Integral Sliding-Mode Fault-Tolerant Control for Fuzzy Singular Systems with Actuator Saturation

Kchaou, Mourad; Ahmadi, Saleh Al; Draou, E.

doi:10.1007/s00034-019-01210-1

Cited by 7 publications

(1 citation statement)

References 49 publications

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“…Furthermore, limited motor torque and a variety of payloads that can be carried by the electric drive-powered system is undeniably a huge concern, which forces the need to make modern control systems even more insensitive to any kind of disturbances [15]. Additionally, with all the instantiated issues, problems typical for electric motor control, such as actuator saturation and faults need to be taken into account [16,17]. The control signal established by the controller should remain within certain limits that do not exceed the actuator's movement capabilities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Kaczmarczyk,

Stanislawski,

Szrek

et al. 2024

Energies

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External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

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Section: Introductionmentioning

confidence: 99%