Title : Feasible Performance Evaluations of Digitally-Controlled Auxiliary Resonant Commutation Snubber-Assisted Three Phase Vo

International Journal of Industrial Electronics, Control and Optimization . © 2020 IECO. 160 Controlling the matrix converters using the conventional PI control techniques is a challenging task, since multiple control objectives have to be simultaneously achieved [22]. The decoupled linear control techniques are only valid around an operating point and do not guarantee robustness [17]. The predictive control is of great versatility for its ability to treat with different requirements. The main disadvantage of the predictive control is, however, that a high content of harmonics may be generated at the output of the matrix converter [20]. The sliding mode control which was proposed during the late 1950s, benefits a high disturbance rejection capability, a low sensitivity to the variation of system parameters, and a high-speed response [23]. Due to these advantages as well as easy implementation, the sliding mode controller can be efficiently employed in various industrial control applications [24]-[25]. The main drawback of this controller, however, is that its control signal exhibits high frequency oscillations, which is called the chattering problem [26]. This problem causes undesirable impacts on the control trajectory such as low accuracy and high heat losses in power circuits. Among the various solutions to mitigate the chattering effect, the boundary layer approach is of the most popular one [21]. In the boundary layer approach, a thin boundary layer is considered neighbor the switching surface and makes the system trajectories stay within the predetermined boundary layer. As a result, the control discontinuity is smoothed out in a boundary layer around the switching surface. This solution, however, compromises on accuracy of the trajectory tracking. Another method in literature is high order sliding mode [27]-[29]. In the high order sliding mode, the sliding surface structures are improved at the cost of a higher complexity in the control structure. Combining with signal processing tools such as neural networks [30]-[31] and fuzzy logic controller [32]-[33], the chattering phenomenon can be reduced efficiently. The above hybrid methods use adaptive neural networks or fuzzy logics to approximate the unknown system model. Recently, several hybrid fuzzy SMC methods have been developed. The methods proposed in [34] and [35] utilize fuzzy models to describe some uncertain nonlinear systems. In [36], an adaptive fuzzy approach is employed to approximate the unknown nonlinearity in the presence of signal quantization. In [37], a fuzzy regulator is proposed to adaptively smooth the hitting control and concurrently compensate for observation deviations. The work in [38] uses an adaptive neuro-fuzzy inference system (ANFIS) to estimate unknown part of the system dynamics based on the off-line input-output data. This ANFIS based control system provides more general solution for the singularity and chattering problems. As a result, the sliding gain is adapted to the estimated value of the unknown part and hence, provides a fast convergence and low chattering. However, this requires an appropriate design procedure and a stability proof to guarantee the performance and the stability of the whole hybrid system [39]. In [40], the stability problem of the uncertain fuzzy systems is compensated via an adaptive SMC method. To this end, a fuzzy integral-type sliding surface is utilized and the SMC method is applied to estimate the unknown system parameters. In [41], an adaptive algorithm is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. This procedure avoids large switching gain which can lead to the occurrence of highfrequency sliding mode chattering. Besides, adaptive output feedback SMC is also designed for linear systems with matched external disturbances [42]. These hybrid methods provide partly some advantages over standard SMC such as reduced chattering, non-singularity, high precision, and finite-time convergence. However, these techniques need accurate tuning of controller parameters. In addition, the chattering phenomenon still occurs due to using the discrete control input and as a result, some additional methods are required to meet the chattering problem [38]. In this paper, a new adaptive control method of MC is proposed which is based on interval type-2 fuzzy sliding mode control using indirect SVM. In addition to the input current control development, the chattering phenomenon is reduced through integration of the interval type-2 fuzzy with sliding mode control strategy. In contrast to the previous chattering reduction methods in [21], [27], the proposed fuzzy integration method does not increase the error amplitude of the controller. As a result, the accuracy of the trajectory is enhanced and complex parameter tuning is avoided. In addition to an easy implementation, the interval type-2 fuzzy compensation does not require complicated membership function. Simulation results verify the efficient performance of the proposed adaptive control method for MCs. This paper consists of five sections. The MC model and its inverter and rectifier stage modulations are described in Section 2. The proposed fuzzy sliding mode control for the direct MC is presented in Section 3. The performance assessment of the proposed control system is verified by simulation results in Section 4. Finally, the conclusion is brought in Section 5. II. THE MC MODEL The circuit diagram of an MC is illustrated in Fig. 1. Each of three input phases can be connected to each of three output phases through bidirectional switches. The states of switches are represented as International Journal of Industrial Electronics, Control and Optimization . © 2020 IECO. Vol. 3, No. 2, pp. 159-172, April (2020)