Switching frequency regulation in sliding mode control by a hysteresis band controller

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFixing the switching frequency is a key issue in sliding mode control implementations. This paper presents a hysteresis band controller capable of setting a constant value for the steady-state switching frequency of a sliding mode controller in regulation and tracking tasks. The proposed architecture relies on a piecewise linear modeling of the switching function behavior within the hysteresis band, and consists of a discrete-time integral-type controller that modifies the amplitude of the hysteresis band of the comparator in accordance with the error between the desired and the actually measured switching period. For tracking purposes, an additional feedforward action is introduced to compensate the time variation of the switching function derivatives at either sides of the switching hyperplane in the steady state. Stability proofs are provided, and a design criterion for the control parameters to guarantee closed-loop stability is subsequently derived. Numerical simulations and experimental results validate the proposal.Accepted versio

A Virtual Space Vectors based Model Predictive Control for Three-Level Converters

Three-phase three-level (3-L) voltage source converters (VSC), e.g., neutral-point clamped (NPC) converters, T-type converters, etc., have been deemed to be suitable for a wide range of medium- to high-power applications in microgrids (MGs) and bulk power systems. Compared to their two-level (2-L) counterparts, adopting 3-L VSCs in the MG applications not only reduces the voltage stress across the power semiconductor devices, which allows achieving higher voltage levels, but also improves the quality of the converter output waveforms, which further leads to considerably smaller output ac passive filters. Various control strategies have been proposed and implemented for 3-L VSCs. Among all the existing control methods, finite-control-set model predictive control (FCS-MPC) has been extensively investigated and applied due to its simple and intuitive design, fast-dynamic response and robustness against parameter uncertainties. However, to implement an FCS-MPC on a 3-L VSC, a multi-objective cost function, which consists of a term dedicated specifically to control the dc-link capacitor voltages such that the neutral-point voltage (NP-V) oscillations are minimized, must be designed. Nevertheless, selecting proper weighting factors for the multiple control objectives is difficult and time consuming. Additionally, adding the dc-link capacitor voltages balancing term to the cost function distributes the controller effort among different control targets, which severely impacts the primary goal of the FCS-MPC. Furthermore, to control the dc-link capacitor voltages, additional sensing circuitries are usually necessary to measure the dc-link capacitor voltages and currents, which consequently increases the system cost, volume and wiring complexity as well as reduces overall reliability. To address all the aforementioned challenges, in this dissertation research, a novel FCS-MPC method using virtual space vectors (VSVs), which do not affect the dc-link capacitor voltages of the 3-L VSCs, was proposed, implemented and validated. The proposed FCS-MPC strategy has the capability to achieve inherent balanced dc-link capacitor voltages. Additionally, the demonstrated control technique not only simplifies the controller design by allowing the use of a simplified cost function, but also improves the quality of the 3-L VSC output waveforms. Furthermore, the execution time of the proposed control algorithm was significantly reduced compared to that of the existing one. Lastly, the proposed FCS-MPC using the VSVs reduces the hardware cost and complexity as the additional dc-link capacitor voltages and current sensors are not required, which further enhances the overall system reliability

Model predictive control: a review of its applications in power electronics

Model-based predictive control (MPC) for power converters and drives is a control technique that has gained attention in the research community. The main reason for this is that although MPC presents high computational burden, it can easily handle multivariable case and system constraints and nonlinearities in a very intuitive way. Taking advantage of that, MPC has been successfully used for different applications such as an active front end (AFE), power converters connected to resistor inductor RL loads, uninterruptible power supplies, and high-performance drives for induction machines, among others. This article provides a review of the application of MPC in the power electronics area

Modelling and simulation of voltage source converter with different control technique for offshore windfarm application

The uses of Voltage Source Converter is not something new in wind farm power generation. Advance technologies in rebuilding VSC that can control the output of alternating current (AC) through several current control technique produces from the wind energy, sometime lead to distortion that can disturb another component whether in VSC itself or effecting another equipment. In wind farm power generation, power VSC commonly used in order to deal with large value of voltage and current produced from wind energy. Hence, more electronic device and components will be used to deal with this situation. This is to some extent will create disturbances to the system that will not be seen through human naked eye. Through this paper, a conventional current control method, that is linear PWM is present. This method is much more complicated compare to proposed method, that is predictive current control. The function of multi predictive control (MPC) in determining the future value for the feedback signal into inverter is explained in 4.3. The result of this two method is analyse in aspect of total harmonic distortion (THD) for both voltage and current during transient state and steady state. During transient state for current, both of the methods exceed the IEEE-519 standards. Hence it is recommend to install several equipments to protect the facility of windfarm itself. While for steady state, both of the methods succeed maintain the THD below 5% for voltage and current

Modelling and Model Predictive Control of Power-Split Hybrid Powertrains for Self-Driving Vehicles

Designing an autonomous vehicle system architecture requires extensive vehicle simulation prior to its implementation on a vehicle. Simulation provides a controlled environment to test the robustness of an autonomous architecture in a variety of driving scenarios. In any autonomous vehicle project, high-fidelity modelling of the vehicle platform is important for accurate simulations. For power-split hybrid electric vehicles, modelling the powertrain for autonomous applications is particularly difficult. The mapping from accelerator and brake pedal positions to torque at the wheels can be a function of many states. Due to this complex powertrain behavior, it is challenging to develop vehicle dynamics control algorithms for autonomous power-split hybrid vehicles. The 2015 Lincoln MKZ Hybrid is the selected vehicle platform of Autonomoose, the University of Waterloo’s autonomous vehicle project. Autonomoose required high-fidelity models of the vehicle’s power-split powertrain and braking systems, and a new longitudinal dynamics vehicle controller. In this thesis, a grey-box approach to modelling the Lincoln MKZ’s powertrain and braking systems is proposed. The modelling approach utilizes a combination of shallow neural networks and analytical methods to generate a mapping from accelerator and brake pedal positions to the torque at each wheel. Extensive road testing of the vehicle was performed to identify parameters of the powertrain and braking models. Experimental data was measured using a vehicle measurement system and CAN bus diagnostic signals. Model parameters were identified using optimization algorithms. The powertrain and braking models were combined with a vehicle dynamics model to form a complete high-fidelity model of the vehicle that was validated by open-loop simulation. The high-fidelity models of the powertrain and braking were simplified and combined with a longitudinal vehicle dynamics model to create a control-oriented model of the vehicle. The control-oriented model was used to design an instantaneously linearizing model predictive controller (MPC). The advantages of the MPC over a classical proportional-integral (PI) controller were proven in simulation, and a framework for implementing the MPC on the vehicle was developed. The MPC was implemented on the vehicle for track testing. Early track testing results of the MPC show superior performance to the existing PI that could improve with additional controller parameter tuning

Model predictive current control based on a generalised adjacent voltage vectors approach for multilevel inverters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163810/1/pel2bf01679.pd

Contrôle avancé des convertisseurs de puissance multi-niveaux pour applications sur réseaux faibles

139 p.El advenimiento progresivo de las microrredes que incorporan fuentes de energía renovable está dando lugar a un nuevo paradigma de distribución de la electricidad. Este nuevo planteamiento sirve de interfaz entre consumidores no controlados y fuentes intermitentes, implicando desafíos adicionales en materia de conversión, almacenamiento y gestión de la energía.Los convertidores de potencia se adaptan en consecuencia, en particular con el desarrollo de los convertidores multinivel, que integrando los mismos componentes que sus predecesores y un control más complejo, soportan potencias más altas y aseguran una mejor calidad de la energía.Debido al carácter híbrido de los convertidores de potencia, su control se divide comúnmente en dos partes: por un lado, el control de los objetivos continuos vinculados a la función principal de los convertidores de servir de interfaz, y, por otro, el control discreto de los interruptores de potencia, conocido con el nombre de modulación.En este contexto, las exigencias crecientes en términos de eficiencia, fiabilidad, versatilidad y rendimiento hacen necesaria una mejora de la inteligencia de la estructura de control. Para cumplir conestos requisitos, se propone tratar mediante un solo controlador ambas problemáticas, la vinculada a la función de interfaz de los convertidores y la relacionada con su naturaleza discreta. Esta decisión implica incorporar la no-linealidad de los convertidores de potencia en el controlador, lo que equivale a suprimir el bloque de modulación, que constituye la solución tradicional para linealizar el comportamiento interno de los convertidores. Se adopta un planteamiento de Control Predictivo basado en Modelos (MPC) para abordar la no-linealidad y la gran diversidad de objetivos de control que acompañan a los convertidores de potencia.El algoritmo desarrollado combina teoría de grafos ¿con algoritmos de Dijkstra, A* y otros¿ con un modelo de estado especial para sistemas conmutados al objeto de proporcionar una herramienta potente y universal, capaz de manipular simultáneamente el carácter cuantificado de los interruptores de potencia y el continuo de las entidades interconectadas por el convertidor. Se han obtenido resultados sobre la estabilidad y la controlabilidad de los modelos de estado conmutados aplicados al caso particular de los convertidores de potencia.El controlador así desarrollado y descrito se ha examinado en simulación frente a varios casos y aplicaciones: inversor aislado o conectado a la red, rectificador y convertidor bidireccional. Se ha empleado la misma estructura de control para tres topologías de convertidor multinivel: Neutral-Point Clamped, Flying Capacitor y Cascaded H-Bridge. Al objeto de adaptarse a los cambios citados, lo único que varía en el controlador es el modelo del convertidor adoptado para la predicción, así como la función de coste, que traduce los requisitos de control en un problema de optimización a solucionar por el algoritmo. Un cambio de topología resulta en una modificación del modelo interno, sin impacto sobre la función de coste, mientras que variaciones de esta función son suficientes para adaptarse a la aplicación.Los resultados muestran que el controlador logra actuar directamente sobre los interruptores de potencia en función de diversos requisitos. Los desempeños de la estructura de control propuesta son similares a los de las numerosas estructuras dedicadas a cada uno de los casos estudiados, excepto en el caso de operación en modo rectificador, en el que la versatilidad y rapidez de control obtenidos son particularmente interesantes.En definitiva, el controlador planteado puede emplearse para diferentes aplicaciones, topologías, objetivos y limitaciones. Si bien las estructuras de control lineal tradicionales han de modificarse, a menudo en profundidad, para afrontar diferentes modos de operación o requisitos de control, dichas alteraciones no tienen ningún impacto sobre la arquitectura del controlador MPC obtenido, lo que pone de manifiesto su versatilidad, así como su universalidad, también demostrada por su capacidad para adaptarse a diferentes convertidores de potencia sin modificaciones importantes. Finalmente, la solución propuesta elude por completo la complejidad de la modulación, ofreciendo simplicidad y flexibilidad al diseño del control

Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl