Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …

Artificial intelligence is becoming an increasingly popular tool in more and more areas of
technology. New challenges in control systems design and application are related to …

Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …

In this investigation, the nonlinear swing-up problem associated with the cart-pole system
modeled as a multibody dynamical system is solved by develo** a deep Reinforcement …

In this paper, we propose a new class nonlinear hybrid controller (NHC) for swinging-up and
stabilizing the (under-actuated) rotary inverted pendulum system. First, the swing-up …

This paper presents the realisation of self-erecting inverted pendulum controls via two
switched control approaches, a rule based fuzzy control for swing up inverted pendulum rod …

The control of a rotary inverted pendulum (RIP) is challenging because it is an
underactuated, highly sensitive, and unsteady system. Sliding mode control (SMC) is a …

Rotary inverted pendulum (RIP) is a well-known system that is commonly employed as an
ideal benchmarking model for verifying linear and nonlinear control algorithms thanks to …

Reinforcement learning does not require explicit robot modeling as it learns on its own
based on data, but it has temporal and spatial constraints when transferred to real-world …

This paper presents a real-time machine learning control (MLC) of articulated robotic
manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy …