Model predictive neural control with applications to a 6 DOF helicopter model

Author

Wan, Eric A. ; Bogdanov, Alexander A.

Author_Institution

Oregon Graduate Inst., Beaverton, OR, USA

Volume

1

fYear

2001

fDate

2001

Firstpage

488

Abstract

We present a method for optimal control of MIMO non-linear systems based on a combination of a neural network (NN) feedback controller and a state-dependent Riccati equation (SDRE) controller. Optimization of the NN is performed within a receding horizon model predictive control (MPC) framework, subject to dynamic and kinematic constraints. The SDRE controller augments the NN controller by providing an initial feasible solution and improving stability. The resulting technique is applied to a 6 degree of freedom (DOF) model of an autonomous helicopter

Keywords

MIMO systems; Riccati equations; aircraft control; dynamics; feedback; helicopters; kinematics; neurocontrollers; nonlinear control systems; optimisation; predictive control; 6 DOF helicopter model; MIMO nonlinear systems; autonomous helicopter; dynamic constraints; kinematic constraints; model predictive neural control; neural network feedback controller; optimal control; receding horizon model predictive control framework; state-dependent Riccati equation controller; Adaptive control; Constraint optimization; Control systems; MIMO; Neural networks; Nonlinear control systems; Nonlinear equations; Optimal control; Predictive models; Riccati equations;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2001. Proceedings of the 2001

Conference_Location

Arlington, VA

ISSN

0743-1619

Print_ISBN

0-7803-6495-3

Type

conf

DOI

10.1109/ACC.2001.945592

Filename

945592