Author/Authors :
Khati, H Design and Drive of Production Systems Laboratory - Faculty of Electrical and Computing Engineering - University Mouloud Mammeri of Tizi-Ouzou, Tizi-Ouzou, Algeria , Talem, H Design and Drive of Production Systems Laboratory - Faculty of Electrical and Computing Engineering - University Mouloud Mammeri of Tizi-Ouzou, Tizi-Ouzou, Algeria , Mellah, R Design and Drive of Production Systems Laboratory - Faculty of Electrical and Computing Engineering - University Mouloud Mammeri of Tizi-Ouzou, Tizi-Ouzou, Algeria , Bilek, A Laboratory of Mechanical - Structure and Energetic - Faculty of Engineering Construction, University Mouloud Mammeri of Tizi-Ouzou, Tizi-Ouzou, Algeria
Abstract :
This paper presents an adaptive neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) for a bilateral
teleoperation system based on FPGA (Field Programmable Gate Array). The proposed controller combines the learning
capabilities of neural networks with the inference capabilities of fuzzy logic, to adapt with dynamic variations in master
and slave robots and to guarantee good practical robustness against the disturbances, by adjusting neuro-fuzzy network
output parameters in a short time, thanks to the computing power of FPGA and its high sampling frequency. The design
methodology adopted to design the control algorithm aims to minimize the hardware resources used by the FPGA in
order to optimize the execution and the design times, and this by using the Fixed-Point Tool and HDL Coder features
of MATLAB-Simulink. The proposed controllers were experimentally validated on a teleoperation system comprising
a pair of one degree of freedom. The experimental results clearly show that the proposed ANFIS control algorithm
significantly outperformed the conventional control methods (PID).
Keywords :
teleoperation , neuro-fuzzy , HDL Coder , FPGA , Fixed-Point Tool , ANFIS
