This paper conducts an investigation into the finite-time optimal control problem of matching uncertain nonlinear systems through adaptive dynamic programming methods. Initially, a novel finite-time control approach is put forward. This approach reformulates the robust control problem as the optimal control problem of the nominal system by devising an appropriate cost function. Moreover, a crucial neural network is constructed to approximate the optimal cost function, and the gradient descent algorithm is employed to train the weights of the neural network. The semi-global practical finite-time stability criteria and the Lyapunov method are utilized to guarantee the convergence of closed-loop systems within a finite time. Ultimately, a simulation example is presented to validate the theoretical analysis.

Heping Gu. Finite-time Optimal Control for Uncertain Nonlinear Systems via Adaptive Dynamic Programming. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 4: 9-20. https://doi.org/10.25236/AJCIS.2026.090402.

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