Robust Nonlinear Control and Estimation of a PRRR Robot System

Al-Shabi, Mohammad; Hatamleh, Khaled S.; Gadsden, S. Andrew; Soudan, Bassel; Elnady, A.

doi:10.2316/j.2019.206-0160

Cited by 31 publications

(18 citation statements)

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“…The sigma points and their corresponding weights are selected based on the following rules. The UK-SVSF is one of popular methods in the combination strategies and has been applied in many different systems [20,21,43,44]. For better understanding of the combination strategy, the UK-SVSF is chosen and its specific process is summarized as follows.…”

Section: Updating By Svsf Predictormentioning

confidence: 99%

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Chen

Wang

et al. 2021

Remote Sensing

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As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

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Section: Updating By Svsf Predictormentioning

confidence: 99%

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Chen

Wang

et al. 2021

Remote Sensing

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“…(1) Overall design The following robot system is mainly composed of liDAR sensor, motor control system and robot control system [12]. As shown in Figure 1, under the ROS operating system, when the liDAR sensor detects the following target, the distance information of the liDAR is transmitted to the host computer, and the host computer calculates the position and posture of the next robot through the algorithm of target recognition and motion control.…”

Section: Robot Platform Constructionmentioning

confidence: 99%

Implementation of Intelligent Robot Following System Considering Finite Difference Method

2020

KME

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In recent years, with the improvement of computer vision technology, intelligent following robots have also been rapidly developed, greatly improving people's lives. This paper mainly studies the design and implementation of intelligent robot following system considering finite difference method. In this paper, the liDAR sensor, servo motor and control board of the tracking robot system are debugged under the ROS system, and the software and hardware platform of the tracking robot system are built and tested with the modular engineering thinking. The object detection of the following robot system is studied with finite difference method. Through field experiments in complex indoor environment and outdoor corridor, the real-time performance and effectiveness of the robot target following system are verified.

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“…Filters play a crucial role in a wide range of estimation applications [1][2][3][4][5][6][7][8][9] by extracting meaningful information from signals and minimizing the impact of uncertainties, disruptions, and noise. The main objective of filters is to enhance the overall dynamics performance of the system [10][11][12][13][14][15][16][17][18][19][20] by improving the system controller. However, there are various challenges to achieving optimal performance due to the presence of several obstacles, such as limited measured signals, non-measured or hidden states, and disturbances and noise.…”

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter to estimate power converters of electric vehicles

Al-Shabi

Gadsden

Obaideen

2023

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

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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.

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Robust Nonlinear Control and Estimation of a PRRR Robot System

Cited by 31 publications

References 0 publications

An Improved Smooth Variable Structure Filter for Robust Target Tracking

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Implementation of Intelligent Robot Following System Considering Finite Difference Method

Sliding innovation filter to estimate power converters of electric vehicles

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