Experimentally validated continuous-time repetitive control of non-minimum phase plants with a prescribed degree of stability

This paper considers the application of continuous-time repetitive control to non-minimum phase plants in a continuous-time model predictive control setting. In particular, it is shown how some critical performance problems associated with repetitive control of such plants can be avoided by use of predictive control with a prescribed degree of stability. The results developed are first illustrated by simulation studies and then through experimental tests on a non-minimum phase electro-mechanical system

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

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Controllers, observers, and applications thereof

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed

Voltage balancing in three-level neutral-point-clamped converters via Luenberger observer

This paper addresses the problems associated with the dc-link capacitor voltages of the three-level neutral-point-clamped power converter: the imbalance of the capacitor voltages as well as the presence of an ac-voltage low-frequency oscillation in the dc link of the converter. In order to cope with them, a mathematical analysis of the capacitor voltage difference dynamics, based on a direct average continuous model, is carried out, considering a singular perturbation approach. The analysis leads to a final expression where a sinusoidal disturbance appears explicitly. Consequently, the two problems can be handled together using the ordinary formulation of a problem of regulating the output of a system subject to sinusoidal disturbances, applying classical control theory to design the controller. In this way, the controller is designed including the disturbance estimate provided by a Luenberger observer to asymptotically cancel the disturbance, while keeping also balanced the capacitor voltages. Experiments for a synchronous three-level neutral-point-clamped converter prototype are carried out to evaluate the performance and usefulness of the converter working as a grid-connected inverter under the proposed control law.MICINN-FEDER DPI2009-09661Junta de Andalucía P07-TIC-0299

A current-source DC-AC converter and control strategy for grid-connected PV applications

This paper presents a two-stage current-source DC-AC converter for grid-connected PV applications which is composed of an input step-up stage, followed by a step-down stage and an unfolding inverter. A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest using an Incremental Conductance MPPT algorithm and synthesizing an output current to be injected into the grid with low harmonic distortion. Double-loop PI controllers are used for the boost stage. The DC bus voltage of the buck stage is regulated using a PI controller, and an inner Proportional-Resonant (PR) controller tracks a sinusoidal reference. The PR controller proposed in this paper, includes a reduced number of resonant stages meeting the energy quality required by standards, which results in good stability margins. Finally, a SOGI-FLL algorithm synchronizes the inverter operation with the grid. Experimental results show an excellent dynamic response of the system, and the injected current complies with the IEEE Std. 1547–2018 specifications regarding harmonic content using a control law with a low computational burden.Fil: Buzzio, Christian. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Grupo de Electrónica Aplicada; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: Poloni, Yamil Sergio. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: Oggier, Germán Elías. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: García, Guillermo Osvaldo. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; Argentin

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

Development of Robust Control Schemes with New Estimation Algorithms for Shunt Active Power Filter

The widespread use of power electronics in industrial, commercial and even residential electrical equipments causes deterioration of the quality of the electric power supply with distortion of the supply voltage. This has led to the development of more stringent requirements regarding harmonic current generation, as are found in standards such as IEEE-519. Power Quality is generally meant to measure of an ideal power supply system. Shunt active power filter (SAPF) is a viable solution for Power Quality enhancement, in order to comply with the standard recommendations. The dynamic performance of SAPF is mainly dependent on how quickly and how accurately the harmonic components are extracted from the load current. Therefore, a fast and accurate estimation algorithm for the detection of reference current signal along with an effective current control technique is needed in order for a SAPF to perform the harmonic elimination successfully. Several control strategies of SAPF have been proposed and implemented. But, still there is a lot of scope on designing new estimation algorithms to achieve fast and accurate generation of reference current signal in SAPF. Further, there is a need of development of efficient robust control algorithms that can be robust in face parametric uncertainties in the power system yielding improvement in power quality more effectively in terms of tracking error reduction and efficient current harmonics mitigation. The work described in the thesis involves development of a number of new current control techniques along with new reference current generation schemes in SAPF. Two current control techniques namely a hysteresis current control (HCC) and sliding mode control (SMC) implemented with a new reference current generation scheme are proposed. This reference generation approach involves a Proportional Integral (PI) controller loop and exploits the estimation of the in phase fundamental components of distorted point of common coupling (PCC) voltages by using Kalman Filter (KF) algorithm. The KF-HCC based SAPF is found to be very simple in realization and performs well even under grid perturbations. But the slow convergence rate of KF leads towards an ineffective reference generation and hence harmonics cancellation is not perfect. Therefore, a SMC based SAPF is implemented with a faster reference scheme based on the proposed Robust Extended Complex Kalman Filter (RECKF) algorithm and the efficacy of this RECKF-SMC is compared with other variants of Kalman Filter such as KF, Extended Kalman Filter (EKF) and Extended Complex Kalman Filter (ECKF) employing simulations as well as real-time simulations using an Opal-RT Real-Time digital Simulator. The RECKF-SMC based SAPF is found to be more effective as compared to the KF-HCC, KF-SMC, EKF-SMC and ECKF-SMC. Subsequently, predictive control techniques namely Dead Beat Control (DBC) and Model Predictive Control (MPC) are proposed in SAPF along with an improved reference current generation scheme based on the proposed RECKF. This reference scheme is devoid of PI controller loop and can self-regulate the dc-link voltage. Both RECKF-DBC and RECKF-MPC approaches use a model of the SAPF system to predict its future behavior and select the most appropriate control action based on an optimality criterion. However, RECKF-DBC is more sensitive to load uncertainties. Also, a better compensation performance of RECKF-MPC is observed from the simulation as well as real-time simulation results. Moreover, to study the efficacy of this RECKF-MPC over PI-MPC, a comparative assessment has been performed using both steady state as well as transient state conditions. From the simulation and real-time simulation results, it is observed that the proposed RECKF-MPC outperforms PI-MPC. The thesis also proposed an optimal Linear Quadratic Regulator (LQR) with an advanced reference current generation strategy based on RECKF. This RECKF-LQR based SAPF has better tracking and disturbance rejection capability and hence RECKF-LQR is found to be more efficient as compared to RECKF-SMC, RECKF-DBC and RECKF-MPC approaches. Subsequently, two robust control approaches namely Linear Quadratic Gaussian (LQG) servo control and H∞ control are proposed in SAPF with highly improved reference generation schemes based on RECKF. These control strategies are designed with the purpose of achieving stability, high disturbance rejection and high level of harmonics cancellation. From simulation results, they are not only found to be robust against different load parameters, but also satisfactory THD results have been achieved in SAPF. A prototype experimental set up has been developed in the Laboratory with a dSPACE-1104 computing platform to verify their robustness. From both the simulation and experimentation, it is observed that the proposed RECKF-H∞ control approach to design a SAPF is found to be more robust as compared to the RECKF-LQG servo control approach in face parametric uncertainties due to load perturbations yielding improvement in power quality in terms of tracking error reduction and efficient current harmonics mitigation. Further, there is no involvement of any voltage sensor in this realization of RECKF-H∞ based SAPF resulting a more reliable and inexpensive SAPF system. Therefore, superiority of proposed RECKF-H∞ is proved amongst all the proposed control strategies of SAPF