Sep 2026· International Journal of Robust and Nonlinear Control· 40 references
Adaptive Dynamic Programming Control
Abstract
ABSTRACT This paper proposes an adaptive optimal containment control method for nonlinear strict‐feedback multiagent systems with state constraints. First, a neural network‐based reinforcement learning algorithm is developed within an optimized backstepping framework. Unlike the traditional actor‐critic network structure, the paper introduces an observer‐actor‐critic architecture, where observers are used to estimate unmeasurable states, improving the reliability and accuracy of the system. Then, logarithmic barrier Lyapunov functions are combined with an optimal cost function to handle the state constraints. Using the Lyapunov stability theory, it is rigorously proven that all closed‐loop signals are uniformly, ultimately bounded, and that all system states remain within the constraint set. Finally, the proposed scheme is demonstrated to be valid using numerical and practical simulation examples.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.