2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC) 2016
DOI: 10.1109/cgncc.2016.7829104
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Mode-free altitude control for airship based on Q-learning and CMAC network

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“…In [11], the auxiliary air bursas charge or deflation and elevator combination control was employed to adjust the airship altitude for suspension, and the control law was designed based on fuzzy self-tuning control. The Q-learning and cerebella model articulation controller (CMAC) were utilized to realize the airship altitude reinforcement learning control based on the Markov decision process [12]. It should be noted that all these mentioned control algorithms are complicated in form with many parameters to tune, which are difficult to implement in practice.…”
Section: Introductionmentioning
confidence: 99%
“…In [11], the auxiliary air bursas charge or deflation and elevator combination control was employed to adjust the airship altitude for suspension, and the control law was designed based on fuzzy self-tuning control. The Q-learning and cerebella model articulation controller (CMAC) were utilized to realize the airship altitude reinforcement learning control based on the Markov decision process [12]. It should be noted that all these mentioned control algorithms are complicated in form with many parameters to tune, which are difficult to implement in practice.…”
Section: Introductionmentioning
confidence: 99%