Incremental Model-Based Global Dual Heuristic Programming for Flight Control

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic Programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial...

Safe Exploration Algorithms for Reinforcement Learning Controllers

Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in...

Adaptive spacecraft attitude control with incremental approximate dynamic programming

This paper presents an adaptive control technique to deal with spacecraft attitude tracking and disturbance rejection problems in the presence of model uncertainties. Approximate dynamic programming has been proposed to solve adaptive, optimal control problems without using accurate systems models. Within this category, linear approximate...