We propose a Lyapunov theory based linguistic reinforcement learning (RL) framework for
stable tracking control of robotic manipulators. In particular, we employ Lyapunov theory to …

We propose a fuzzy reinforcement learning (RL) based controller that generates a stable
control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at …

A novel backstepping control scheme based on reinforcement fuzzy Q‐learning is proposed
for the control of container cranes. In this control scheme, the modified backstepping …

This work proposes Lyapunov theory based Fuzzy/Neural Reinforcement Learning (RL)
controllers with guaranteed stability. We look at ways in which Lyapunov theory could be …

Abstract Machine learning techniques in the form of reinforcement learning (RL) can solve
complex nonlinear problems found within spacecraft attitude determination and control …

Automatic voltage regulator (AVR) systems plays a vital role in power system. AVR system is
normally used to maintain uniform power supply from generating station. This paper …

Proposed work is aimed at designing an intelligent fuzzy controller with guaranteed stability.
We employ Lyapunov theory on the linguistic fuzzy rules to constrain action set for the fuzzy …

Climate change due to air pollution and emission of greenhouse gases are affecting the
flora and fauna drastically. Fuel-based transportation is one of the biggest causes of this …

Power quality (PQ) is defined as the ability to maintain a pure sinusoidalvoltage waveform
with the specified amplitude and frequency within the required limit with no changes in …

A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed
for the control of container cranes. In this control scheme, the modified backstepping …