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...

Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on an explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in the proposed method...

Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic...

Intelligent adaptive optimal control using incremental model-based global dual heuristic programming subject to partial observability

The scarcity of information regarding dynamics and full-state feedback increases the demand for a model-free control technique that can cope with partial observability. To deal with the absence of prior knowledge of system dynamics and perfect measurements, this paper develops a novel intelligent control scheme by combining global dual...

Framework for state and unknown input estimation of linear time-varying systems

The design of unknown-input decoupled observers and lters requires the assumption of an existence condition in the literature.This paper addresses an unknown input ltering problem where the existence condition is not satised. Instead of designing a traditional unknown input decoupled lter, a Double-Model Adaptive Estimation approach is extended...

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...

Vision-Based Nonlinear Incremental Control for A Morphing Wing with Mechanical Imperfections

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the...