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.
J. Shen and S. N. Balakrishnan, "A Class of Modified Hopfield Networks for Control of Linear and Nonlinear Systems," Proceedings of the 1998 American Control Conference, 1998, Institute of Electrical and Electronics Engineers (IEEE), Jan 1998.
The definitive version is available at http://dx.doi.org/10.1109/ACC.1998.703552
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
Article - Conference proceedings
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