## 1 Introduction  
FCS-MPC has several distinguishing characteristics, including quick response, simple concept, ease of constraint inclusion, and nonlinearity [1, 2]. However, due to the action of the FCS-MPC, which is delivered straight forward to the converter switches without any modulation stage, it has the disadvantage of variable switching frequency [3]. Several challenges may develop when the converter runs at a variable switching frequency, including circuit resonance, magnetic component and filter complexity, and a lack of power quality for critical applications [4, 5]. There are numerous approaches to adjust the frequency and obtain a fixed switching action for converters controlled by the FCS-MPC. The first method is to include an extra term in the cost function that accounts for the converter switches' previous switching patterns [6]. In addition to the fundamental disadvantage of not being able to choose from a restricted number of acceptable switching states due to the lack of a modulator, this method necessitates an additional weighting component, which could make the tuning of cost function more difficult [7]. Another option is to employ band-stop filters (i.e., notch filters) to regulate the harmonics. This strategy may provide problems with stability, particularly in grid-connected applications [8]. The alternative way is to employ the periodic control technique, which is based on assessing up and down switching events to achieve the modulation behavior [9]. In this procedure, a correctly tuned factor is also required for many operations. Another option is to remove the excess term and use a modulation technique like space vector modulation (SVM) [10]. In this scenario, the FCS-MPC and SVM combination provides a decent performance in terms of fast response and fixed-switching frequency [11]. In this approach, there are numerous ways to compute the dwell times associated with each applied vector. One is based on the traditional SVM, while others use virtual methods to calculate dwell time appropriately [12]. The research community's most recent tendency is to fix the FCS-MPC switching frequency, which is based on the use of modulated MPC [13]. Approaches such as discrete space vector [14], deadbeat predictive control [15; 16], and predictive PWM methods result in intricate expressions for switching time calculations [17; 18]. Moreover, they are not intuitive, because introducing other objectives into the cost function are difficult during the control design [19; 20]. In such methods, the optimal vector is not applied during the whole sampling period, but it is applied in a part of the sampling interval, while in the remaining part, another vector can be applied. To address these issues, a novel method has been presented that allows operation at a fixed switching frequency, while keeping the benefits of the FCS-MPC controller [21]. This method uses a linear PI controller to simulate the implementation of SVM by using two active vectors and two zero vectors. The time of each vector should be determined and optimized using the same methodology as the conventional SVPWM to reach a certain goal. Dwell times are what they are called, and this is referred to as MMPC or M\({}^{2}\)PC [21]. On the other hand, the use of artificial neural networks (ANNs)-based approaches in the field of power electronics is rapidly growing. The use of neural networks for dynamical system control was first proposed in the early 1990s [22]. Multi-layer perceptions were used for a variety of tasks, including system identification and control law implementation. ANN-based controllers and estimators, in particular, have been widely employed to identify and operate power converters and motor drives [23]. The advantages of ANN-based controllers include: they can generally improve the performance of the system when properly tuned, and they can be created based on data received from a real system or plant in the lack of expert knowledge. They do, however, necessitate a considerable amount of training data.