Dynamic Recurrent Neural Networks for Stable Adaptive Control of Wing Rock Motion

Neural-Network-Based Adaptive Control of Wing Rock Motion

The wing rock motion is mathematically described by a nonlinear differential equation with coefficients varying with angle of attack. In this paper, a neural-network-based adaptive control system is developed for the wing rock motion control. The adaptive controller comprises a neural network controller and a compensation controller. The neural network controller using a recurrent neural networ...

Stable Adaptive Control with Recurrent Neural Networks

In this paper, stable indirect adaptive control with recurrent neural networks is presented for multi-input multi-output (MIMO) square non linear plants with unknown dynamics. The control scheme is made of a neural model and a neural controller based on fully connected RTRL networks. On-line weights updating law, closed loop performance, and boundedness of the neural network weights are derived...

Multiple recurrent neural networks for stable adaptive control

It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and when operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks. A hysteresis switching algorithm is applied t...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)

Recurrent neural networks and adaptive motor control

This thesis is concerned with the use of neural networks for motor control tasks. The main goal of the thesis is to investigate ways in which the biological notions of motor programs and Central Pattern Generators (CPGs) may be implemented in a neural network framework. Biological CPGs can be seen as components within a larger control scheme, which is basically modular in design. In this thesis...