## 1 Introduction
The neural models are utilized to predict state evolution in model predictive controllers, under which closed-loop stability is established in a probabilistic manner. In [18], a simplified vehicle dynamics model for a motion planner is designed based on nonlinear model predictive control. Model reference control creates a closed loop controller which tries to compare the output of the plant with a standard reference response and various parameters of the plant may change in practice. In [19], a neural network-based model reference control of braking electric vehicles is presented. The model reference controller is capable of meeting the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The authors in [20] present a collection of references on the modeling of complex systems using artificial intelligence and computational methods. Neural identification is used in [21] for an automotive active suspension control application, with vehicle state estimation, based on a virtual sensor. The study uses a trained neural network model to implement a virtual sensor. The process of development consisted of training, validation, and testing using datasets. The training method was the Bayesian search, and root-mean-square error was used as a metric. Spectral analysis was performed and error histograms were reported. The results demonstrate that a neural network-based sensor can estimate vehicle velocity. In [22], the back propagation neural network is used to predict the maximum Lyapunov exponent under different parameters for robotic arms, and the study demonstrates that with this method, the robotic arm system can be identified more efficiently. The authors in [23] address the control of the position of a magnetic levitation ball, proposing a deep neural network feedforward compensation controller. The controller has in its structure a neural network identifier, a neural network feedforward compensator, and a PID controller. The inverse model of the magnetic levitation ball is used by the neural identifier, trained online. The position control is realized by the feedforward compensator. The authors in [24] present an application of a machine learning algorithm such as artificial networks in the control of a non-linear liquid level system to cover a wider operating range of the plant. The structure of the neural control is designed with minimization of the root-mean-square error and experimental validation. The authors in [25] present a solution based on artificial intelligence techniques developed for real time control of a robotic arm using a neural network-based inverse kinematics solution. The obtained results were compared with the PI control characteristics. The authors in [26] present a model reference controller approach for robot arm tracking using neural networks. The control system uses a neural network as a reference model, trained to follow the desired reference trajectory and a neural network controller, which provides the desired torque. The simulated controller tracks a desired trajectory with least tracking error and minimum control input. The authors in [27] present a model-free predictive control solution for power electronic converters. The control structure contains a recurrent neural network used as a model, trained with particle swarm optimization. The paper demonstrates that the solution is more robust than a conventional model predictive control system. The paper [28] presents a solution for position tracking of mobile manipulators based on two adaptive neural PD controllers. The controllers are trained using a Kalman filter, in order to obtain a faster learning speed.