A Study of Variable Structure and Sliding Mode Filters for Robust Estimation of Mechatronic Systems

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

Obaideen

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

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

Obaideen

2023

“…Filtering estimating methods that employ stability functions like Lyapunov functions fall under the second class of methods. Sliding mode observers (SMOs) , smooth variable structure filters (SVSFs) [87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103], and sliding innovation filters (SIFs) [104][105][106][107][108][109][110][111][112] are only a few examples. Some of the drawbacks of the first group are mitigated by SMO, SVSF, and SIF filters.…”

Section: Introductionmentioning

confidence: 99%

Estimating the complex maneuvering of a UGV using IMM-SIF

Obaideen

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

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.

“…This is accomplished using a correction gain derived from the Lyapunov stability theorem. Both filters have been demonstrated to be robust and stable in the literature [35][36][37][38][39]. To smooth out the noise, the filter employs a smoothing boundary layer.…”

Section: Introductionmentioning

confidence: 99%

FPGA to study the behavior of a maneuvering UGV using sliding innovation filter

Bonny

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

2023

Field programmable gate arrays (FPGAs) are increasingly popular due to their customizability, which enables them to be tailored to specific applications, resulting in minimal resource usage that saves energy and space. In this work, we used an FPGA with a Z-board from Xilinx to simulate the application of the sliding innovation filter (SIF) to a robotic arm. SIF is a predictor-corrector filter used for both linear and nonlinear systems to estimate states and/or parameters. It shares similar principles with sliding mode observer and smooth variable structure filter (SVSF) and uses a correction gain derived to satisfy Lyapunov stability, keeping the estimates near the measurements. We tested SIF on a manipulator with two joints (rotational and prismatic), using FPGA to run the simulation while tracking resource utilization. We compared the results with those of SVSF.