Computationally Efficient Self-Tuning Controller for DC-DC Switch Mode Power Converters Based on Partial Update Kalman Filter

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a DC-DC switch-mode power converter (SMPC). The proposed estimation algorithm is based on a novel combination between the classical Kalman filter and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the Kalman Filter (KF), with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky PID controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a pre-calculated average model

Non-Invasive Real-Time Diagnosis of PMSM Faults Implemented in Motor Control Software for Mission Critical Applications

This paper presents a non-intrusive, real-time, online Condition Monitoring and FaultDiagnosis system for Permanent Magnet Synchronous Machines. The system utilizesonly the motor drive's built-in sensors, such as current and voltage sensors, to detectthree types of faults: inter-turn short circuit, partial demagnetization, and staticeccentricity. The proposed solution adopts a hardware-free approach, utilizingcurrent/voltage signature analysis to optimize cost-effectiveness. It requires a smallmemory and short execution time, allowing it to be implemented on a simple motorcontroller with limited memory and calculation power. The system is designed forcritical mission applications, and therefore, computation load, code size, memoryallocation, and run-time optimization are key focuses for real-time operation. Theproposed method has a high detection accuracy of 98%, is computationally efficient,and can accurately detect and classify the fault. The system provides immediateinsights into motor health without interrupting the drive operation

Real-time parameter estimation of DC-DC converters using a self-tuned kalman filter

To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method