**1. Introduction**

Recently, the Direct Torque Control (DTC) of electrical machines has taken the attention of several researchers, thanks to its interest and advantages, like the simple structure, the fast torque response, and the less dependence on machine parameters [1–3]. The structure of the classical DTC is mainly based on two hysteresis controllers and a lookup table to independently control the torque and the flux by selecting the optimal voltage vector in each sampling period. The classical DTC suffers from several problems like the torque ripples, the harmonics in the stator current waves, as well as the variation in the switching frequency. The fixed bands of the hysteresis controllers are the main cause of these problems [4, 5].

In the recent years, several methods have been put forward for overcoming the classical DTC problems, such as the use of intelligent techniques like the artificial neural networks and the fuzzy logic [6, 7]. However, the experimental implementation of the intelligent techniques requires powerful calculation processes due to their complexity. The torque and flux ripples, and the stator current waveform distortions can be reduced by fixing the switching frequency and selecting the more appropriate voltage vector for each commutation period. Indeed, in order to impose an operation with a fixed switching frequency, a combination between the DTC and the Space Vector Modulation (SVM) has been proposed by several research studies [8–10]. In fact, the DTC with a fixed switching frequency consists in introducing two Proportional Integral PI controllers and a SVM technique to achieve the best choice of the voltage vector in each sampling period [8–10]. However, the stability and dynamics of the system will be affected by the variation in machine parameters due to the existence of the PI controllers. In order to get rid of the drawbacks of the mentioned techniques, several robust control techniques have been proposed in order to guarantee the high performance control of induction motor drives. Among of these techniques we can cite the sliding mode control, the backstepping control and the Input–Output Feedback Linearization (IOFL) approach [11–13], which are the most popular control strategies. IOFL consists in transforming a nonlinear system into an equivalent linear one, which can be utilized for controlling the system [14]. IOFL is based on an inverse mathematical transformation for obtaining a suitable control law of the Induction Motor (IM).

The main first objective of this chapter consists in combining the IOFL technique and an SVM-DTC (SVM-DTC-IOFL) in order to design a novel DTC strategy featured by fast torque and speed responses, more robustness under stator resistance variations, reduced ripples and distortions, and a decoupled control between the torque and the flux. In this study, the stator flux and the electromagnetic torque are chosen as control states to develop the decoupled model of the IM.

For real time control of electrical machines, digital electronic boards like the STM32-microcontrollers [15, 16] and the Digital Signal Processor (DSP) are usually utilized [17–20]. The digital circuits based on microprocessors are known by their sequential computation of the control algorithm which consequently increases the execution time and the sampling period when the complexity of the control algorithm increases. Indeed, if the sampling time raises, the delays in the control system goes up, this causes additional ripples and distortions in the torque and the current, respectively. Moreover, the DSP controllers are chosen for implanting the control algorithms of electrical systems [21, 22], which are based on processor cores with high performance and few peripherals to communicate with the external environment. In fact, the sampling period of the processor depends of the computational burden due to the parallel processing, which creates delays in the feedback loop and raises the stator current harmonics and the torque ripples [23–25].

With the target of overcoming the DSP limitation and minimizing the DSP computational burden, a combination between the DSP and the FPGA has been proposed in the literature [26, 27] with the purpose of distributing the computational burden between these two digital controllers. This solution offers better performance by reducing the sampling period, the ripples in the torque and the distortions in the stator current.

### *Robust Control Based on Input-Output Feedback Linearization for Induction Motor Drive… DOI: http://dx.doi.org/10.5772/intechopen.104645*

However, the main limitations of this solution are the high cost and the complexity of circuit's connections, which causes problems for commercialization. In order to overcome the limitations of the cited solutions, the FPGA can be used only for controlling the motor drives. Indeed, thanks to its hardware architecture, the FPGA offers good performance by reducing the execution time and consequently the delays in the retroaction loop. In the last few years, the DSP (DSPACE 1104) has been suggested and confirmed by several engineers and researchers for real time control of AC machines [24, 28, 29]. In the same context, the FPGA can overcome the software solution drawbacks by adopting parallel processing [30–33]. In fact, the FPGA offers the designer the possibility of implementing in a low sampling period, control techniques with good performance and high algorithmic complexities. Indeed, in [32], the authors have implemented a control algorithm of an IM using an FPGA under a sampling period of 5 μs [32].

The second objective of this chapter consists in implementing the proposed SVM-DTC-IOFL on an FPGA board. For the hardware implementation on the FPGA, the SVM-DTC-IOFL must be transformed into VHDL or Verilog description languages. Indeed, VHDL or Verilog programming is a difficult task which raises the design time, the time to market and the system cost. In this chapter, a graphical programming method based on Xilinx System Generator (XSG) is utilized in order to reduce the prototyping time. In fact, the graphical architecture from the XSG under a Matlab/Simulink-tool makes it possible to generate the VHDL of the Verilog code, as well as the programming bitstream files [33–35]. The XSG is a toolbox created by the Xilinx engineers' team, which operates between Matlab and Vivado tools, whose objective is to facilitate the programming tasks and reduce the time to market [35].

In this chapter, SVM-DTC-IOFL is theoretically developed, designed from the XSG tool, and verified by digital simulation utilizing a Xilinx Zynq FPGA.

This work is composed of five sections. In Section 2, the state mode of an induction motor drive, the SVM technique principle and the suggested IOFL theory are presented. In Section 3, designs from the XSG of the proposed SVM-DTC-IOFL and simulation results are shown. The implementation and synthesis results are given in Section 4. The conclusion is summarized in Section 5.
