An adaptive robust position control for real-time high-performance applications of induction motors is developed in this work. The proposed sliding-mode controller provides a global asymptotic position tracking in the presence of model uncertainties and load torque variations. The proposed control scheme incorporates an adaptation law for the switching gain, so that the controller can calculate the switching gain value that is necessary to overcome the existing system uncertainties. The design also incorporates a sliding-mode-based load torque and rotor flux observers in order to improve the control performance without using sensors that increase cost and reduce reliability. The proposed design does not present a high computational cost and, therefore, can be easily implemented in real-time applications. Simulated and experimental results show that this scheme provide high-performance dynamic characteristics, and that it is robust with respect to plant parameter variations and external load disturbances.

This paper presents the design and the experimental validation of a new linear multivariable generalized predictive control for speed and rotor flux of induction motor. This control approach has been designed in the d-q rotating reference frame, and the indirect vector control has been employed. Load and flux observers, as well as the possibility of including a model-reference-adaptive-system speed estimator, have been considered in the implementation. The proposed controller not only provides enhanced dynamic performance but also guarantees compliance with physical voltage and current constraints. Hence, it ensures that the space vector pulsewidth modulation (SVPWM) always operates in the linear area and that the stator windings are not damaged due to overcurrent. Moreover, the controller includes a novel torque current tracker that allows obtaining an effective electromagnetic torque without a chattering phenomenon. Several simulation and experimental tests have been carried out, both in suitable and adverse conditions, even at zero speed zone, demonstrating that the proposed controller provides an efficient speed tracking and suggesting its use in industry.

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

In this paper, an enhanced Integral Sliding Mode Control (ISMC) for mechanical speed of an Induction Motor (IM) is presented and experimentally validated. The design of the proposed controller has been done in the d-q synchronous reference frame and indirect Field Oriented Control (FOC). Global asymptotic speed tracking in the presence of model uncertainties and load torque variations has been guaranteed by using an enhanced ISMC surface. Moreover, this controller provides a faster speed convergence rate compared to the conventional ISMC and the Proportional Integral methods, and it eliminates the steady-state error. Furthermore, the chattering phenomenon is reduced by using a switching sigmoid function. The stability of the proposed controller under parameter uncertainties and load disturbances has been provided by using the Lyapunov stability theory. Finally, the performance of this control method is verified through numerical simulations and experimental tests, getting fast dynamics and good robustness for IM drives.