Dynamical analysis and reachable set estimation of T-S fuzzy system with permanent magnet synchronous motor

Vadivel, R.; Njitacke, Zeric Tabekoueng; Shanmugam, Lakshmanan; Hammachukiattikul, Porpattama; Gunasekaran, Nallappan

doi:10.1016/j.cnsns.2023.107407

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…To further illustrate the advantage of the proposed approach, comparisons with [30-33] on the estimated sampling period for PMSM are presented in Table 2.Note that since the derived upper bound of the sampling period depends on the PMSM parameters, we calculated this bound at the given values in each corresponding work of [30][31][32][33]. The results clearly show that the proposed approach performs in a superior manner when reducing the sampling frequency while maintaining closed-loop stability.…”

Section: Simulation Comparisonmentioning

confidence: 98%

“…However, all these results are developed for either continuous-time or discrete-time cases but are not adapted for sampled-data implementation. The sampled-data control problem for PMSM has been studied in only a few works [26][27][28][29][30][31][32][33]. The authors of [26][27][28] studied the stabilization of the PMSM under periodic sampling with different control methods, while [28,29] investigated this problem for output reference tracking.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [26][27][28] studied the stabilization of the PMSM under periodic sampling with different control methods, while [28,29] investigated this problem for output reference tracking. The control design technique of [30] is based on the passivity approach, while in [31], fuzzy controllers were designed using the Lyapunov-Krasovskii functional strategy. In [32], the sampled-data sliding mode controller was constructed; it was based on the symplectic Euler approach.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

2023

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The stabilization of a permanent magnet synchronous motor using digital controllers requires the design of both the feedback law and an appropriate sampling frequency. Moreover, the design approach must be robust against existing uncertainties, such as disturbances and parameter variations. In this paper, we develop a stabilizing state feedback nonlinear control scheme for the permanent magnet synchronous motor. Moreover, we consider the case where the feedback signal is transmitted over a digital platform, and we derive the stabilizing sampling frequency, such that the stability of the closed-loop system is maintained. We design the controller by emulation, where the closed-loop stability is first established in continuous time; we then take into account the effect of sampling. The feedback law consists of two parts: feedback linearization and robust linear quadratic regulator for the linearized mode. The robustness is achieved by augmenting the state space model, with additional states representing the tracking errors of the motor speed and the motor current. Then, to cope with sampling, we estimate the maximally allowable sampling interval to reduce the sampling frequency while preserving the closed-loop stability. The overall system is modeled as a hybrid dynamical system, which allows handling both the continuous-time and discrete-time dynamics. The effectiveness of the proposed technique is illustrated by simulation and verified experimentally using a hardware-in-the-loop setup. Upon implementing the proposed approach, the obtained sampling interval was around 91 ms, making it suitable for digital implementation setups.

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Section: Simulation Comparisonmentioning

confidence: 98%