## 1 Introduction
[8], the predictive control model approach was applied to the induction motor drive system that controls its torque and current. Using the linear state-space representation (currents were state variables), the simulation results were shown. In ref. [9], the implementation of an optimal control based on quadratic criteria was presented in a low-power electric drive with a three-phase induction motor. The dSPACE 1104 controller was used to obtain the optimal online solution of the Riccati matrix differential equation. A linear model was considered, and its state variables were the rotor speed and the angular position of the rotor field. Based on the optimal solution and the corresponding feedback signals, the optimal speed reference for the AC drive system was obtained and tracked by the Altivar 71 inverter. It was compared with the inner rotor field oriented control and the PI controller of the Altivar 71 inverter. Experimental results of speed and load torque regulation were presented. In ref. [10], a robust adaptive speed position tracking control was presented for a mathematical model of the linear fifth-order induction motor mathematical model with unknown end effects and secondary resistance. Other works related to the synthesis of advanced control strategies considering the full state (fifth-order) nonlinear model of the induction motor have been developed. For example, in ref. [11], a nonlinear predictive control was synthesized to regulate an induction motor in a water pumping system. This scheme was validated via simulations. Another similar work on predictive control is ref. [12], as well as applications of motor control such as ref. [13]. In ref. [14], a discrete-time sliding mode control of an induction motor was designed to regulate the rotor speed and stator current. Real-time experiments were carried out using a dSpace system with the DS1104 control board based on the TMS320F240 digital signal processor. In ref. [15], an indirect rotor flux oriented control and backstepping control of a five-phase induction motor drive were presented. The performance of both controllers was analyzed through experimental validation. Most of the synthesized control strategies in the specialized literature were validated via simulation results [7, 11] or by experiments using a test bench [2, 3, 4, 5, 6, 10, 14, 15] without the use of an industrial inverter. In fact, there are few projects that use industrial VFDs that incorporate advanced control techniques [9], possibly because industrial VFDs generally have a closed architecture. For instance, in ref. [16], a remote control system for an induction motor was presented using a PLC OMRON CJ1M- CPU11-ETN21 (Kyoto, Japan) and LabVIEW software. A VFD OMRON SYSDRIVE 3G3 MV-A2007 was used to change the speed control. The whole system was in an open loop since there was no practical data measurement from the actual output of the motor. In ref. [17], the VFD Altivar 71 was preconfigured to accept speed reference via the Modbus communication protocol from a Raspberry Pi board; however, there was no closed-loop system, and neither control strategy was mentioned. In ref. [18], an induction motor (three-phase, 380 V, 50 Hz, four-pole, low-voltage squirrel cage) drove a conveyor belt for a bottle filling plant. The open loop control of motor speed and direction control was performed using data sent through a PLC (Schneider Electric Modicon TM241 CEC24R (Rueil-Malmaison, France)) connected for serial communication (RS485 port) to the VFD (ATV12H075M2), whose parameters (speed, direction, acceleration, and deceleration) were changed and monitored using the Modbus protocol to communicate with a SCADA server. The dynamic programming approach was used as a base to develop the nonlinear control for the Baldor IDNM3534 induction motor, which was modeled using the Clarke transformation from three phases into two phases [1], leading to an equivalent representation given by an affine nonlinear fifth-order model.