Effective damping of the torsional vibrations of the drive system with an elastic joint based on the forced dynamic control algorithms

Serkies, Piotr; Szabat, K.

doi:10.1177/1077546319852472

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…The above-described construction of the connection between machines can lead to an additional component (oscillations) in the state variables. Assuming the 's' symbol as the Laplace operator, the control structure presented in Figure 1 can be described by the following system of equations [11][12][13][14][15]:…”

Section: Mathematical Model Of the Control Structurementioning

confidence: 99%

“…The location of the system closed-loop poles influence the characteristics of the drive, so it is a significant point during the design procedure [12,13]. In [14,15], the forced dynamic control structure is proposed. It allows to locate the system closed-loop poles in a desired position and additionally eliminate (rather in theory) the influence of the load torque on the load speed.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Kaminski

Szabat

2021

Energies

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This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

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Section: Mathematical Model Of the Control Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Kaminski

Szabat

2021

Energies

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“…This control method was proposed by S. Dodds in [14]. The future modification of this strategy was discussed in [15]. In this paper, the control of the two-mass system with the additional limitation of electromagnetic and shaft torque is considered.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Type II Controller for Two-Mass System

et al. 2022

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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.

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“…A number of other control techniques used to control the speed/position of a drive with elastic coupling have been presented in the literature, e.g., [17][18][19], but these methods are not widely used in industrial practice.…”

Section: Introductionmentioning

confidence: 99%

Implementation of PI and MPC-Based Speed Controllers for a Drive with Elastic Coupling on a PLC Controller

Serkies

Gorla

2021

Electronics

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This paper presents some of the issues related to the implementation of advanced control structures (PI controller with additional feedback, Model Predictive Controller) for drives with elastic coupling on a programmable logic controller (PLC). The predominant solutions to electric drive control include the use of rapid prototyping cards, signal processors or programmable matrices. Originally, PLC controllers were used to automate sequential processes, but for several years now, a trend related to their implementation for advanced control objects can be observed. This is mainly due to their compact design, immunity to disturbances and standard programming languages. The following chapters of the paper present the mathematical model of the drive and describe the implementation of the proposed control structures. A PI controller with additional feedback loops and a predictive controller are taken into consideration. Their impact on the CPU load was analysed, and the work was summarised by a comprehensive experimental study. The presented results confirm that it is possible to implement advanced control structures on a PLC controller for drives with elastic coupling while maintaining a sufficiently low load on its CPU.

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Effective damping of the torsional vibrations of the drive system with an elastic joint based on the forced dynamic control algorithms

Cited by 15 publications

References 14 publications

Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Fuzzy Adaptive Type II Controller for Two-Mass System

Implementation of PI and MPC-Based Speed Controllers for a Drive with Elastic Coupling on a PLC Controller

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