Custom optimization algorithms for efficient hardware implementation

The focus is on real-time optimal decision making with application in advanced control systems. These computationally intensive schemes, which involve the repeated solution of (convex) optimization problems within a sampling interval, require more efficient computational methods than currently available for extending their application to highly dynamical systems and setups with resource-constrained embedded computing platforms. A range of techniques are proposed to exploit synergies between digital hardware, numerical analysis and algorithm design. These techniques build on top of parameterisable hardware code generation tools that generate VHDL code describing custom computing architectures for interior-point methods and a range of first-order constrained optimization methods. Since memory limitations are often important in embedded implementations we develop a custom storage scheme for KKT matrices arising in interior-point methods for control, which reduces memory requirements significantly and prevents I/O bandwidth limitations from affecting the performance in our implementations. To take advantage of the trend towards parallel computing architectures and to exploit the special characteristics of our custom architectures we propose several high-level parallel optimal control schemes that can reduce computation time. A novel optimization formulation was devised for reducing the computational effort in solving certain problems independent of the computing platform used. In order to be able to solve optimization problems in fixed-point arithmetic, which is significantly more resource-efficient than floating-point, tailored linear algebra algorithms were developed for solving the linear systems that form the computational bottleneck in many optimization methods. These methods come with guarantees for reliable operation. We also provide finite-precision error analysis for fixed-point implementations of first-order methods that can be used to minimize the use of resources while meeting accuracy specifications. The suggested techniques are demonstrated on several practical examples, including a hardware-in-the-loop setup for optimization-based control of a large airliner.Open Acces

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Sensorless Commissioning and Control of High Anisotropy Synchronous Motor Drives

L'abstract è presente nell'allegato / the abstract is in the attachmen

Learning For Predictive Control: A Dual Gaussian Process Approach

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166

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

A Highly Reliable Propulsion System with Onboard Uninterruptible Power Supply for Train Application:Topology and Control

Providing uninterrupted electricity service aboard the urban trains is of vital importance not only for reliable signaling and accurate traffic management but also for ensuring the safety of passengers and supplying emergency equipment such as lighting and signage systems. Hence, to alleviate power shortages caused by power transmission failures while the uninterruptible power supplies installed in the railway stations are not available, this paper suggests an innovative traction drive topology which is equipped by an onboard hybrid energy storage system for railway vehicles. Besides, to limit currents magnitudes and voltages variations of the feeder during train acceleration and to recuperate braking energy during train deceleration, an energy management strategy is presented. Moreover, a new optimal model predictive method is developed to control the currents of converters and storages as well as the speeds of the two open-end-windings permanent-magnet-synchronous-machines in the intended modular drive, under their constraints. Although to improve control dynamic performance, the control laws are designed as a set of piecewise affine functions from the control signals based on an offline procedure, the controller can still withstand real-time non-measurable disturbances. The effectiveness of proposed multifunctional propulsion topology and the feasibility of the designed controller are demonstrated by simulation and experimental results

Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded