Self-tuning minimum-variance regulators with adaptive determination of the model order

Analytical Determination of the Parameters of the Self-Tuning Circuit of the Traffic Control System on the Limit of Vibrational Stability

2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) ◽

10.1109/picst47496.2019.9061256 ◽

2019 ◽

Author(s):

Sergii Korotin ◽

Yurii Kravchenko ◽

Olena Starkova ◽

Kostiantyn Herasymenko ◽

Roman Mykolaichuk

Keyword(s):

Control System ◽

Traffic Control ◽

The Self ◽

Analytical Determination ◽

Self Tuning ◽

Traffic Control System ◽

Tuning Circuit

Download Full-text

Self-Tuning Two-Degree-of-Freedom PID Compensator Based on Two-Degree-of-Freedom Generalized Minimum Variance Control

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)41623-5 ◽

2001 ◽

Vol 34 (14) ◽

pp. 207-212 ◽

Cited By ~ 1

Author(s):

Takao Sato ◽

Akira Inoue ◽

Yoichi Hirashima

Keyword(s):

Minimum Variance ◽

Degree Of Freedom ◽

Minimum Variance Control ◽

Self Tuning ◽

Two Degree Of Freedom

Download Full-text

Design of Self-Tuning Regulator for Large-Scale Interconnected Hammerstein Systems

Journal of Control Science and Engineering ◽

10.1155/2016/6769714 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Mourad Elloumi ◽

Samira Kamoun

Keyword(s):

Large Scale ◽

Recursive Algorithm ◽

Minimum Variance ◽

Parametric Estimation ◽

Hammerstein Model ◽

Stochastic Nonlinear Systems ◽

Hydraulic Systems ◽

Self Tuning ◽

Variance Approach ◽

Error Method

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.

Download Full-text

Multivariate Distributions in Hydrology and River Regulation

Hydrology Research ◽

10.2166/nh.1976.0017 ◽

1976 ◽

Vol 7 (5) ◽

pp. 265-280

Author(s):

N.A. Kartvelishvili ◽

L.T. Gottschalk

Keyword(s):

Markov Process ◽

Finite Difference ◽

Difference Scheme ◽

River Runoff ◽

Finite Difference Scheme ◽

River Regulation ◽

Distribution Functions ◽

Multivariate Distributions ◽

Model Order

It is assumed that the river runoff process can be approximated by a Markov process. The process is thus described by M distribution functions: Fn (qt, t ; qt-1; t-1;…;qt-n, t-n), t ≡ 1, 2, …, M where M is the number of time intervals within the year, n - the order of the Markov process and qt, in general, is a vector representing runoff at several sites in a river or neighbouring rivers. Fundamental hypothesis of relations between multivariate distributions and corresponding marginal distributions is given. A finite difference scheme for multisite and multilag generation of river runoff is derived. The derivation is based on the multivariate normal distribution. Different methods for determination of the order of the finite difference scheme are discussed as well as the influence of model order and method of parameter estimation on properties of the model.

Download Full-text