Krishna Busawon

Disturbance attenuation using proportional integral observers

In this paper, we first propose a proportional integral observer design for single-output uncertain linear systems which permit us to attenuate either measurement noise or modelling errors. We show that, when only sensor noise is present in the system, an integral observer alone suffices to achieve good convergence and filtering properties. On the other hand, when modelling errors and sensor noise are present, we show that, for some classes of linear systems, the proportional integral observer allows us to decouple completely the modelling uncertainties while keeping satisfactory convergence properties. A comparison of the classical proportional observer to the proposed observers are given via academic simulation examples. We next extend the design to the class of single-output uniformly observable non-linear systems. We show through a practical simulation example, dealing with a flexible joint robot, that the non-linear proportional integral has very satisfactory disturbance attenuating properties.

Observer design for a special class of nonlinear systems

In this paper, we present the design of an observer for a special class of nonlinear systems satisfying some regularity assumptions in both the single-output and multioutput case. The controlled part of the considered systems have a triangular structure. A further extension of the design strategy is made to a specific class of multioutput systems which assume stronger nonlinear dependence between the various subsystems. The design is illustrated by a simulation experiment dealing with rotor flux estimation in an induction motor.

Observer design based on triangular form generated by injective map

A procedure is provided for constructing an injective map as a generalized coordinate change for triangular form transformation. Based on the triangular form a method is developed for designing nonlinear state observers.

New algorithm for observer error linearization with a diffeomorphism on the outputs

In this paper, we give the necessary and sufficient conditions that guarantee the existence of a diffeomorphism which allows one to transform a multi-output nonlinear dynamical system into a normal observable canonical form. In particular, we propose an algorithm that permits one to derive such diffeomorphism. The main feature of the canonical form is that it is obtained by allowing a diffeomorphism on the outputs and it also allows one to design an observer with linear error dynamics. We first consider multi-output nonlinear dynamical system without inputs. We then extend the results obtained to multi-output nonlinear dynamical system with inputs.

High Gain Observer for Structured Multi-Output Nonlinear Systems

In this note, we present two system structures that characterize classes of multi-input multi-output uniformly observable systems. The first structure is decomposable into a linear and a nonlinear part while the second takes a more general form. It is shown that the second system structure, being more general, contains several system structures that are available in the literature. Two high gain observer design methodologies are presented for both structures and their distinct features are highlighted.

High-order sliding mode control of a DC motor drive via a switched controlled multi-cellular converter

In this article, we present a high-order sliding mode controller of a DC motor drive connected to a multi-cellular converter. More specifically, we design a second-order (super-twisting) control algorithm for the speed regulation of a DC motor. For this, a switching control for the multi-cellular converter is derived in order to supply the correct reference value for the speed regulation. A practical implementation of the controller is realised using a laboratory set-up. The performance and the validity of the controller are shown experimentally.

A Multivariable Optimal Energy Management Strategy for Standalone DC Microgrids

Due to substantial generation and demand fluctuations in standalone green microgrids, energy management strategies are becoming essential for the power sharing and voltage regulation purposes. The classical energy management strategies employ the maximum power point tracking (MPPT) algorithms and rely on batteries in case of possible excess or deficit of energy. However, in order to realize constant current-constant voltage (IU) charging regime and increase the life span of batteries, energy management strategies require being more flexible with the power curtailment feature. In this paper, a coordinated and multivariable energy management strategy is proposed that employs a wind turbine and a photovoltaic array of a standalone DC microgrid as controllable generators by adjusting the pitch angle and the switching duty cycles. The proposed strategy is developed as an online nonlinear model predictive control (NMPC) algorithm. Applying to a sample standalone dc microgrid, the developed controller realizes the IU regime for charging the battery bank. The variable load demands are also shared accurately between generators in proportion to their ratings. Moreover, the DC bus voltage is regulated within a predefined range, as a design parameter.

On the transformation of nonlinear dynamical systems into the extended nonlinear observable canonical form

This article deals with the transformation of a class of nonlinear systems into an extended nonlinear observable canonical form (ENOCF) by dynamic extension. The dynamic extension is obtained by adding auxiliary dynamics and virtual outputs to the original system, thus giving rise to a higher dimensional system in an extended state space. Sufficient geometrical conditions to guarantee the existence of a local diffeomorphism which allow the transformation of the extended system into the ENOCF are given. In particular, an algorithm that permits us to compute such a diffeomorphism is derived.

On the design of integral and proportional integral observers

This paper deals with the design of integral and proportional integral observers for linear uncertain systems. It is shown that, when only sensor noise is present in the system, integral observers permit to achieve good convergence and filtering properties. On the other hand, when modeling errors and sensor noise are present, it is shown that, for some classes of systems, the proportional integral observer allows to decouple completely the modeling uncertainties while keeping satisfactory convergence properties. A comparison of the classical proportional observer to the proposed observers are given via simulation examples.

Estimation of the power coefficient in a wind conversion system

In this paper, we present the design of an observer/estimator for the estimation of the power coefficient in a wind energy conversion system (WECS) based on a separately excited dc generator. It is shown that the estimator is capable of supplying accurate estimates of the power coefficient, and that it can handle measurement noise and provide satisfactory convergence qualities. A further advantage of the observer design presented is that it can be easily extended to other WECS where different types of generators are employed.