An improved hysteretic PWM control with feed-forward and feedback

An improved hysteretic PWM control with feed-forward and feedback

In this paper, an improved hysteretic PWM control with feed-forward and feedback for the buck converter is presented. The proposed control method is based on hysteretic control of capacitor C voltage, which not only offers faster transient response to meet the challenges of the power supply requirements of fast dynamic input and load changes, but also provides better stability and solves the co...

Parsimonious learning feed-forward control

We introduce the Learning Feed-Forward Control configuration. In this configuration, a B-spline neural network is contained, which suffers from the curse of dimensionality. We propose a method to avoid the occurrence of this problem. 1 Introduction In order to design a conventional feedback controller, like a state-feedback controller, detailed knowledge of the process under control is required...

LMI Robust Control of PWM Converters: An Output-Feedback Approach

A Modified Structure for Feed Forward Active Noise Control Systems with Improved Performance

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. In this paper, the existing FxLMS algorithm is modified which provides a new structure for improving the noise reduction and conv...

A greenhouse control with feed-forward and recurrent neural networks

Greenhouses are classified as complex systems, so it is difficult to implement classical control methods for this kind of process. In our case we have chosen neural network techniques to drive the internal climate of a greenhouse. An Elman neural network has been used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feedforward neural network has been trained ...