Precise torque control for interior mounted permanent magnet synchronous motors with recursive least squares algorithm based parameter estimations

Interior mounted permanent magnet (IPM) machines have superior features comparing to their counterparts for electric vehicle traction applications. Having relatively higher efficiency, high torque and power densities, low torque ripple and not requirement of regular maintenance are among their superior features. It is widely known that the precise torque control in practical IPM drives highly relies on accurate knowledge of machine parameters viz, inductance values and magnetic flux linkage. These machine parameters vary significantly in real time operation depending on manufacturing tolerance, operating temperature, inductance saturation, load torque and so on. It is known that d-and q-axis inductances and magnetic flux linkage at the full load operation may be approximately 20%, 35% and 20%, respectively, lower than their actual values at no-load operation. It is also commonly known in the literature for traction applications that these variations (considering wide range operation) have much influence on both drive system efficiency and output torque production compared to other system nonlinearities such as stator resistance variation. It has been achieved in this paper that these parameters are estimated online with fairly high accuracies of each, utilizing recursive least squares (RLS) algorithm. The superiority of the proposed drive achieving considerably higher output torque (-28,7% of peak torque) is validated through extensive realistic simulations with nonlinear machine model of a 4.1 kW prototype IPM machine designed and manufactured for traction applications. Proposed strategy and its superiority among state-of-art drives are discussed in detail. (c) 2021 Karabuk University. Publishing services by Elsevier B.V

Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers

Advance control of a synchronous reluctance motor drive

This thesis investigates two predictive control algorithms designed to enhance the performance of a synchronous reluctance motor drive. In particular, a finite-control set solution approach has been followed. In particular, this thesis proposes the inclusion of integral terms into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracy. In addition, a control effort term is also considered in the online optimization definition to achieve a quasi-continuous time digital controller given the high achievable ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional predictive controller solution over a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives. In addition, this thesis investigates an innovative duty cycle calculation method for a continuous-control set model predictive control. The formulation of the duty cycles, as well as the introduction of integral terms, enable good reference tracking performance with zero steady-state error at fixed switching frequency over the whole current operating range. Low current ripple with smooth and fast dynamics are achievable, making the proposed control algorithm suitable as a valid alternative in synchronous reluctance motor drives over the established control methods. Simulations and experimental results show the effectiveness and the advantages of the proposed control algorithm over the benchmark

Predictive current control in electrical drives: an illustrated review with case examples using a five-phase induction motor drive with distributed windings

The industrial application of electric machines in variable-speed drives has grown in the last decades thanks to the development of microprocessors and power converters. Although three-phase machines constitute the most common case, the interest of the research community has been recently focused on machines with more than three phases, known as multiphase machines. The principal reason lies in the exploitation of their advantages like reliability, better current distribution among phases or lower current harmonic production in the power converter than conventional three-phase ones, to name a few. Nevertheless, multiphase drives applications require the development of complex controllers to regulate the torque (or speed) and flux of the machine. In this regard, predictive current controllers have recently appeared as a viable alternative due to an easy formulation and a high flexibility to incorporate different control objectives. It is found however that these controllers face some peculiarities and limitations in their use that require attention. This work attempts to tackle the predictive current control technique as a viable alternative for the regulation of multiphase drives, paying special attention to the development of the control technique and the discussion of the benefits and limitations. Case examples with experimental results in a symmetrical five-phase induction machine with distributed windings in motoring mode of operation are used to this end

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

Advance control of a synchronous reluctance motor drive

This thesis investigates two predictive control algorithms designed to enhance the performance of a synchronous reluctance motor drive. In particular, a finite-control set solution approach has been followed. In particular, this thesis proposes the inclusion of integral terms into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracy. In addition, a control effort term is also considered in the online optimization definition to achieve a quasi-continuous time digital controller given the high achievable ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional predictive controller solution over a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives. In addition, this thesis investigates an innovative duty cycle calculation method for a continuous-control set model predictive control. The formulation of the duty cycles, as well as the introduction of integral terms, enable good reference tracking performance with zero steady-state error at fixed switching frequency over the whole current operating range. Low current ripple with smooth and fast dynamics are achievable, making the proposed control algorithm suitable as a valid alternative in synchronous reluctance motor drives over the established control methods. Simulations and experimental results show the effectiveness and the advantages of the proposed control algorithm over the benchmark

Simplified Finite-State Predictive Torque Control Strategies for Induction Motor Drives

This thesis develops a simplified finite-state predictive torque control (FS-PTC) algorithm based on selected prediction vectors (SPVs). This reduces the number of voltage vectors required to be predicted and the objectives to be controlled. The sign of torque or stator flux deviation and the position of stator flux are used to select the prediction vectors. The proposed SPVs strategy also assists reducing the average switching frequency for a two-level voltage source inverter fed induction motor (IM) drive. As a result, the cost function is simplified, as the frequency term is not required. The proposed SPVs based FS-PTC is also applied to a three-level neutral-point clamped inverter driven IM drive. Using the SPVs strategy reduces the computational burden for the proposed three-level inverter fed drive without affecting the system performance. However, an appropriate weighting factor is required for torque and flux errors in the cost function. This leads to the development of a second simplified FS-PTC which does not require complex torque calculations in the prediction loop and hence tuning effort on the weighting factor. A new reference stator flux vector calculator (RSFVC) with an inner proportional-integral torque regulator is employed to convert the torque and flux amplitude references into an equivalent stator flux reference vector. This stator flux reference is used in the cost function for the flux error calculation. The required processing power for the RSFVC-based FS-PTC is further reduced by combining it with the SPVs strategy. Finally, a speed-sensorless simplified FS-PTC of IM supplied from a 3L-NPC inverter is proposed. The sensorless simplified FS-PTC yields improved torque, flux and speed responses, especially at low-speed. The proposed simplified FS-PTC strategies in terms of computational efficiency, cost function design, torque and flux responses, robustness and average switching frequency are validated through experimental results

Direct Torque Control for Silicon Carbide Motor Drives

Direct torque control (DTC) is an extensively used control method for motor drives due to its unique advantages, e.g., the fast dynamic response and the robustness against motor parameters variations, uncertainties, and external disturbances. Using higher switching frequency is generally required by DTC to reduce the torque ripples and decrease stator current total harmonic distortion (THD), which however can lower the drive efficiency. Through the use of the emerging silicon carbide (SiC) devices, which have lower switching losses compared to their silicon counterparts, it is feasible to achieve high efficiency and low torque ripple simultaneously for DTC drives. To overcome the above challenges, a SiC T-type neutral point clamped (NPC) inverter is studied in this work to significantly reduce the torque and flux ripples which also effectively reduce the stator current ripples, while retaining the fast-dynamic response as the conventional DTC. The unbalanced DC-link is an intrinsic issue of the T-type inverter, which may also lead to higher torque ripple. To address this issue, a novel DTC algorithm, which only utilizes the real voltage space vectors and the virtual space vectors (VSVs) that do not contribute to the neutral point current, is proposed to achieve inherent dc-link capacitor voltage balancing without using any DC-link voltage controls or additional DC-link capacitor voltages and/or neutral point current sensors. Both dynamic performance and efficiency are critical for the interior permanent-magnet (IPM) motor drives for transportation applications. It is critical to determine the optimal reference stator flux linkage to improve the efficiency further of DTC drives and maintain the stability of the drive system, which usually obtained by tuning offline and storing in a look-up table or calculated online using machine models and parameters. In this work, the relationship between the stator flux linkage and the magnitude of stator current is analyzed mathematically. Then, based on this relationship, a perturb and observe (P&O) method is proposed to determine the optimal flux for the motor which does not need any prior knowledge of the machine parameters and offline tuning. However, due to the fixed amplitude of the injected signal the P&O algorithm suffers from large oscillations at the steady state conditions. To mitigate the drawback of the P&O method, an adaptive high frequency signal injection based extremum seeking control (ESC) algorithm is proposed to determine the optimal reference flux in real-time, leading to a maximum torque per ampere (MTPA) like approach for DTC drives. The stability analysis and key parameters selection for the proposed ESC algorithm are studied. The proposed method can effectively reduce the motor copper loss and at the same time eliminate the time consuming offline tuning effort. Furthermore, since the ESC is a model-free approach, it is robust against motor parameters variations, which is desirable for IPM motors

Application of Optimal Switching Using Adaptive Dynamic Programming in Power Electronics

In this dissertation, optimal switching in switched systems using adaptive dynamic programming (ADP) is presented. Two applications in power electronics, namely single-phase inverter control and permanent magnet synchronous motor (PMSM) control are studied using ADP. In both applications, the objective of the control problem is to design an optimal switching controller, which is also relatively robust to parameter uncertainties and disturbances in the system. An inverter is used to convert the direct current (DC) voltage to an alternating current (AC) voltage. The control scheme of the single-phase inverter uses a single function approximator, called critic, to evaluate the optimal cost and determine the optimal switching. After offline training of the critic, which is a function of system states and elapsed time, the resulting optimal weights are used in online control, to get a smooth output AC voltage in a feedback form. Simulations show the desirable performance of this controller with linear and nonlinear load and its relative robustness to parameter uncertainty and disturbances. Furthermore, the proposed controller is upgraded so that the inverter is suitable for single-phase variable frequency drives. Finally, as one of the few studies in the field of adaptive dynamic programming (ADP), the proposed controllers are implemented on a physical prototype to show the performance in practice. The torque control of PMSMs has become an interesting topic recently. A new approach based on ADP is proposed to control the torque, and consequently the speed of a PMSM when an unknown load torque is applied on it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilized to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field-oriented control (FOC) and direct torque control based on space vector modulation (DTC-SVM). Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modeled dynamics