## 1 Introduction
formulated a nonlinear model predictive control algorithm based on successive linearization, where the extended Kalman filter uses a multi-step future state prediction [41] Almost 10 year later, in 2002, [PERSON] et al. [42] proposed an optimal model predictive control for high order plants modeled first-order linear systems with time-delay and random disturbance including non-gaussian noise, while the Kalman filter was used for present and future state estimation. By 2007, model predictive control (MPC) with Kalman filters was appearing in undergraduate theses [43]. Published in 2012, Reference [44] deals with the closed-loop robust stability of nonlinear model predictive control (NMPC) coupled with an extended Kalman filter (EKF) where optimization was used for target setting used in the objective function in nonlinear model predictive control, while, unfortunately, stability deteriorates due to the estimation error. In 2013, predictive control was used for the constrained Hammerstein-Wiener model of nonlinear chemical processes (e.g., continuous stirred tank reactors) with a Kalman filter for output feedback of a linear subsystem elaborated from a finite horizon through the receding horizon [45]. In that same year, Reference [46] focused on state estimation, comparing the Kalman filter and the Particle Filter, while state estimation with finite state models was also briefly considered. The Bayesian approach was emphasized. In 2016, [PERSON] et al. sought to improve economic performance of batch processes using the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. Model mismatch of the plant preclude achievement of optimality. However, neglecting process noise and assuming small parametric variations, re-optimization always improves performance assuming the trajectory is feasible and, thus, [PERSON] et al. assert that this illustrates robustness. This manuscript will re-examine this goal instead of seeking to achieve robustness by including feedback that directly derives from the original optimization problem (whose results are codified in time-parameters rather than state parameters). [47] [PERSON] et al. represented uncertainties using scenarios from a robust nonlinear model's predictive control strategy, where large-scale stochastic programming is used at each sampling instance solved by the parallel Schur complement method. The method merely exceeds the robustness of an open-loop optimal control, and, furthermore, processing time remains a limitation while achieving an optimal update only twice per minute. [48] [PERSON] et al. used a model predictive control system developed to control furnace exit temperature to prevent parts from overheating in a quench hardening process. Knowing only a proportional state and time, a mere 5.3% efficiency increase was achievable [49]. In 2017, [PERSON] et al. used nonlinear model predictive control combined with parameter and state estimation techniques to provide dynamic process feedback to counter uncertainty. To counter uncertainty, ubiquitous dual methods provide excitation with the control while minimizing a given objective. [PERSON] et al. proposed to sequentially solve robust optimal control, optimal experimental design, and state and parameter estimation problems separating the control and experimental design problems, and permitting independent analysis of sampling. This proved very useful when fast feedback was needed. One such case is the Lotka-Volterra fishing benchmark where the Jost algorithm indicates a 36% reduction of parameter [50]. [PERSON] et al. used the Discrete Mechanics and Optimal Control (DMOC) method to formulate a nonlinear model predictive control, using numerical simulations to compare with the standard nonlinear model predictive control (NMPC) formulation based on the method of multiple-shooting.