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Indexed by:期刊论文

Date of Publication:2018-08-01

Journal:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Included Journals:SCIE、ESI高被引论文、Scopus

Volume:29

Issue:8

Page Number:3747-3760

ISSN No.:2162-237X

Key Words:Adaptive approximation; single-hidden-layer feedforward network (SLFN); uncertain nonlinear system without feedback linearizability; unknown dynamics

Abstract:In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.