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Spiking Adaptive Dynamic Programming Based on Poisson Process for Discrete-Time Nonlinear Systems
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DOI：10.1109/TNNLS.2021.3085781.png)
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Abstract
En 中文
In this article, a new iterative spiking adaptive dynamic programming (SADP) method based on the Poisson process is developed to solve optimal impulsive control problems. For a fixed time interval, combining the Poisson process and the maximum likelihood estimation (MLE), the three-tuple of state, spiking interval, and probability of Poisson distribution can be computed, and then, the iterative value functions and iterative control laws can be obtained. A property analysis method is developed to show that the value functions converge to optimal performance index function as the iterative index increases from zero to infinity. Finally, two simulation examples are given to verify the effectiveness of the developed algorithm.
Keywords:
Process control
Control systems
Optimal control
Maximum likelihood estimation
Performance analysis
Dynamic programming
Nonlinear systems
Maximum likelihood estimation (MLE)
nonlinear systems
optimal control
Poisson process
spike train
spiking adaptive dynamic programming (SADP)
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