High performance nonlinear controller design for AC and DC machines: partial feedback linearization approach

Sarkar, Subroto K.; Das, Sajal K.

doi:10.1007/s40435-017-0330-x

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…Analysis of Chaos using Floquet theory was discussed in [25][26][27][28][29][30]. Floquet Theory (Filippov's method) is applied to analyze the stability based on Floquet multipliers ( [34,37,38] and [39]). Monodromy matrix is shown equally:…”

Section: Analysis Of the Chaosmentioning

confidence: 99%

“…Consequently, studying chaos behavior and suppressing it has been the major target of researchers for practical nonlinear systems. Consequently, there are numerous methods to control chaotic systems, such as the (Ott, Grebogi and Yorke) OGY method [31], feed-back linearization method [32][33][34], a realtime cycle to cycle variable slope compensation method [35], time-delay feedback control [36], developing Floquet theory [26,30,[37][38][39], fuzzy control [41,43,45,46].…”

Section: Introductionmentioning

confidence: 99%

“…This interpretation of the Poincaré map is seldom used as it requires developing information for a limited cycle location [24,25]. While, the Floquet theory depends on the State Transition Matrix (STM) for the full cycle [34,35,[37][38][39]. Both methods give the same results; however, the Floquet theory fits well with mechanical switching systems and simpler to be implemented as compared to the Lyapunov exponent method [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Period-doubling bifurcation analysis and chaos control for load torque using FLC

Moustafa

Sobaih

Abozalam

et al. 2021

Complex Intell. Syst.

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Chaotic phenomena are observed in several practical and scientific fields; however, the chaos is harmful to systems as they can lead them to be unstable. Consequently, the purpose of this study is to analyze the bifurcation of permanent magnet direct current (PMDC) motor and develop a controller that can suppress chaotic behavior resulted from parameter variation such as the loading effect. The nonlinear behaviors of PMDC motors were investigated by time-domain waveform, phase portrait, and Floquet theory. By varying the load torque, a period-doubling bifurcation appeared which in turn led to chaotic behavior in the system. So, a fuzzy logic controller and developing the Floquet theory techniques are applied to eliminate the bifurcation and the chaos effects. The controller is used to enhance the performance of the system by getting a faster response without overshoot or oscillation, moreover, tends to reduce the steady-state error while maintaining its stability. The simulation results emphasize that fuzzy control provides better performance than that obtained from the other controller.

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Section: Analysis Of the Chaosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Period-doubling bifurcation analysis and chaos control for load torque using FLC

Moustafa

Sobaih

Abozalam

et al. 2021

Complex Intell. Syst.

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show abstract

“…The technique requires measurements of the state vector x in order to transform a multi-input nonlinear control system into a linear and controllable one. The state space model of an induction motor in the (α, β) frame coordinate is given by [4]:…”

Section: Input-output Feedback Linearization Control and Sliding Mode Observer Using The Reduced Model Of Im 41 Input-output Feedback Linmentioning

confidence: 99%

“…There are many methods dedicated to control induction motors, however, the controlled part is subjected to strong nonlinearities and temporal variables, it is necessary to design control algorithms ensuring the robustness of the process against the uncertainties on the parameters and their variations. The input-output feedback linearization control has focussed on the attention owing to the simple design and on the perfect decoupling between rotor speed and flux, as well as fast dynamic response, even too easy implementation, robustness to parameter variations, and load disturbances [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

Harzelli

Menacer

Ameid

2019

J Ambient Intell Human Comput

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High performance nonlinear controller design for AC and DC machines: partial feedback linearization approach

Cited by 12 publications

References 25 publications

Period-doubling bifurcation analysis and chaos control for load torque using FLC

Period-doubling bifurcation analysis and chaos control for load torque using FLC

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

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