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

Abstract

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 identifier–critic–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.

Online reinforcement learning control via discontinuous gradient

“…However, these works do not consider perturbations in their models. On the other hand, Ning et al 28 and Liu et al 29 describe a control with reinforcement learning (RL) using NN for a missile and predator–prey system, respectively, considering perturbations, but these are matches. In Verrelli et al , 30 they introduce an adaptive control method tailored for nonminimum phase systems with periodic reference signals.…”

Finite-time command filter backstepping control of uncertain chaotic systems

“…and electronics [1][2][3]. Consequently, synchronization of UCSs has become a research hotspot, and researchers have provided multiple schemes, such as neural network control [4,5], adaptive fuzzy control [6,7], sampling control [8,9], backstepping control [10][11][12], and sliding mode control [13,14]. Note that some UCSs can be transformed into strict feedback forms, and backstepping control is a common method for dealing with these types of systems.…”