Pál Michelberger

SVD-based complexity reduction to TS fuzzy models

One of the typical important criteria to be considered in real-time control applications is the computational complexity of the controllers, observers, and models applied. In this paper, a singular value decomposition (SVD)-based complexity reduction technique is proposed for Takagi Sugeno (TS) fuzzy models. The main motivation is that the TS fuzzy model has exponentially growing computational complexity with the improvement of its approximation property through, as usually practiced, increasing the density of antecedent terms. The reduction technique proposed here is capable of defining the contribution of each local linear model included in the TS fuzzy model, which serves to remove the weakly contributing ones as according to a given threshold. Reducing the number of models leads directly to the computational complexity reduction. This work also includes a number of numerical and application examples.

TP model transformation in non-linear system control

The main objective of the paper is to study the recently proposed tensor product distributed compensation (TPDC) based design framework in the case of observer and controller design. The TPDC links the TP model transformation and the parallel distributed compensation (PDC) framework. The study is conducted trough the output feedback control design of the prototypical aeroelastic wing section that is used for the theoretical as well as experimental analysis of two-dimensional aeroelastic behavior and exhibits limit cycle oscillation without control effort. The control strategy is based on a state feedback controller and an observer to estimate the available state values.

Dynamic Modelling Of Commercial Road Vehicle Structures From Test Data

The paper discusses the identification of multivariable models for air suspension bus structures using the measured stress response arising from stochastic road profile excitation. The vehicle and noise transfer matrices are identified from the time history of measured data. Applying a special parametrization of the transfer matrices, the modal characteristics can also be directly computed. Models were identified at various selected road categories, road profile variances and speed levels, thus the random vibration analysis of the vehicle can be performed for the whole range of operation. The identified models can be used to determine the global dynamic characteristics of the vehicle for arbitrary road profile excitations and speed levels within the examined range.

HOSVD based computational complexity reduction of TS fuzzy models

One of the usual important criteria to be considered in real time control applications is the computational complexity of the controllers, observers and models applied. In this paper a higher order singular value decomposition (HOSVD) based complexity reduction technique is proposed to Takagi Sugeno (TS) fuzzy models, which is capable of defining the contribution of each local linear model included in the TS fuzzy model. This helps us with discarding the weakly contributing ones according to a given threshold. Reducing the number of models leads directly to the computational complexity reduction.

Multivariable Identification for Commercial Vehicle Stress Analysis

This paper presents the application of multivariable identification methods to obtain models for commercial road vehicles from measured stress responses arising in certain structural elements due to stochastic road excitation. The elementary subystem representation of the system transfer matrix allowing an effective structure and parameter estimation is used to model the vehicle and noise dynamics. Measurements were carried out on a laminated spring suspension bus, and models were indentified atvarious road profile variances and speed levels of the vehicle.

An adaption technique to SVD reduced rule bases

The practically non-universal approximation property, shown by Tikk et al. and the exponential complexity problem of widely adopted fuzzy logic techniques, shown by Koczy and Hirota, reveal the contradictions features of fuzzy rule bases in pursuing of good approximation. As a result complexity reduction topic emerged in fuzzy theory. One of the natural disadvantages of using complexity reduction is that the adaptivity property of the reduced approximation becomes strictly restricted. This paper proposes a technique, to singular value decomposition (SVD) based reduction, which may alleviate the adaptivity restriction. A high order tensor projection is proposed here as key idea.

Identification of Bus Dynamics from Test Data

Abstract The paper discusses the identification of multivariable models for vehicle structures excited by stochastic road profiles. A time domain approach is used to the identification of the transfer matrix and the modal characteristics /natural frequencies and eigenvaluesl from accelerations and stress measurements. It will be shown that the application of ESS-representation arise naturally in modelling vibrating structures. The methods are u6ed to identify models of an air suspension bus for various speed levels and road profile variances

Polytopic Decomposition of the Linear Parameter-varying Model of the Parallel-type Double inverted Pendulum

Most of the formal controller synthesis approaches require conditions like controllability, observability of the systems, however it is still difficult to verify these properties in general. In case of polytopic models these conditions and the feasibility of the linear matrix inequalities (LMI) based design strongly depends on the weighting of the LTI vertex components of the polytopic model. With tensor product (TP) model transformation it is possible to decompose linear parameter-varying (LPV) models into polytopic forms in various ways and also provides the appropriate weighting functions. The objective of this paper is to use this decomposition on the the parallel-type double inverted pendulum control problem.

ITERATIVE IDENTIFICATION AND CONTROL DESIGN FOR UNCERTAIN PARAMETER SUSPENSION SYSTEM

Abstract This paper presents a novel model based controller design method for vehicle suspension system. The combined frame of the process identification and control design requires the conformity between the control law and the identification criterion. This demand leads to an iterative controller design method, namely the identification respects the control law’s attitude and the control design respects the identified model. This paper presents how the identification is performed taking the control law into account, moreover the control design is based on the reduced complexity identified model and its uncertainties instead of the actual plant For practical application of the theoretical result the design of the active suspension system is demonstrated based on the quarter car model.

Determination of mass, damping and stiffness matrices using structural and parametric identification of linear vehicle frame models

SUMMARY The paper deals with some important problems of determination of mass, damping and stiffness matrices using identification of linear vehicle dynamic models. The body of the vehicle is considered as distributed parameter system wich can be described by partial differential equations and the parameters represent mass, stiffness and damping distributions. Using finite elements method the body dynamics can be modelled as concentrated parameter system given by second order vector differential equations. For this case the directly identified parameters of discrete models are sophisticated functions of physical parameters of continuous models. Therefore, it is very important to choose the suitable discrete model class for an effective and reliable solution of the above problem. In the paper special time-domain maximum likelihood method is applied to estimate the coefficients in the transfer matrix of the body and to ensure a direct relationship between the transfer matrix, the modal and physical paramete...