In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features
are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system's parameters …

Due to outstanding learning capabilities, neural networks have attracted a great deal of
research interests and various structures for them have been presented. Quantum neural …

In this paper, an adaptive observer for robust control of robotic manipulators is proposed.
The lumped uncertainty is estimated using Chebyshev polynomials. Usually, the uncertainty …

A deep learning method is developed for chaotic time series classification. We investigate
the chaotic state of a dynamical system, based on the output of the system. One of the main …

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms
of simplicity, accuracy, and convergence. Three improvements were made in this study as a …

In this work, an adaptive dynamic sliding mode control approach is proposed for robotic
systems via uncertainty estimators with exponential convergence rate. The uncertainties are …

Solar radiation estimation is integral part for various solar energy systems which requires
precise and expensive devices to gage the solar radiation for a particular region. However …

In this paper, a simple observer-based robust control of robot manipulators in the task-space
is presented. Many nonlinear observers require system dynamics. The novelty of this paper …

This paper presents a robust dynamic sliding mode control for electrically driven robot
manipulators. The control law computes the motor voltages based on the voltage control …

This paper introduces a robust task-space control scheme for a robotic system with an
adaptive observer. The proposed approach does not require the availability of the system …