On-Line FPGA Hardware in the Loop Validation of Based Fuzzy-STPWM Induction Motor Control Abdelghani Aib 1 , Djalal Eddine Khodja 2 , Salim Chakroune 1* , Loutfi Benyettou 1 1 Research Laboratory on the Electrical Engineering, Faculty of Technology, University of M’Sila, M’Sila 28000, Algeria 2 Research Laboratory on the Electrification of Industrial Enterprises, Boumerdes University, Boumerdes 35000, Algeria Corresponding Author Email: salim.chakroun@univ-msila.dz https://doi.org/10.18280/ama_b.641-403 ABSTRACT Received: 28 February 2021 Accepted: 16 June 2021 This work is part of the study and implementation of a fuzzy PWM control structure (fuzzy pulse width modulation) of an induction machine (MAS) on a circuit-based platform reconfigurable FPGA type. We first presented a strategy for the hardware implementation of a fuzzy inference system on a programmable logic circuit of FPGA type, through the hardware description language (VHDL) and Xilinx generator system (XSG). Secondly we proposed Fuzzy-PWM architecture for the improvement of response time of an asynchronous machine (MAS) and finally we validate the proposed hardware co-simulation architecture in real time on the ML402 development kit (based on FPGA Xilinx Virtex-4) and Simulink / Matlab. Keywords: pulse width modulation, fuzzy control, ML402, hardware co-simulation, induction machine control, Xilinx generator system 1. INTRODUCTION The induction machine, by its construction, is the most robust and cheapest machine on the market. The progress made in control and the considerable technological advances, both in the field of power electronics and microelectronics, have made possible the implementation of powerful controls of this machine making it a wonderful competitor in the sectors of variable speed and rapid torque control [1-4]. In recent decades, rather laborious controls have been developed to perform asynchronous machine control using PWM pulse width modulation techniques [5-8]. This advance is mainly due to the evolution of microelectronics such as ASICs, microprocessors and programmable logic circuits [9- 11] that allows complex algorithms to implement the commands of the asynchronous machine [12-14]. In addition, the integration of artificial intelligence techniques to improve the performance of commands is increasingly used [15, 16]. In [1] the author uses genetic algorithms to identify dynamic model parameters of the asynchronous machine and implement controllers, based on fuzzy logic and neural networks within a vector control by rotor field orientation. A new control based on adaptive fuzzy controllers has been proposed by Salim and Thierry [17] fuzzy controllers are implemented for the control of MAS through voltage inverters [18, 19]. The implementation of artificial intelligence techniques on programmable logic circuits FPGA for the control of electrical drive systems such as MAS reduces the computation time generated by the complex algorithms of these techniques [20]. Also make them more suitable applications in real time, methodologies for the implementation of a fuzzy controller on FPGA [21, 22]. An implementation of optimized approximate sigmoid function on FPGA circuit to use in ANN for MAS control and monitoring is detailed in ref. [23]. The objective of this work is the validation of a hardware architecture that allows the acceleration of the response time of an asynchronous machine controlled by Sinus triangle PWM. Through a fuzzy inference system implemented on the ML402 platform with a Xilinx Virtex-4 FPGA, aiming at improving computing time and optimizing hardware resources used by an optimized architecture described in hardware description language VHDL and Xilinx system generator. The development, simulation, synthesis and hardware co- simulation steps are performed using the Xilinx system generator tool on Simulink / Matlab. 2. IMPLEMENTING A FUZZY CONTROLLER ON FPGA The hardware implementation of a fuzzy inference system consists in implementing the three phases of fuzzy logic regulation: Fuzzification, Fuzzy Inference and Defuzzification (Figure 1). Figure 1. The components of a fuzzy inference system 2.1 Fuzzification Fuzzy logic systems deal with fuzzy input variables and provide results on output variables that are themselves fuzzy. Advances in Modelling and Analysis B Vol. 65, No. 1-4, December, 2021, pp. 17-26 Journal homepage: http://iieta.org/journals/ama_b 17