Novel closed-loop identification algorithm based on the finite-dimensional signal subspace

Usually, when parameter identification is applied, there are some gains related to the identification algorithm whose value must be carefully adjusted in order to obtain a good performance of the algorithm. However, when performing closed loop identification, there are some other constants that in general are not taken into account for the identification algorithm: the controller gains, which may appear inside the identification algorithm, specifically in the regressor vector, which is very important for the parameter convergence according to the persistence of excitation condition. Therefore, the effect of these gains on the estimated parameters should be analyzed so that better estimates can be obtained. This paper addresses the behavior of the parameter estimates for a closed-loop identification methodology applied to a DC servomechanism with a bounded perturbation signal and a PD controller. It is shown that, with this perturbation, the parameter estimates converge to a region whose size can be modified not only by varying the identification algorithm gains but also by modifying the P and D controller gains in a suitable way.

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Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

Abstract and Applied Analysis ◽

10.1155/2014/869879 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Xiaosuo Luo ◽

Yongduan Song

Keyword(s):

Predictive Control ◽

Closed Loop ◽

Control Method ◽

Negative Impact ◽

Data Driven ◽

Recursive Identification ◽

Subspace Identification ◽

Identification Algorithm ◽

Closed Loop Identification ◽

Adaptive Predictive Control

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

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Analysis of the controller and identification algorithm gains for closed-loop identification applied to a perturbed DC servomechanism working under PD control

2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control ◽

10.1109/iceee.2011.6106706 ◽

2011 ◽

Author(s):

R. Miranda ◽

F. Ochoa

Keyword(s):

Closed Loop ◽

Identification Algorithm ◽

Pd Control ◽

Closed Loop Identification

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Closed Loop Subspace Identification and Control of a two input-Single output Conical Tank System

10.20944/preprints202008.0401.v1 ◽

2020 ◽

Author(s):

Sudhahar S ◽

Ganesh Babu C ◽

Sharmila D

Keyword(s):

System Identification ◽

Process Model ◽

Closed Loop ◽

Mimo System ◽

Open Loop ◽

Identification Algorithm ◽

Process Data ◽

Single Output ◽

Closed Loop Identification ◽

Tank System

The process model is very essential for the model based control design. The model of the process can be identified using system identification algorithm. The system identification is done through the open loop and closed loop approaches. In this work, the lab scale conical tank setup configured as a non square MIMO system. The conical tank system is identified through the both appraoches, the effectiveness and need of the both approaces are discussed. Based on the open loop identified model the controller designed and the controller implemented in the real time the to record the process data. From this data the closed loop identification are conducted uisng N4SID algorithm. The controller seetings are obtained using the smith predcitor based IMC based PI controller for the obtained model. The proposed identification algorithm and controller tuning show the better reults over the conventional method. Moreover, this method is applicable for all the non square MIMO system.

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Autotuning with multiharmonic perturbations. I. Nonparametric closed loop identification

UKACC International Conference on Control (CONTROL '98) ◽

10.1049/cp:19980425 ◽

1998 ◽

Author(s):

P.D. Fox

Keyword(s):

Closed Loop ◽

Closed Loop Identification

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Closed Loop Identification of a Steam Superheater

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)65514-9 ◽

1979 ◽

Vol 12 (8) ◽

pp. 961-968

Author(s):

J.A. de la Puente ◽

P. Albertos

Keyword(s):

Closed Loop ◽

Steam Superheater ◽

Closed Loop Identification

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Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽

10.3390/s21113653 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3653

Author(s):

Lilia Sidhom ◽

Ines Chihi ◽

Ernest Nlandu Kamavuako

Keyword(s):

Degrees Of Freedom ◽

Closed Loop ◽

Sliding Mode ◽

Robot Manipulator ◽

Parametric Identification ◽

Recursive Least Squares ◽

Unknown Parameters ◽

Closed Loop Identification ◽

Input Variables ◽

Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

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