## 1 Introduction
Using the Real-Time Iteration scheme, the real-time application of the NMPC algorithm extended the DMOC based NMPC applied to the swing-up and stabilization task of a double pendulum on a cart with limited rail length to show its capability for controlling nonlinear dynamical systems with input and state constraints [51]. [PERSON] et al. presented a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrated it on a large case study with an economic cost function solving a sequence of quadratic programs to trace the optimal nonlinear model's predictive control solution along a parameter change incorporating strongly-active inequality constraints included as equality constraints in the quadratic programs, while the weakly-active constraints are left as inequalities [52]. [PERSON] et al. designed an economic model's predictive control to asymptotically achieve optimality in the presence of plant-model mismatch using an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of a plant-model mismatch [53]. Last year, in 2018, [PERSON] et al. addressed a data-driven model-based economic model and predictive control capable of stabilizing a system at an unstable equilibrium point [54]. [PERSON] et al. applied model predictive control to pharmaceutical manufacturing operations using physics-based models akin to the technique adopted in this study from deterministic artificial intelligence [37]. [PERSON] et al. developed a nonlinear systems framework for understanding cyberattack-resilience of process and control designs and indicates through an analysis of three control designs for resiliency [55]. [PERSON] utilized model predictive control for hydraulic fracturing based on the physics-based assumptions that the rock mechanical properties, such as the Young's modulus, are known and spatially homogenous, where optimality is forsaken if there is an uncertainty in the rock mechanical properties by proposing an approximate dynamic programming-based approach. This is a model-based control technique, which combines a high-fidelity simulation and function approximate to alleviate the \"curse-of-dimensionality\" associated with the traditional dynamic programming approach [33]. [PERSON] et al. addressed the problem of fault-tolerant stabilization of nonlinear processes subject to input constraints, control actuator faults, and limited sensor-controller communication by using the fault-tolerant Lyapunov-based model [36]. [PERSON] et al. applied model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled hetero-directional hyperbolic partial differential equations (PDEs) and ordinary differential equations (ODEs), which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs) and transport systems given by hetero-directional hyperbolic PDEs, with these two regions combined through the PDEs boundaries. Cayley-Tustin time discretization transformation was used to develop a discrete model's predictive control. In this article, discretization delays are implemented alone without specified treatment to evaluate their deleterious effects on the control's accuracy and robustness [56]. [PERSON] et al. considered a distributed model predictive controller on a class of large-scale systems, which is composed of many interacting subsystems. Each of them is controlled by an individual controller where, by integrating the steady-state calculation, the designed controller proved able to guarantee the recursive feasibility and asymptotic stability of the closed-loop system in the cases of both tracking the set point and stabilizing the system to zeroes [34]. [PERSON] et al.