Neural Network Controller for Minimizing Hub Shear Forces in Helicopter - Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational

A Constrained Simultaneous Perturbation Stochastic Approximation Algorithm Based on Penalty Function - Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational

1.1. Available Techniques In this paper, we present a stochastic approximation algorithm based on penalty function method and a simultaneous perturbation gradient estimate for solving stochastic optimization problems with general inequality constraints. We also presents a very general convergence result for the proposed algorithm.

Design of an Intelligent Controller for Station Keeping, Attitude Control, and Path Tracking of a Quadrotor Using Recursive Neural Networks

During recent years there has been growing interest in unmanned aerial vehicles (UAVs). Moreover, the necessity to control and navigate these vehicles has attracted much attention from researchers in this field. This is mostly due to the fact that the interactions between turbulent airflows apply complex aerodynamic forces to the system. Since the dynamics of a quadrotor are non-linear and the ...

Intelligent Auto pilot Design for a Nonlinear Model of an Autonomous Helicopter by Adaptive Emotional Approach

There is a growing interest in the modeling and control of model helicopters using nonlinear dynamic models and nonlinear control. Application of a new intelligent control approach called Brain Emotional Learning Based Intelligent Controller (BELBIC) to design autopilot for an autonomous helicopter is addressed in this paper. This controller is applied to a nonlinear model of a helicopter. This...

Neural networks for modelling and control of a non-linear dynamic system - Intelligent Control, 1992., Proceedings of the 1992 IEEE International Symposium on

Hybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...