Setsuo Sagara

Parameter Identification and Adaptive Control of Continuous Systems with Zero-Order Hold

Abstract This paper discusses the digital implementation techniques of parameter identification and adaptive control for continuous systems with zero-order hold (ZOH) focusing on the bilinear transformation based on the block pulse functions (BPFs). It is found that the emphasized discretizing method yields very excellent performances of parameter identification and adaptive control even in the case of large sampling intervals.

Impulse Response Identification of Continuous Systems using Generalized Radial Basis Function Networks

Abstract In this paper, the authors propose a new identification method for the continuous-time impulse response model (CTIRM) of a linear system from sampled data in the ill-posed cases of band-limited input, fast sampling rate and measurement noise of high level which may be colored. To achieve smooth estimate, the CTIR which is viewed as a continuous function of time, is approximated by a generalized radial basis function network (GRBFN) in which the gaussian basis functions are equidistantly located in the time-domain and have the same width. Then the parameters of the GRBFN are estimated by the least-squares (LS) method. The design techniques of the center distance and the basis function width axe also shown using frequency-domain analysis. Simulation results are included to verify the performance of the proposed method.

System impulse response identification using a multiresolution neural network

A pressure-vacuum cap for a chamber having a filler neck, the cap comprising a housing proportioned and designed to engage and close the filler neck. The housing provides a passageway extending axially therethrough in communication with the filler neck and, intermediate the ends of the passageway, a concentric annular outwardly facing seat and a concentric annular inwardly facing seat. A gasket closes the passageway, the gasket having an outer peripheral portion in sealing engagement with one of said seats and an inner peripheral portion in sealing engagement with the other of said seats. Springs yieldably urge the gasket into sealing engagement with the seats. The springs are calibrated such that the gasket serves as a two-way valve for normalizing the pressure in such a chamber, venting the chamber to atmosphere when the pressure in the chamber exceeds a predetermined superatmospheric level and when the pressure in the chamber drops below a predetermined subatmospheric level.

Identification of Parameters and Time Delays of Continuous Systems using the Genetic Algorithm

Abstract This report proposes a novel method of identification of continuous time-delay systems from sampled inputoutput data. By the aid of a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sampling period. Then an identification method combining the common linear least squares (LS) method or the instrumental variable (IV) method with the genetic algorithm (GA) is proposed. That is, the time-delay is selected by the GA, and the system parameters are estimated by the LS or IV method. Furthermore, the proposed method is extended to the case of multi-input multi-output(MIMO) systems where the time-delays in the individual input channels may differ each other. Simulation results show that our method yields accurate estimates even in the presence of high measurement noises.

Subspace Model Identification for Continuous-Time Systems

Abstract Usually, the subspace-based state-space system identification algorithms are focused on discrete-time models, which may cause some numerical problems when the sampling interval is small. This paper proposes an algorithm of subspace-based state-space system identification for continuous-time systems from sampled input-output data. The ω — operator ω = ( p - α)/( p + α) where p denotes a differential operator and α > 0, is introduced to avoid direct numerical differentiations. And the ω — operator state-space model identified by the 4SID method can be transformed back to the common continuous-time state-space model. The numerical superiority of the ω — operator approach compared to some other methods is verified through simulation study.

Integral-equation approach to parameter identification without consideration of initial conditions

The widely used integral-equation approach for identification of continuous systems is investigated. Attention is focused on the initial condition problem which has been puzzling many researchers. A new calculation procedure for the multiple integrations of the system signal derivatives is proposed and a new integral-equation approach is proposed for which the initial conditions need not be identified as unknown parameters. Therefore, the burden of the identification algorithms can be greatly reduced compared to conventional methods. Discussions on the unification of the integral-equation approach with the other methods are also provided.

Impulse response identification using multiresolution analysis

Abstract In this paper, the authors propose a new identification method for the discrete-time impulse response model of a linear system from sampled input-output data using multiresolution analysis theory. The continuous-time impulse response which is viewed as a L2(R) function of time, is approximated by scaling and wavelet functIons which are shifted and dilated based on the multiresolution analysis theory. Hence the system under study can be viewed as weighted summation of a group of subsystems in which the shifted and dilated scaling functions and wavelet functions are interpretated as then impulse responses respectively. Then the genetic algorithm and AIC are introduced to select significant subsystems such that only moderate parameters are required to estimate in contrast to the conventional method. It IS shown that the proposed method yields accurate estimate of the impulse response with locally rapidly changing components, even In the ill-posed cases of band-limited input, fast sampling rate and significant measurement noise.

TLS Method for Pulse Transfer Function Estimation in the Presence of Input and Output Noise

Abstract Total least squares (TLS) is a method of solving sets of linear equations Ax = b, where both the observation vector b and the data matrix A are inaccurate. In this paper, it is shown that the parameter estimation problem in the presence of input and output noise can be formulated as a TLS problem. In addition, an algebraic equivalence between the TLS method and the Koopmans-Levin method is proven. A comparison of the TLS method to the bias compensated least-squares method is also made.