Publication Type:

Conference Paper

Source:

2009 2nd International Workshop on Nonlinear Dynamics and Synchronization (2009)

URL:

https://ieeexplore.ieee.org/document/5227994

Keywords:

adaptive control, adaptive controller, Adaptive systems, Artificial intelligence, Artificial Neural Network, Artificial Neural Network (ANN), Artificial neural networks, control system synthesis, Control systems, controller design, Learning systems, neurocontrollers, Non-linear control system, nonlinear control systems, Nonlinear systems, online training, Programmable control, real time recurrent learning algorithm, Real Time Recurrent Learning Algorithm (RTRL), Real time systems, recurrent neural network, Recurrent neural networks, state feedback, synaptic weights adaptation

Abstract:

The paper presents artificial neural network (ANN) based adaptive controller for nonlinear systems. A state feedback adaptive control algorithm using fully connected recurrent neural network is employed. The desired trajectory for the on-line training of the neural network is obtained from a reference model. The synaptic weights adaptation of the network is based on real time recurrent learning algorithm (RTRL). Since the synaptic weights are adjusted in real time, this novel method of controller design has potential applications in non-linear systems. Simulation results of the controller applied to a simple non-linear dynamic system demonstrate the effectiveness of the controller.

Cite this Research Publication

Dr. Sindhu Thampatty K.C., Nandakumar, M. P., and Cheriyan, E. P., “ANN based adaptive controller tuned by RTRL algorithm for non-linear systems”, in 2009 2nd International Workshop on Nonlinear Dynamics and Synchronization, 2009.