between the heat flux at the beginning of the beam and the temperature at a given distance. This article also presents the experimental results of modelling real plant in the frequency domain based on the obtained transfer function.

Fractional-Order Systems

A general fractional-order system can be described by a fractional differential equation of the form

A Note on the Fractional-Order Cellular Neural Networks

{a_n}{D^{{\alpha _n}}}y(t) + {a_{n - 1}}{D^{{\alpha _{n - 1}}}}y(t) + \cdot \cdot \cdot + {a_0}{D^{{\alpha _0}}}y(t) = {b_m}{D^{{\beta _m}}}u(t) + {b_{m - 1}}{D^{{\beta _{m - 1}}}}u(t) + \cdot \cdot \cdot + {b_0}{D^{{\beta _0}}}u(t),