## 1 Introduction
It has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. Reference [7] presents a case study in the automotive industry of an integrated system in the design, measure, analyze, improve, and control cycle, allowing for the representation of expert knowledge. The authors in [8] present an optimization method for a fuzzy logic controller used for a differential drive wheeled mobile robot. In [9], a fuzzy logic controller based on an analytic activation function for the defuzzification procedure, with Gaussian shape of input fuzzy sets and a 2 D fuzzy rule base, is applied to the position control of a servo-pneumatic drive. In [10], a fuzzy logical frame is used to control the angular momentum of left and right wheels of a robot. The solution is able to drive efficiently the robot inside curved paths, avoiding obstacles. In [11] a complex control method based on a combination of methods--fuzzy control, time delay estimation, deep learning and a non-dominated sorting genetic algorithm--are used at the nonlinear active power train mount system. The authors in [12] present a comparison of position control of a gyroscopic inverted pendulum using PID, fuzzy logic, and fuzzy PID controllers, and assure stabilization and disturbance rejection. Alternative approaches to fuzzy logic such as neural networks can perform just as well as fuzzy logic in many cases. Neural networks may be applied for dynamic system identification, modeling, prediction, and control. Neural networks are deep learning computing systems characterized by the ability to learn from examples. Their use enables the behavior of complex systems to be modeled, predicted, and controlled, these tasks being achieved through training, without a priori information about the system's structure and parameters. The model predictive control is an advanced method of process control that is used to control a process while satisfying a set of constraints. Model predictive controllers rely on dynamic models of process, obtained by system identification. The main advantage of model predictive control is the fact that it allows the current timeslot to be optimized, while keeping future timeslots into account. The authors in [13] conduct a review of model predictive control systems from an engineering perspective. Model-based predictive control consists of a set of advanced control methods that make use of a process model to predict the future behavior of the control system. The control system solves a constrained optimization problem and determines the control law implicitly. This method allows control of processes which cannot be controlled by conventional feedback control systems. The authors in [14] present a solution to using a neural network as a model predictive controller of a nonlinear system with constrains. The neural network works as a function approximator, and it is trained to minimize a cost function. This solution assures the construction of reduced-order and decentralized controllers with arbitrary structures. The authors in [15] show that model predictive control may be used for control of nonlinear processes with uncertain models and for processes where the time constants are large. A deep neural network is used to implement the controller, and as an effect, the computational cost of implementing is reduced. The authors in [16] applied a predictive algorithm based on multiple neural networks for simulation of dynamics of a ground vehicle and controlling a multi-domain battle space. The simulation was made using MATLAB/Simulink and demonstrated a reduction in wasted energy in variable operating conditions. In [17], the authors employ statistical machine learning theory to develop a methodological framework of generalization error bounds for recurrent neural networks.