Robust cascade controller for the power factor of the three-phase supply and the induction motor velocity.

It is well known that induction motors consume active and reactive energy from the utility grid to operate; additionally, when a power converter drives the motor, a high content of current harmonics is produced, and both circumstances decrease the utility grid power factor, which later requires to be improved. To this end, this paper presents a novel complete solution through a robust control system employed in a back-to-back topology power converter to deliver, instead of consuming, regulated reactive power toward the main grid, which comes from a capacitor bank in a DC-bus. This salient feature of delivering reactive power, and simultaneously, regulating the speed for an induction motor, becomes one of the contributions of this work to enhance the power factor. The robust converter controller is synthesized in a cascade form, by applying the linearization block control and state-feedback techniques. These techniques are combined with the super-twisting strategy for canceling the nonlinearities and the effect of the external disturbances. The complete system consists of a back-to-back converter, an LCL filter coupled to the main grid for mitigating the current harmonic content, and an induction motor under variable load conditions. Experimental tests expose the performance and robustness of the proposed controller, where a robust control for the reactive power acts under sudden changes in the active power produced through abrupt variations in the motor load.

Method to generate a large cohort in-silico for type 1 diabetes.

BACKGROUND AND OBJECTIVE: In the last decade, several technological solutions have been proposed as artificial pancreas systems able to treat type 1 diabetes; most often they are built based on a control algorithm that needs to be validated before it is used with real patients. Control algorithms are usually tested with simulation tools that integrate mathematical models related mainly to the glucose-insulin dynamics, but other variables can be considered as well. In general, the simulators have a limited set of subjects. The main goal of this paper is to propose a new computational method to increase the number of virtual subjects, with physiological characteristics, included in the original mathematical models. METHODS: A subject is defined by a set of parameters given by a mathematical model. From the available reduced number of subjects in the model, the covariance of each parameter of every subject is obtained to establish a mathematical relationship. Then, new sets of parameters are calculated using linear regression methods; this generates larger cohorts, which allows for testing insulin therapies in open-loop or closed-loop scenarios. The new method proposed here increases the number of subjects in a virtual cohort using two versions of Hovorka's mathematical model. RESULTS: Two covariant cohorts are obtained with linear regression. Both cohorts are clustered to avoid overlapping in the glucose-insulin dynamics and are compared in terms of their qualitative and quantitative behaviours in the normoglycemic range. As a result, there have been generated two larger cohorts (256 subjects) than the original population, which contributes to improving the variability in in-silico tests. In addition, for analysing the characteristics of the covariant generation method, two random cohorts have been generated, where the parameters are obtained individually and independently from each other, exhibiting only distribution limitations so that these cohorts do not have physiological subjects. CONCLUSIONS: The proposed methodology has enabled the generation of a large cohort of 256 subjects, with different characteristics that are plausible in the T1DM population, significantly increasing the number of available subjects in existing mathematical models. The proposed methodology does not limit the number of subjects that can be generated and thus, it can be used to increase the number of cohorts provided by other mathematical models in diabetes, or even other scientific problems.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.

A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.