Dynamic Performance Analysis of a Five-Phase PMSM Drive Using Model Reference Adaptive System and Enhanced Sliding Mode Observer

This paper aims to evaluate the dynamic performance of a five-phase PMSM drive using two different observers: sliding mode (SMO) and model reference adaptive system (MRAS). The design of the vector control for the drive is firstly introduced in details to visualize the proper selection of speed and current controllers’ gains, then the construction of the two observers are presented. The stability check for the two observers are also presented and analyzed, and finally the evaluation results are presented to visualize the features of each sensorless technique and identify the advantages and shortages as well. The obtained results reveal that the de-signed SMO exhibits better performance and enhanced robustness compared with the MRAS under different operating conditions. This fact is approved through the obtained results considering a mismatch in the values of stator resistance and stator inductance as well. Large deviation in the values of estimated speed and rotor position are observed under MRAS, and this is also accompanied with high speed and torque oscillations

Lead pursuit control of multiphase drives

Los accionamientos multifásicos, compuestos por una máquina eléctrica de más de tres fases alimentada por un convertidor de potencia, han atraído recientemente un importante interés en la comunidad investigadora debido a las ventajas que presentan frente a las máquinas trifásicas convencionales. Este es el caso de la mejor distribución de potencia por fase, la menor producción de armónicos en el convertidor de potencia y, la más importante, la tolerancia a fallos, lo cual significa que la máquina multifásica puede seguir funcionando cuando una o varias fases se pierden, siempre que el número restante de fases sea igual o mayor que tres. Debido a esta alta fiabilidad, los accionamientos multifásicos son especialmente adecuados para aplicaciones relacionadas con los vehículos eléctricos (terrestres, marítimos y aéreos) y las energías renovables por razones de seguridad y/o económicas. El uso de controladores avanzados y de alto rendimiento en accionamientos multifásicos es particularmente relevante, ya que las estrategias de control convencionalmente aplicadas a los accionamientos trifásicos no terminan de alcanzar un estándar en su extensión al caso multifásico. La razón es la mayor complejidad y número de variables a controlar. En este contexto, los controladores predictivos han encontrado un interesante nicho de aplicación en convertidores de potencia y accionamientos multifásicos debido a su formulación intuitiva y flexible: un modelo del sistema es usado para calcular las predicciones de las variables controladas, que luego se comparan con las referencias impuestas dentro de una función de coste. Esta estrategia permite incorporar varios objetivos de control y restricciones en el proceso de control a través de la función de coste. Sin embargo, es bien sabido que este tipo de controlador sufre de un alto coste computacional y contenido armónico de corriente que limita su aplicación en los accionamientos multifásicos. La investigación desarrollada en esta Tesis se centra en la mitigación de las limitaciones citadas siguiendo dos objetivos principales: • La incorporación de observadores de corrientes rotóricas en el controlador predictivo para mejorar así la precisión del modelo predictivo y, consecuentemente, el rendimiento del sistema de control, principalmente en términos de contenido armónico y pérdidas por conmutación en el convertidor de potencia. Un observador de Luenberger es construido para este propósito utilizando una estrategia innovadora de posicionamiento de polos en su diseño. • La introducción de un grado de libertad adicional en el controlador predictivo basado en tiempos de muestreo variables e implementado usando el concepto de lead pursuit. El resultado es un controlador novedoso que conduce a una resolución en los tiempos de conmutación más fina en comparación con las técnicas predictivas más convencionales, lo que proporciona una reducción importante en el contenido armónico. Las estrategias de control propuestas son validadas mediante simulación y experimentación utilizando un accionamiento compuesto por una máquina de inducción de cinco fases como caso de ejemplo. Los resultados y conclusiones derivadas de esta investigación han sido presentados en cinco trabajos principales publicados en revistas internacionales de alto impacto, los cuales constituyen las contribuciones de esta Tesis por compendio de artículos. Sin embargo, otros trabajos relacionados con la línea de investigación han sido también publicados en artículos de revista y conferencia y en un capítulo de libro.Multiphase drives, constituted by an electric machine with more than three phases fed by a power converter, have recently attracted an important interest in the research community due to the advantages that they present over the conventional three-phase ones. This is the case of the better power distribution per phase, the lower harmonic production in the power converter, and the most important one, the fault-tolerant capability, which means that the multiphase machine can still be operated when one or several phases are missing, provided that the number of remaining phases is equal or greater than three. Due to this high reliability, multiphase drives are specially well suited for applications related to electric vehicles (terrestrial, maritime and aerial) and renewable energies for safety and/or economical reasons. The use of advanced and high-performance controllers in multiphase drives is particularly relevant, since the control strategies conventionally applied to three-phase drives do not reach a standard in their extension to the multiphase case. The reason is the greater complexity and number of variables that must be controlled. In this context, predictive controllers have found an interesting niche of application in power converters and multiphase drives due to their intuitive and flexible formulation: a model of the system is used to compute predictions of the controlled variables, which are later compared with the imposed references in a cost function. This strategy permits incorporating several control objectives and constraints in the control process through the cost function. However, it is well known that this type of controller suffers from a high computational cost and current harmonic content that limit its application in multiphase drives. The research developed in this Thesis work is focused on the mitigation of the cited limitations following two main goals: • The incorporation of rotor current observers in the predictive controller in order to improve the accuracy of the predictive model and, consequently, the control system performance, principally in terms of harmonic content and commutation losses in the power converter. A Luenberger observer is constructed for that purpose using an innovative pole-placement strategy in its design. • The introduction of an additional degree of freedom in the predictive controller based on variable sampling times and implemented using the lead-pursuit concept. The result is a novel controller that leads to a finer resolution in the commuting times in comparison with more conventional predictive techniques, which provides an important reduction in the harmonic content. The proposed control strategies are validated by simulation and experimentation using a five-phase induction machine drive as case example. The results and conclusions derived from this research have been presented in five main works published in high-impact international journals, which constitute the contributions of this article compendium Thesis. Nevertheless, other related works have also been published in journal and conference papers and a book chapter

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance

Bipedal Walking Analysis, Control, and Applications Towards Human-Like Behavior

Realizing the essentials of bipedal walking balance is one of the core studies in both robotics and biomechanics. Although the recent developments of walking control on bipedal robots have brought the humanoid automation to a different level, the walking performance is still limited compared to human walking, which also restricts the related applications in biomechanics and rehabilitation. To mitigate the discrepancy between robotic walking and human walking, this dissertation is broken into three parts to develop the control methods to improve three important perspectives: predictive walking behavior, gait optimization, and stepping strategy. To improve the predictive walking behavior captured by the model predictive control (MPC) which is transitionally applied with the nonlinear tracking control in sequence, a quadratic program (QP)-based controller is proposed to unify center of mass (COM) planning using MPC and a nonlinear torque control with control Lyapunov function (CLF). For the gait optimization, we focus on the algorithms of trajectory optimization with direct collocation framework. We propose a robust trajectory optimization using step-time sampling for a simple walker under terrain uncertainties. Towards generating human-like walking gait with multi-domain (phases), we improve the optimization through contact with more accurate transcription method for level walking, and generalize the hybrid zero dynamics (HZD) gait optimization with modified contact conditions for walking on various terrains. The results are compared with human walking gaits, where the similar trends and the sources of discrepancies are identified. In the third part for stepping strategy, we perform step estimation based on capture point (CP) for different human movements, including single-step (balance) recovery, walking and walking with slip. The analysis provides the insights of the efficacy and limitation of CP-based step estimation for human gait

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles has accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. The design and implementation of an optimized control strategy is a complex challenge. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. Real-time strategies incorporate methods such as drive cycle prediction algorithms, parameter feedback, driving pattern recognition algorithms, etc. The goal of this work is to use a previously defined strategy which has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS) [1], which uses an equivalence factor to define the control strategy. The equivalence factor essentially defines the torque split between the electric motor and internal combustion engine. Consequently, the equivalence factor greatly affects fuel economy. An equivalence factor that is optimal (with respect to fuel economy) for a single drive cycle can be found offline – with a priori knowledge of the drive cycle. The RBF ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data are used to train the RBF ANN, each set contains characteristics from a different drive cycle. Each drive cycle is characterized by 9 parameters. For each drive cycle, the optimal equivalence factor is determined and included in the training data. The performance of the RBF ANN is evaluated against the fuel economy obtained with the optimal equivalence factor from the ECMS. For the majority of drive cycles examined, the RBF ANN implementation is shown to produce fuel economy values that are within +/- 2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF ANN is that it does not require a priori drive cycle knowledge and is able to be implemented real time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF ANN could be improved to produce better results across a greater array of driving conditions

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method