This paper presents a class of modified Hopfield neural networks (MHNN) and their use in solving linear and nonlinear control problems. This class of networks consists of parallel recurrent networks which have variable dimensions that can be changed to fit the problems under consideration. It has a structure to implement an inverse transformation that is essential for embedding optimal control gain sequences. Equilibrium solutions are discussed. Numerical results for a motivating aircraft control problem (linear) are presented. Furthermore, we formulate the state-dependent Riccati equation method (SDRE) for a class of nonlinear dynamical system and show how MHNN provides the solution. Two examples that illustrate the potential of this network for the SDRE method are also presented.

Meeting Name

1998 American Control Conference, 1998


Mechanical and Aerospace Engineering

Keywords and Phrases

Hopfield Neural Nets; MHNN; Riccati Equations; SDRE; Aircraft Control Problem; Equilibrium Solutions; Inverse Transformation; Linear Control Problems; Modified Hopfield Neural Networks; Neurocontrollers; Nonlinear Control Problems; Nonlinear Control Systems; Nonlinear Dynamical System; Optimal Control; Optimal Control Gain Sequences; Parallel Recurrent Networks; State-Dependent Riccati Equation Method; Variable Dimensions

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 1998 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link