Compound learning adaptive neural network optimal backstepping control of uncertain fractional-order predator-prey systems  

作　　者：Heng Liu Mei Zhong Jinde Cao Chengdai Huang 

机构地区：[1]School of Mathematics and Physics Guangri Minzu University,Nanning 530006,P.R.China [2]School of Mathematics,Southeast University Nanjing 211189,P.R.China [3]School of Mathematics and Statistics,Xinyang Normal University Xinyang 464000,P.R.China

出　　处：《International Journal of Biomathematics》2024年第8期91-122,共32页生物数学学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(12261009).

摘　　要：Reinforcement learning as an effective strategy is widely utilized in optimal control.However,when updating critic-actor weight vectors based on the square of Bellman residual,it often leads to substantial computational complexity.This paper formulates a compound learning optimal backstepping control programme that can efficaciously reduce the computational burden for fractional-order predator-prey systems(FOPPS)with uncertainties.To economize resource,a reinforcement learning technology is adopted to realize the optimal control in view of neural networks under identifiercritic-actor structure.To address the computational complexity issue raised above,a simple positive definite function is proposed to update critic-actor weight vectors.Fractional-order filters are utilized to estimate virtual signals and their fractional-order derivatives for tackling the"explosion of complexity"problem existing in the conventional backstepping technology.Simultaneously,to enhance the approximation accuracy of uncertainties in FOPPS,a compound learning updating law is built by using tracking error and prediction error.In accordance with the stability analysis,the formulated scheme ensures that the output of FOPPS can track the reference signal with the expected accuracy and all signals are bounded.Eventually,a numerical simulation is presented to validate the effectiveness of the proposed control strategy.

关 键 词：Fractional-order predator-prey system adaptive backstepping control compound learning optimal control reinforcement learning 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]