RBFNN-based adaptive control of singular systems via non-fragile proportional and derivative feedback method

Huiyan Zhang, Yu Huang, Ning Zhao, Kalidass Mathiyalagan and Peng Shi

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 243, issue C, 51-68

Abstract: This paper investigates the issue of adaptive neural non-fragile proportional and derivative (PD) feedback control for the singular systems with unknown nonlinear dynamics. First, considering the inaccuracy of controller implementation, the problem of non-fragile controller design is considered and solved by using a robust control strategy. Second, PD feedback control is established to transform the singular system into a normal system, which facilitates stability analysis of the system. Third, the adaptive proportional–derivative radial basis function neural network technique is used to approximate the unknown nonlinear function and resist its influence. Under this designed framework, the stability conditions of the closed-loop system are given by using the Lyapunov method. The designed methods of state feedback gains and observer-based gain matrices are presented, respectively. Last, three examples are employed to elucidate the feasibility of the developed control strategy.

Keywords: Singular nonlinear systems; Non-fragile adaptive control; Proportional and derivative feedback; Radial basis function neural network (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:243:y:2026:i:c:p:51-68

DOI: 10.1016/j.matcom.2025.11.021

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