Transactions of the Institute of Systems, Control and Information Engineers

Recursive Subspace-based Identification Algorithm Using Fixed Input-Output Data

Kentaro KAMEYAMA, Yuji MATSUURA, Akira OHSUMI

2004 Volume 17 Issue 12 Pages 517-527
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.517

JOURNAL FREE ACCESS

Show abstractHide abstract

A recursive algorithm is derived for subspace system identification which is flexible and applicable to continuous/discrete-time, time-invariant/varying stochastic systems. Efficacy of the algorithm is demonstrated by simulations, comparing with another proposed algorithm.

View full abstract

Download PDF (1584K)
A New Approach to Parameter Tuning of Controllers by Using One-Shot Experimental Data

A Proposal of Fictitious Reference Iterative Tuning

Shotaro SOMA, Osamu KANEKO, Takao FUJII

2004 Volume 17 Issue 12 Pages 528-536
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.528

JOURNAL FREE ACCESS

Show abstractHide abstract

In this paper, we propose one of the powerful iterative tuning methods for the parameters of the controller of a closed loop system. The method we provide here requires only one-shot experiment and then an iterative non-linear optimization for obtaining the optimum parameters of the controller can be performed off-line by using the fictitious reference signal computed by the first experimental data (from this reason, we refer to our method as “fictitious reference iterative tuning”, which is abbreviated to “FRIT”). The main concept and the algorithm of FRIT are provided as our main results of this paper. Moreover, we give a control experiment of the cart-system in order to show the validity of FRIT.

View full abstract

Download PDF (1449K)
Parameter Tuning of a Controller Including Multiple PID Elements by Using IFT Method

Masashi NAKAMOTO

2004 Volume 17 Issue 12 Pages 537-543
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.537

JOURNAL FREE ACCESS

Show abstractHide abstract

Iterative feedback tuning is a method to find control parameters by iteratively carring out control experiments. In this paper the IFT method is applied to adjust control parameters of multiple PID controllers and an I PD controller. Since an input signal for an integral element of an I-PD controller is different from a proportional and derivative element, parameter tuning by the IFT method has to do by the similar way for a multivariable controller. Some test signal should be added to a manipulated signal in order to apply the IFT method for a general multivariable system. It is shown that all test can be performed by setpoint response in cases of parameter tuning for an I-PD controller or multiple PID controllers. Experimental tests are carried out and show properties and results of the IFT method.

View full abstract

Download PDF (967K)
Analysis of Generalization Error of Ensemble Learning Through Linear Weak Learner

Kazuyuki HARA, Masato OKADA

2004 Volume 17 Issue 12 Pages 544-552
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.544

JOURNAL FREE ACCESS

Show abstractHide abstract

Generalization ability of the ensemble learning through linear simple perceptron is analyzed by using the stochastic mechanics method. The element of the initial connection weight is given by a Gaussian random number so that the ensemble learning is performed effectively. To analyze the effect of number of the perceptrons used in the ensemble learning, linear simple perceptrons are taken into account. Additive noise for the teacher's output or student's output is considered. Each element of teacher and student networks are considered to be initialized by Gaussian distibution of zero mean and unit valiance. From the analytical results, we found that in the limit of K is infinity, the generalization error of ensemble learning with the K linear perceptrons are a half of that of the single perceptron. For finite number of perceptrons, the generalization error converges to the value of a half of the single perceptron's one with 1/K when no noise is added. Asymptotic property of the ensemble learning is not effective for teacher's output noise, however, from analysis of dynamics of the generalization error, it is found that the ensemble learning is effective at the early stage of the learning.

View full abstract

Download PDF (1332K)
On Periodic Motion of Linear Systems with State Jump

Modelling, Stability Analysis and Feedback Control of Compass Walking

Kentaro HIRATA, Hideki KOKAME

2004 Volume 17 Issue 12 Pages 553-560
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.553

JOURNAL FREE ACCESS

Show abstractHide abstract

In this paper, we consider the stability of periodic solution of linear systems with state jump. While this model is general enough to describe the complex dynamics of a class of hybrid systems including the (approximated) biped passive compass walking, it also retains the tractability for the analytic mathematical treatment. With an appropriate definition of the stability, we can justify the use of Poincare map in the analysis. The formula for the linearized Poincare map, which determines the local stability of the periodic solution, is given. The effect of feedback control (for the case that the nominal trajectory is known) is also discussed.

View full abstract

Download PDF (1156K)
Accurate Analysis of Water Pipelines Considering Pressure-dependency of Outflows

New Modeling of Outflows along Pipes

Akihiko UDO, Akira YAMASHITA

2004 Volume 17 Issue 12 Pages 561-563
Published: December 15, 2004
Released on J-STAGE: October 13, 2011

DOIhttps://doi.org/10.5687/iscie.17.561

JOURNAL FREE ACCESS

Download PDF (417K)

Register with J-STAGE for free!