Francis Mwasilu

Enhanced Fault-Tolerant Control of Interior PMSMs Based on an Adaptive EKF for EV Traction Applications

This paper proposes an enhanced sensor fault-tolerant control (FTC) scheme of an interior permanent magnet synchronous motor (IPMSM) drive for the electric vehicle (EV) traction applications. For a safe and continuous operation of the modern EV, the drive has to acquire robustness features for position sensor failures. Hence, the proposed FTC is based on an adaptive extended Kalman filter (AEKF), which continuously estimates both the states and covariance matrices that describe the statistic characters of the system. Under a position sensor failure, the proposed FTC scheme instantly detects sensor fault and reconfigures the traction system with a virtual sensor to provide an EV with a necessary limp home capability. Unlike the conventional EKF with fixed covariance matrices, the proposed AEKF exhibits the robustness to the system stochastic noises and the transient operating conditions. Simulation on MATLAB/Simulink and experimental results on the IPMSM test bed with a TMS320F28335 DSP under various transient operating conditions are presented to demonstrate the effectiveness and feasibility of the proposed FTC scheme in comparison to the FTC with the conventional EKF. The comparative results indicate that the proposed AEKF more precisely estimates the rotor position with features robust to the position sensor failures than the conventional EKF.

An Observer-Based Optimal Voltage Control Scheme for Three-Phase UPS Systems

This paper proposes a simple optimal voltage control method for three-phase uninterruptible-power-supply systems. The proposed voltage controller is composed of a feedback control term and a compensating control term. The former term is designed to make the system errors converge to zero, whereas the latter term is applied to compensate for the system uncertainties. Moreover, the optimal load current observer is used to optimize system cost and reliability. Particularly, the closed-loop stability of an observer-based optimal voltage control law is mathematically proven by showing that the whole states of the augmented observer-based control system errors exponentially converge to zero. Unlike previous algorithms, the proposed method can make a tradeoff between control input magnitude and tracking error by simply choosing proper performance indexes. The effectiveness of the proposed controller is validated through simulations on MATLAB/Simulink and experiments on a prototype 600-VA testbed with a TMS320LF28335 DSP. Finally, the comparative results for the proposed scheme and the conventional feedback linearization control scheme are presented to demonstrate that the proposed algorithm achieves an excellent performance such as fast transient response, small steady-state error, and low total harmonic distortion under load step change, unbalanced load, and nonlinear load with the parameter variations.

Doubly fed induction generator wind turbines: A novel integrated protection circuit for low-voltage ride-through strategy

This paper proposes an integrated low-voltage ride-through (ILVRT) scheme to improve the transient responses of the doubly fed induction generator (DFIG) based wind turbines. The proposed strategy integrates a series-dynamic-resistor, a dc-link chopper, and a crowbar (CRW) with a coordinated switching control strategy. Generally, when the CRW short-circuits the rotor windings, the rotor-side power converter (RSPC) is blocked and the DFIG becomes a squirrel-cage induction generator. This temporary configuration acquires its magnetization current from the grid, which leads to a more voltage-dip. On the other hand, if the CRW is combined with the series R-L circuit, the RSPC remains connected to the slip-rings, and hence, the active/reactive (P-Q) control is partially maintained. Moreover, the terminal voltage depression is reduced compared to when only the CRW scheme is applied. Following a brief discussion of two conventional LVRT strategies, the proposed ILVRT scheme is designed with an improved switching control algorithm which minimizes the CRW activation time. By applying the proposed ILVRT approach, the negative effects of the grid faults and two conventional strategies can be avoided. Finally, the performance comparison between the two conventional LVRT strategies and the proposed ILVRT scheme is conducted with the simulation results using MATLAB/Simulink software.

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.

Online Parameter Identification for Model-Based Sensorless Control of Interior Permanent Magnet Synchronous Machine

This paper proposes an online identification method that can accurately estimate the stator resistance and dq-axis stator inductances for the effective model-based sensorless control of interior permanent magnet synchronous motors (IPMSMs). The proposed affine projection algorithms are uniquely designed in the estimated rotating γ-δ frame to precisely identify the parameters mentioned above. The two time-scale approaches are employed in the affine projection algorithms to estimate the three electrical parameters. Despite the electrical parameter variations due to the temperature change and magnetic saturation during operation, the rich enough data are provided to the affine projection algorithms in the discrete-time domain to accurately retrieve the updated parameters. These correctly estimated parameters are adapted to the extended back electromotive force observer for the sensorless control of IPMSM drives. Hence, the adaptation of online updated parameters makes the observer stable and robust to parameter variations as compared to the conventional observer without updated parameters. The MATLAB/Simulink-based simulation results and experimental results via a prototype IPMSM test-bed having TMS320F28335 DSP are given to verify the accurate convergence of the estimated parameters, which results into a stable sensorless control system under various operating conditions.

Design and Stability Analysis of a Fuzzy Adaptive SMC System for Three-Phase UPS Inverter

This paper proposes a combined fuzzy adaptive sliding-mode voltage controller (FASVC) for a three-phase UPS inverter. The proposed FASVC encapsulates two control terms: a fuzzy adaptive compensation control term, which solves the problem of parameter uncertainties, and a sliding-mode feedback control term, which stabilizes the error dynamics of the system. To extract precise load current information, the proposed method uses a conventional load current observer instead of current sensors. In addition, the stability of the proposed control scheme is fully guaranteed by using the Lyapunov stability theory. It is shown that the proposed FASVC can attain excellent voltage regulation features such as a fast dynamic response, low total harmonic distortion (THD), and a small steady-state error under sudden load disturbances, nonlinear loads, and unbalanced loads in the existence of the parameter uncertainties. Finally, experimental results are obtained from a prototype 1 kVA three-phase UPS inverter system via a MS320F28335 DSP. A comparison of these results with those obtained from a conventional sliding-mode controller (SMC) confirms the superior transient and steady-state performances of the proposed control technique.

Finite Set Model Predictive Control of Interior PM Synchronous Motor Drives With an External Disturbance Rejection Technique

This paper designs a predictive speed controller of interior permanent-magnet synchronous motor (IPMSM) based on finite control set (FCS)–model predictive control (MPC). The proposed predictive speed controller has a cascade-free structure that comprises a single predictive control function for the speed and dq-axis stator currents. The future rotor speed and stator currents are predicted at each sampling interval. Then, the proposed predictive control function is evaluated to determine the optimal switching states for the IPMSM drive. In high-performance speed-controlled drive, the unknown load torque disturbance has to be suppressed. Therefore, the load torque disturbance estimator using the two-stage extended Kalman filter (TSEKF) is proposed to enhance the dynamic performance of the IPMSM drive. The proposed TSEKF simultaneously estimates the system states, which greatly improve the model prediction process. The proposed predictive speed control is simple and compact, which demonstrates a faster transient response and a robust feature to mechanical system parameter variations when compared with the conventional cascaded speed control under a wide speed range and load torque variations. The experimental results on a prototype IPMSM drive built on a Texas Instruments TMS320F28335 floating point digital signal processing (DSP) board are presented considering the constant torque and flux-weakening regions to confirm the feasibility of the proposed FCS-MPC scheme.

Intelligent voltage control strategy for three-phase UPS inverters with output lc filter

This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term incorporates an online parameter training methodology, using the gradient descent method and Lyapunov stability theory. Besides, a linear load current observer that estimates the load currents is used to exclude the load current sensors. The proposed SFNN controller and the observer are robust to the filter inductance variations, and their stability analyses are described in detail. The experimental results obtained on a prototype UPS test bed with a TMS320F28335 DSP are presented to validate the feasibility of the proposed scheme. Verification results demonstrate that the proposed control strategy can achieve smaller steady-state error and lower total harmonic distortion when subjected to nonlinear or unbalanced loads compared to the conventional sliding mode control method.

Fuzzy Model Predictive Direct Torque Control of IPMSMs for Electric Vehicle Applications

This paper proposes a fuzzy model predictive direct torque control (FMP-DTC) strategy of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) applications. The fuzzy logic control technique incorporated into the proposed FMP-DTC scheme dynamically determines the appropriate values of the weighting factors, and then generates the optimal switching states that minimize the electromagnetic torque and stator flux errors. Unlike the conventional model predictive (MP)-DTC strategy, the optimal switching states of the proposed FMP-DTC are selected without retuning the weighting factors. It means that they are updated depending on the specific operating conditions. Therefore, the proposed FMP-DTC is effective in various operating conditions that make it suitable for the EV-traction operating environment. Hence, the proposed FMP-DTC method has a simple control structure and can explicitly handle the system constraints. The performance evaluation is carried out via both MATLAB/Simulink and a prototype IPMSM test-bed with a TMS320F28335 digital signal processor (DSP). Comparative simulation and experimental results present the evidence of the performance improvements based on the proposed FMP-DTC strategy compared with the conventional MP-DTC strategy by indicating a fast transient torque response, low ripples, and an accurate speed tracking even under rapid climbing or emergency braking situations.