Event-triggered adaptive prescribed performance control for a class of pure-feedback stochastic nonlinear systems with input saturation constraints. Issue 12 (9th September 2020)

• Record Type: Journal Article
• Authors: Ouyang, Xin-Yu; Wu, Li-Bing; Zhao, Nan-Nan; Gao, Chuang
• Is Part Of: International journal of systems science. Volume 51:Issue 12(2020)
• Page Start: 2238
• Page End: 2257
• Publication Date: 2020-09-09
• Subjects: Nonaffine nonlinear stochastic systems -- event-triggered control (ETC) -- prescribed performance control (PPC) -- neural networks -- saturation nonlinearities; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2020.1793232
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : Aiming at the tracking control problem of non-affine stochastic nonlinear systems with input saturation constraints, the event trigger control (ETC) based on Radial basic function neural networks (RBFNNs) and prescribed performance control (PPC) is considered. First, the mean value theorem is used to decouple the non-affine terms existing in the system. Second, the design process of PPC is reconstructed for a class of stochastic nonlinear systems, because the existence of stochastic disturbances has not been fully considered in previous literature on PPC, so that the system output is not to violate the set constraint bound by preset function. Finally, unlike the existing event triggering results, a special event triggering strategy is designed, which further takes into account the error between the preset function and the system output and the errors in all states of stochastic nonlinear systems, so it is expected that the amount of communications will be further reduced. Also, the proposed adaptive PPC scheme with event triggering mechanism can guarantee that all closed-loop signals are uniformly ultimately bounded (UUB) in probability within the appropriate compact sets. Finally, the effectiveness of the proposed method is verified by a practical example.