## 1 Introduction
utilized an economic model's predictive control on a nonlinear system class with input constraints for chemical seeking to first guarantee stability, and then ensure optimality (this is the opposite approach taken in this manuscript) [57]. [PERSON] et al. proposed an economic model's predictive control with zone tracking based on economic performance and reachability of the optimal steady-state by decoupling dynamic zone tracking and economic objectives to simplify parameter tuning [58]. [PERSON] et al. sought to augment dissipative theory to propose a novel distributed control framework to simultaneously optimize conflicting cost statements in demand-side management schemes (e.g., building air conditioning), while seeking to mitigate power fluctuations. Their proposed method achieved the objective of renewable intermittency mitigation through proper coordination of distributed controllers, and the solution proved to be scalable and computationally efficient [35]. [PERSON] et al. presented a linear offset-free model predictive controller applied to active magnetic bearings, which exploits the performance and stability advantages of a classical model-based control while overcoming the effects of the plant-model mismatch on reference tracking by incorporating a disturbance observer to augment the plant model equations. Effectiveness of the method was demonstrated in terms of the reference tracking performance, cancellation of plant-model mismatch effects, and low-frequency disturbance estimation [59]. [PERSON] et al. compared model predictive control to bang-bang control and proportional-integral-derivative control of home air conditioning seeking to minimize energy consumption assuming six time-of-use electricity cost rates during an entire summer, while tariff (taxation) rates were evaluated for efficacy [60]. Lastly, this year (in 2019), [PERSON] et al. described a model predictive control based on the technique of a finite control set applied to uninterrupted power supply systems, where the system's mathematical model is used to predict voltages of each switch at each sampling time applied to switching states of parallel inverters. They achieved a low computational burden, good steady state performance, fast transient response, and robust results against parameter disturbances as compared to linear control [61]. Since variable switching frequency negatively impacts output filters, [PERSON] et al. implemented constant switching frequency in a finite control-set model predictive controller based on the gradient descent method to find optimal voltage vectors. They found lower total harmonic distortion in output current with improved computation times [62]. [PERSON] et al. utilized sequential model predictive control for power electronic AC converter, and their research sought to improve a complicated weighting factor design and also computational burden applied to three-phase direct matrix conversion. The performance akin nominal model's predictive control was revealed [63]. [PERSON] et al. introduced approaches to the predictive control of a DC-DC boost converter in a comparison of the controllers that consider all of the current objectives and minimize the complexity of the online processing, which results in an alternative way of building the process model based on the fuzzy identification that contributes to the final objective. This includes the applicability of the predictive methods for fast processes [64]. Seeking to optimize the control strategy of the traction line-side converter (LSC) in high-speed railways, [PERSON] et al. applied a model-based predictive current control and the results indicate a significant influence on the performance of traditional transient direct current control. Both the oscillation pattern and the oscillation peak under traditional transient direct current control can be easily influenced when these parameters change [65]. In 2019, [PERSON] et al. [66] investigated how, when, and why to choose model predictive control over proportional-integral-derivative control.