Details

Autor(en) / Beteiligte

• Zhao, Ziliang
• Zhu, Qinglin
• Guo, Bin
• Aguilar-Ibanez, Carlos
• Carlos Aguilar-Ibanez

Titel

Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning

Ist Teil von

• Complexity (New York, N.Y.), 2022, Vol.2022 (1)

Ort / Verlag

Hoboken: Hindawi

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• This study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be resolved using the regulation control strategy. Then, the robust control problem of uncertain system can be represented as an optimal control problem of nominal system, which provides a new pathway to address the robust control problem. To realize the optimal control, the derived Hamilton–Jacobi–Bellman equation (HJBE) is solved by training a critic neural network (CNN). Finally, two innovative critic learning techniques are suggested to calculate the unknown NN weights, where the convergence of NN weights can be guaranteed. Simulations are carried out to demonstrate the effectiveness of the proposed method.