Abstract

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are considered unknown whereas the control coefficient matrix of the affine system is considered known. By applying approximate dynamic programming (ADP) using multilayer neural networks (MNNs), the optimal control policy is obtained. Under our proposed MNN weight update laws, the tracking and weight estimation errors are proven to be uniformly ultimately bounded (UUB) using Lyapunov analysis. The efficacy and reliability of the method are demonstrated through hardware implementation on the Quanser QDrone2 UAV.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Office of Naval Research, Grant N00014-21-1-2232

Keywords and Phrases

Aerial Robotics; Autonomous Systems; Neural Networks; Reinforcement Learning

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2025

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