title: Identification and Control for Singularly Perturbed Systems Using Multitime-Scale Neural Networks.
abstract: Many well-established singular perturbation theories for singularly perturbed systems require the full knowledge of system model parameters. In order to obtain an accurate and faithful model, a new identification scheme for singularly perturbed nonlinear system using multitime-scale recurrent high-order neural networks (NNs) is proposed in this paper. Inspired by the optimal bounded ellipsoid algorithm, which is originally designed for discrete-time systems, a novel weight updating law is developed for continuous-time NNs identification process. Compared with other widely used gradient-descent updating algorithms, this new method can achieve faster convergence, due to its adaptively adjusted learning rate. Based on the identification results, a control scheme using singular perturbation theories is developed. By using singular perturbation methods, the system order is reduced, and the controller structure is simplified. The closed-loop stability is analyzed and the convergence of system states is guaranteed. The effectiveness of the identification and the control scheme is demonstrated by simulation results.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Zheng
authors.forename: Wen-Fang
authors.initials: WF
authors.lastname: Xie
authors.forename: Xuemei
authors.initials: X
authors.lastname: Ren
authors.forename: Jing
authors.initials: J
authors.lastname: Na
mesh_terms: D001185:Artificial Intelligence / Q000639:trends; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors