Optimizing the parameters of spiking neural networks for mobile robot implementation

Azimirad, Vahid; Sotubadi, Saleh Valizadeh; Sharifi, Farrokh

doi:10.1109/iccke50421.2020.9303660

Cited by 3 publications

(1 citation statement)

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“…Solutions to these issues have been proposed in the form of Model Predictive Control and Explicit Reference Governor. [21][22][23][24] Similar to integer-order systems, FO systems can also be subject to these operational constraints. Consequently, these constrained control schemes have been developed and adapted for use with FO systems.…”

Section: Introductionmentioning

confidence: 99%

Stabilizing unstable periodic orbit of unknown fractional-order systems via adaptive delayed feedback control

Yaghooti,

Safavigerdini,

Hajiloo

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents an adaptive nonlinear delayed feedback control scheme for stabilizing the unstable periodic orbit of unknown fractional-order chaotic systems. The proposed control framework uses the Lyapunov approach and sliding mode control technique to guarantee that the closed-loop system is asymptotically stable on a periodic trajectory sufficiently close to the unstable periodic orbit of the system. The proposed method has two significant advantages. First, it employs a direct adaptive control method, making it easy to implement this method on systems with unknown parameters. Second, the framework requires only the period of the unstable periodic orbit. The robustness of the closed-loop system against system uncertainties and external disturbances with unknown bounds is guaranteed. Simulations on fractional-order duffing and gyro systems are used to illustrate the effectiveness of the theoretical results. The simulation results demonstrate that our approach outperforms the previously developed linear feedback control method for stabilizing unstable periodic orbits in fractional-order chaotic systems, particularly in reducing steady-state error and achieving faster convergence of tracking error.

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Section: Introductionmentioning

confidence: 99%