Receding Horizon Robust Control for Nonlinear Systems Based on Linear Differential Inclusion of Neural Networks

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6 Citations (Scopus)

Abstract

In this paper, we present a new receding horizon neural robust control scheme for a class of nonlinear systems based on the linear differential inclusion (LDI) representation of neural networks. First, we propose a linear matrix inequality (LMI) condition on the terminal weighting matrix for a receding horizon neural robust control scheme. This condition guarantees the nonincreasing monotonicity of the saddle point value of the finite horizon dynamic game. We then propose a receding horizon neural robust control scheme for nonlinear systems, which ensures the infinite horizon robust performance and the internal stability of closed-loop systems. Since the proposed control scheme can effectively deal with input and state constraints in an optimization problem, it does not cause the instability problem or give the poor performance associated with the existing neural robust control schemes.

Original languageEnglish
Pages (from-to)659-678
Number of pages20
JournalJournal of Optimization Theory and Applications
Volume160
Issue number2
DOIs
Publication statusPublished - 2014 Feb

Keywords

  • Linear differential inclusion (LDI)
  • Neural networks
  • Nonlinear systems
  • Receding horizon control

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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