In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …

A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …

This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

At present, designing convolutional neural network (CNN) architectures requires both
human expertise and labor. New architectures are handcrafted by careful experimentation or …

This article discusses the control design and experiment validation of a flexible two-link
manipulator (FTLM) system represented by ordinary differential equations (ODEs). A …

Replay is the reactivation of one or more neural patterns that are similar to the activation
patterns experienced during past waking experiences. Replay was first observed in …

Lane change is a crucial vehicle maneuver which needs coordination with surrounding
vehicles. Automated lane changing functions built on rule-based models may perform well …

We study the use of randomized value functions to guide deep exploration in reinforcement
learning. This offers an elegant means for synthesizing statistically and computationally …

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is
fundamental to robotics, serving a wide range of civil and defense applications. In control …