Jorge Rodas

Bio: Jorge Rodas is an academic researcher from Universidad Nacional de Asunción. The author has contributed to research in topics: Model predictive control & Induction motor. The author has an hindex of 15, co-authored 92 publications receiving 652 citations.

Online Estimation of Rotor Variables in Predictive Current Controllers: A Case Study Using Five-Phase Induction Machines

Abstract: Predictive current control (PCC) has been recently proposed like an alternative to conventional PI-PWM current control techniques. Implemented solutions are based on inaccurate estimation of the rotor electrical variables to reduce the computational cost of the method. In this study, the utility and computational cost of PCC with different methods for the online estimation of the rotor variables are studied. Experimental results are provided to characterize the obtained benefits and drawbacks, using a five-phase induction machine as a case example.

Influence of Covariance-Based ALS Methods in the Performance of Predictive Controllers With Rotor Current Estimation

Abstract: The use of online rotor current estimators with predictive current controllers has been very recently stated in five-phase induction motor drives, where the closed-loop performance of the system is improved by using suboptimal estimators based on Kalman filters. In this paper, the interest of using optimization methods in the definition of the Kalman filter, like the covariance technique, is analyzed. Obtained system performances using optimal and suboptimal rotor current estimators are experimentally compared.

A Novel Modulated Model Predictive Control Applied to Six-Phase Induction Motor Drives

Abstract: Model-based predictive control techniques, with finite set control, are considered an interesting option to control multiphase drives due to their control flexibility and fast dynamic response. However, those techniques have some drawbacks such as a high computational cost, poor ( $x-y$ ) currents reduction, and steady-state error, especially at high speeds. To improve some of these drawbacks, modulation stages have been presented as an alternative. However, some of those drawbacks have not been improved. This article proposes a novel approach to the classic predictive current control (PCC) applied to an asymmetrical six-phase induction machine, where a space vector modulation with specific vectors is used in order to improve the ( $x-y$ ) currents, the steady-state error and total harmonic distortion (THD) at high operation speeds. Experimental results are presented to demonstrate the characteristics of the proposed control technique in terms of current tracking, ( $x-y$ ) currents reduction and THD of stator currents compared to the classic PCC.

Current Control of a Six-Phase Induction Machine Drive Based on Discrete-Time Sliding Mode with Time Delay Estimation

Abstract: This paper proposes a robust nonlinear current controller that deals with the problem of the stator current control of a six-phase induction motor drive. The current control is performed by using a state-space representation of the system, explicitly considering the unmeasurable states, uncertainties and external disturbances. To estimate these latter effectively, a time delay estimation technique is used. The proposed control architecture consists of inner and outer loops. The inner current control loop is based on a robust discrete-time sliding mode controller combined with a time delay estimation method. As said before, the objective of the time delay estimation is to reconstruct the unmeasurable states and uncertainties, while the sliding mode aims is to suppress the estimation error, to ensure robustness and finite-time convergence of the stator currents to their desired references. The outer loop is based on a proportional-integral controller to control the speed. The stability of the current closed-loop system is proven by establishing sufficient conditions on the switching gains. Experimental work has been conducted to verify the performance and the effectiveness of the proposed robust control scheme for a six-phase induction motor drive. The results obtained have shown that the proposed method allows good performances in terms of current tracking, in their corresponding planes.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Big Data Analytics in Intelligent Transportation Systems: A Survey

Abstract: Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Flux Immunity Robust Predictive Current Control With Incremental Model and Extended State Observer for PMSM Drive

Abstract: A robust predictive current control algorithm for permanent magnet synchronous motor (PMSM) drives is proposed. The robust controller adopts an incremental system model, thus can operate without knowing the rotor flux, which is a pivotal parameter in conventional dead-beat control. The incremental model is easy to implement compared with relevant flux identification algorithms or observers. The robust controller adopts an extended state observer (ESO) to enhance inductance robustness to cater for the divergence caused by stator inductance mismatch. The inductance mismatch uncertainties can be estimated and compensated by ESO. Conventional dead-beat control is modified for multistep prediction to achieve high disturbance rejection. Detailed stability and disturbance rejection analysis are conducted theoretically, and rules on parameter selection are given. Comparative simulations and experimentations verify the effectiveness and superiority of proposed robust control over conventional dead-beat control.

A Review on Multiphase Drives for Automotive Traction Applications

Abstract: This article attempts to cover the most recent advancements in multiphase drives (MPDs), which are candidates for replacing three-phase drives in electric vehicle (EV) applications. Multiphase machines have distinctive features that arouse many research directions. This article reviews the recent advancements in several aspects such as topology, control, and performance to evaluate the possibility of exploiting them more in EV applications in future. The six-phase drives are extensively covered here because of their inherent structure as a dual three-phase system, which eases the production process. This article presents different topologies used in dual three-phase drives and the modulation techniques used to operate them as well as the status of using MPDs in traction applications industrially and the upcoming trends toward promoting this technology more.

Optimization techniques to enhance the performance of induction motor drives: A review

Abstract: Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial-and-error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open- and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.