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Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay
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DOI：10.1109/TNNLS.2022.3213566.png)
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Abstract
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This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton-Jacobi-Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms of the convergence of the adaptive critic design and the stability of the closed-loop systems, where parameter convergence can be achieved under a weakened excitation condition. Simulation studies are investigated to verify the efficacy of the presented design scheme.
Keywords:
Mathematical models
Optimal control
Optimization
Convergence
Iterative algorithms
Dynamic programming
Learning systems
Hamilton-Jacobi-Bellman (HJB) equation
Hamiltonian-driven adaptive dynamic programming (ADP)
pseudo-Hamiltonian
quasi-Hamiltonian
relaxed excitation condition
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8.9
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