A comparison of nonlinear filtering methods for blackbody radiation applications in photonics

Gadsden

Energy Harvesting and Storage: Materials, Devices, and Applications XIII

2023

The sliding innovation filter (SIF) is a recently developed estimation technique that has gained widespread use. It is a predictor-corrector filter that utilizes a hyperplane and applies a force to allow estimates to fluctuate about it. SIF belongs to the same family as the smooth variable structure filter and sliding mode observer, and it is stable and robust in the face of uncertainties. This paper discusses the use of SIF for estimating the states of Power Converters, which play a crucial role in Electric Vehicles (EVs) by converting high-voltage DC from the battery to low-voltage AC used by the motor. One of the main challenges in Power Converters is accurately estimating their states, such as input voltage, output voltage, and inductor current, which are critical for optimal control and efficient operation. The SIF has demonstrated promising results in addressing this challenge.

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter to estimate power converters of electric vehicles

Gadsden

Energy Harvesting and Storage: Materials, Devices, and Applications XIII

2023

“…These approaches examine sensor data to reveal hidden states, system characteristics, and system health, enabling system evolution study and monitoring. Filters cater for sensor reading restrictions and uncertainties to enhance system state assessment accuracy and reliability [5][6][7][8][9][10][11][12][13][14]. Filters minimize sensor data noise, enhancing estimated information and issue and diagnostic discoveries [15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter for micorgrid application

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

El-Nadi

et al. 2023

Currently, microgrids are frequently used and various control algorithms have been applied to improve their performance in both grid-connected and islanded modes. However, research has shown that incorporating a filtering technique into the controller can lead to even better performance. As a result, a simple controller with a filter can perform just as well as a complex controller that operates alone. This study focuses on the performance of a microgrid using a new filter called the sliding innovation filter, which is known for its robustness and stability. The filter is an excellent option for estimating performance under various conditions, such as load and injected powers. To demonstrate the filter's advantages, modeling uncertainties are introduced into the system while the filter is estimating states. The filter's performance is evaluated using a MATLAB© simulation environment.

“…Since estimating approaches account for the constraints and uncertainties associated with sensor readings, the state assessment of the system is both more accurate and reliable. Reduce the influence of noise or disturbances in the sensor data using an estimation approach, such as a filter, to improve the quality of the estimated information and provide more reliable problem and diagnostic findings [1][2][3][4][5][6][7][8][9][10]. Because sensors are inherently limited, their output is often noisy.…”

Section: Introductionmentioning

confidence: 99%

Estimating the complex maneuvering of a UGV using IMM-SIF

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

Gadsden

2023

Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.