Intelligent Control of Human Prosthetic Eye Movements System for the Emotional Support by a Huggable Pet-Type Robot via Gaussian RBF Neural Network Based on Sliding Mode Control

In this study, intelligent control of human eye movements system for the emotional support by a huggable pet type robot via Gaussian RBF neural network based on sliding mode control is presented. Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics. By using Gaussian RBF Neural Networks Based on Sliding Mode Control, the control goal can be achieved. The adaptation laws of designed controllers are obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method is capable to control of human eye movements system. Intelligent methods such as artificial neural networks are proposed to control the human eye movements and numerical simulations are presented. It is concluded that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system.