Adaptive neural speed controllers applied for a drive system with an elastic mechanical coupling – A comparative study

Kaminski, Marcin; Orłowska-Kowalska, Teresa

doi:10.1016/j.engappai.2015.06.011

Cited by 22 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A low value can lead to long-time calculations, but a high coefficient may make it impossible to obtain the optimum of the cost function. In the extreme case, it is possible to lose the convergence of the training algorithm (Kaminski and Orlowska-Kowalska, 2015). The sigma can be treated as a scaling factor for the argument of the Gaussian function.…”

Section: Resultsmentioning

confidence: 99%

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Kaminski¹,

Stanislawski²,

Bastrzyk³

2020

Physicochem. Probl. Miner. Process.

View full text Add to dashboard Cite

In this study, the neural model for modeling of oil agglomeration of dolomite in the presence of anionic and cationic surfactants (sodium oleate and dodecylammonium hydrochloride) was implemented. The effect of surfactants concentration, oil dosage, time of mixing, pH, and mixing speed of the impeller in the process recovery were investigated using Radial Basis Function Neural Network (RBFNN). A significant problem in this modeling, was the selection of the structure of the neural network. In algorithms based on the RBFNN, the issue mentioned relates to the number of nodes in the determination of the hidden layer. Also, the distribution of functions in data space is significant. In the proposed solution, at this stage of the neural model design, the Growing Neural Gas Network (GNGN) was implemented. Such a procedure introduced automation of the calculation process. The centers were obtained from the GNGN and the structure (number of radial neurons) can be approximated based on a simple searching algorithm. The idea of the data calculations was implemented as an original algorithm that can be easily transferred to Matlab, Python, or Octave software. The values predicted from the neural networks model were in good agreement with the experimental data. Thus, the RBFNN-GNGN model used in this study, can be employed as a reliable and accurate method to predict, and in the future to optimize the performance of oil agglomeration process.

show abstract

Section: Resultsmentioning

confidence: 99%

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Kaminski¹,

Stanislawski²,

Bastrzyk³

2020

Physicochem. Probl. Miner. Process.

View full text Add to dashboard Cite

show abstract

“…According to the problem that the flexible modes affect the performances of the drive, a fuzzy Luenberger observer is designed (Szabat, Tran-Van, & Kamiski, 2015), and the dynamic states are recognized. Kaminski and Orlowska-Kowalska (2015) analyzed and compared four types of neural-adaptive controllers and apply the methods for a drive system with an elastic mechanical coupling. However, all the control approaches are focused primarily on the stability of the nonlinear systems with disturbances.…”

Section: Introductionmentioning

confidence: 99%

Speed tracking control for the gimbal system with harmonic drive

Ning

Han

2017

Control Engineering Practice

View full text Add to dashboard Cite

“…Adaptive systems can be distinguished as a third group. In general, two types of adaptive control are mentioned in the literature [27][28][29][30][31][32][33][34][35][36]. The first one is called indirect adaptive control.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach is called direct adaptive control, in which the parameters of the controller are changed directly, due to the changes of the tracking error (specified as output between the desired trajectory and real output of the plant) [28][29][30][31][32][33][34][35][36][37][38]. For linear systems the suitable solution could be a classical PI controller.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Type II Controller for Two-Mass System

et al. 2022

View full text Add to dashboard Cite

This paper presents original concepts of control systems for an electrical drive with an elastic mechanical coupling between the motor and the driven mechanism. The synthesis procedure of the speed controller uses a proposed quality index (cost function) of system operation ensures the minimization of both tracking errors and torsional vibrations. Proper selection of the cost function focusses more on the reduction of torsional vibrations due to their negative influence on the drive’s mechanical coupling vitality. The omission of the plant identification of an adaptive fuzzy controller was proposed. Two types of fuzzy controllers were analyzed, namely with type I and type II fuzzy membership functions. The novelty of the presented approach is in the application of a Petri transition layer in a type II fuzzy controller which reduces the numerical complexity in case of a large number of complicated type II fuzzy sets. The presented simulation and experimental results prove that the best dumping of mechanical vibrations ensures the adaptive fuzzy controller with type II functions and a Petri transition layer.

show abstract

Adaptive neural speed controllers applied for a drive system with an elastic mechanical coupling – A comparative study

Cited by 22 publications

References 32 publications

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Speed tracking control for the gimbal system with harmonic drive

Fuzzy Adaptive Type II Controller for Two-Mass System

Contact Info

Product

Resources

About