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The Lyapunov stability method is adopted to verify the stability of the interval type-2 fuzzy sliding-mode controller system.
The adaptive neural sliding-mode control system is comprised of neural network (NN) and a compensation controller.
According to the big disturbance torque and high requirement of control accuracy, a compound sliding mode controller is proposed to control the system based on Kalman filter.
Then it is used to implement a sliding mode controller in a hybrid system.
The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function.
Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.
This paper deals with design parameter selection of a discrete-time neural second order sliding mode controller for unknown nonlinear systems, based on bacterial foraging optimization.
In this paper an integral backstepping sliding mode controller is proposed for controlling underactuated systems.
For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations.
Here we design a sliding mode controller for a hydraulic press system to demonstrate the effectiveness of this method.
Our attention is mainly focused on designing the observer-based adaptive sliding mode controller for such a complex system.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com