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Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV

作者： Alsadi, Naseem Hilal, Waleed Surucu, Onur Giuliano, Alessandro Gadsden, Stephen A. Yawney, John AlShabi, Mohammad McMaster Univ 1280 Main St W Hamilton ON L8S 4L8 Canada Adastra Corp 8500 Leslie St 600 Thornhill ON L3T 7M8 Canada Univ Sharjah Sharjah U Arab Emirates

ISBN: (纸本)9781510651036;9781510651029

Artificial feedforward neural networks (ANN) have been traditionally trained by backpropagation algorithms involving gradient descent algorithms. This is in order to optimize the network's weights and parameters in the training phase to minimize the out of sample error in the output during testing. However, gradient descent (GD) has been proven to be slow and computationally inefficient in comparison with studies implementing the extended Kalman filter (EKF) and unscented Kalman filter (UKF) as optimizers in ANNs. In this paper, a new method of training ANNs is proposed utilizing the sliding innovation filter (SIF). The SIF by Gadsden et al. has demonstrated to be a more robust predictor-corrector than the Kalman filters, especially in ill-conditioned situations or the presence of modelling uncertainties. In this paper, we propose implementing the SIF as an optimizer for training ANNs. The ANN proposed is trained with the SIF to predict the MackeyGlass Chaotic series, and results demonstrate that the proposed method results in improved computation time compared to current estimation strategies for training ANNs while achieving results comparable to a UKF-trained neural network.

关键词： Optimization Estimation Theory Kalman filter Gradient Descent smooth variable structure filter Sliding Modes State-Space Methods Deep Learning Machine Learning Neural Networks

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SLIDING MODE CONTROLLER AND filter APPLIED TO A PNEUMATIC MCKIBBEN MUSCLE ACTUATOR

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ASME International Mechanical Engineering Congress and Exposition

作者： Jouppila, V. Gadsden, S. A. Habibi, S. R. Bone, G. M. Ellman, A. Tampere Univ Technol Dept Mech & Design FIN-33101 Tampere Finland

ISBN: (纸本)9780791843833

In this paper, a robust and stable control strategy is applied to a Festo fluidic muscle actuator, with the objective of trajectory following control. A complete model of this system is not available which leads to unmodeled dynamics and uncertainties. Furthermore, full-state feedback is required for this type of control. However, in practice not all of the states are measurable or available due to cost or availability of instruments, thus a full-state observer is required. The smooth variable structure filter (SVSF) is a recently introduced robust predictor-corrector method used for state and parameter estimation, and has a form that is able to provide full-state information. In this regard, a new strategy that combines Sliding Mode Control (SMC) with the SVSF is used to control this system. The estimated states from the SVSF are used by the sliding mode controller to obtain a discontinuous control signal. This signal drives the plant to follow a desired state trajectory required by the pneumatic McKibben muscle actuator. Simulation results were generated based on a realistic desired trajectory. The results of the SMC-SVSF control strategy are compared with a tuned PID controller. The described control strategy is able to overcome the nonlinearities present in the system, has a fast response time, and is robust to modeling uncertainties and measurement noise.

关键词： McKibben muscle actuator sliding mode control smooth variable structure filter trajectory following

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Dual Estimation Strategy for New and Aged Electric Vehicles Batteries

Dual Estimation Strategy for New and Aged Electric Vehicles ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Rahimifard, Sara Habibi, Saeid Tjong, Jimi McMaster Univ Dept Mech Engn Hamilton ON Canada Ford Motor Co Windsor ON Canada

ISBN: (纸本)9781728146294

The use of hybrid (HEVs) and electric vehicles (EVs) has become more prevalent around the world in the last decade. These vehicles commonly use lithium-ion batteries due to their high energy density. They also utilize battery management systems (BMSs), which rely on continuous real-time monitoring and control, to ensure safe operation. The BMS acquires accurate State Of Charge (SOC) and State Of Health (SOH) estimates. For these, battery models that are identified and updated at various states of life from new to aged are required. In this paper, a dual strategy for both parameter and SOC estimation is proposed using a third-order equivalent circuit-based battery model (OCV-R-3RC). The strategy employs a square-root recursive least squares (SR-RLS) method to identify the parameters related to SOC. The smooth variable structure filter (SVSF) is then used as an estimation strategy for obtaining the battery SOC. The efficacy of the proposed algorithm is verified by applying it to experimental data from an extensive aging test.

关键词： Lithium-Ion Batteries equivalent circuit-based battery model parameter identification square-root recursive least square smooth variable structure filter state of charge

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Derivation of the sliding innovation information filter for target tracking 32

Derivation of the sliding innovation information filter for ...

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

作者： Alsadi, Naseem Hilal, Waleed Gadsden, S. Andrew Al-Shabi, Mohammad McMaster Univ 1280 Main St Hamilton ON L8S 4L8 Canada Univ Sharjah Univ City Rd Sharjah 27272 U Arab Emirates

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term 'information' is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.

关键词： Information filter Kalman filter sliding innovation filter smooth variable structure filter target tracking

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Robustness Comparison of Some State Estimation Methods with an Explicit Consideration of Modeling Uncertainties 28

Robustness Comparison of Some State Estimation Methods with ...

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IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Afshari, Hamed H. Al-Ani, Dhafar Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9781479958290

This paper presents a comparative analysis of well-known state estimation methods that are commonly used in real systems. The aim of this research is to measure and then evaluate the robustness (i. e., a measure of performance when a small and deliberate changes are made to the method conditions) of these methods against modeling uncertainties. The state estimation methods include the Kalman filter, the 1st-Order smooth variable structure filter (1st-order SVSF), and the new developed Dynamic 2nd-Order SVSF. A relatively new performance robustness criterion (so-called robustness index) is adopted in this work to first measure the robustness of the estimation methods, and then, evaluate their performance. The robustness index is calculated for each method when modeling uncertainties are explicitly considered. Simulation analysis is performed using the linear model of an Electro-Hydrostatic Actuator (EHA) setup under the normal and uncertain conditions. Simulation results showed the superior performance of the Dynamic 2nd-Order SVSF over other methods in terms of robustness against modeling uncertainties.

关键词： robust state estimation smooth variable structure filter dynamic sliding mode systems modeling uncertainties

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A Square-Root Formulation of the Sliding Innovation filter for Target Tracking 31

A Square-Root Formulation of the Sliding Innovation Filter f...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI

作者： Hilal, W. Gadsden, S. A. Wilkerson, S. A. Al-Shabi, M. McMaster Univ Hamilton ON L8S 4L8 Canada Univ Maryland Baltimore Cty UMBC Baltimore MD 21250 USA Univ Sharjah Sharjah U Arab Emirates

ISBN: (数字)9781510651210

ISBN: (纸本)9781510651210;9781510651203

The sliding innovation filter (SIF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SIF, a square-root formulation is derived. The square-root SIF is based on Potter's algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.

关键词： Square-root Kalman filter smooth variable structure filter target tracking

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Two-Pass smoother Based on the SVSF Estimation Strategy 24

Two-Pass Smoother Based on the SVSF Estimation Strategy

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV

作者： Gadsden, S. A. Al-Shabi, M. Kirubarajan, T. Univ Maryland Baltimore Cty Baltimore MD 21250 USA Philadelphia Univ Amman Jordan McMaster Univ Hamilton ON L8S 4L7 Canada

ISBN: (纸本)9781628415902

The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilizes a switching gain that brings an inherent amount of stability to the estimation process. In this paper, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the popular Kalman-based two-pass smoother.

关键词： Minimum-variance smoother Kalman filter smooth variable structure filter aerospace actuator

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Square-Root Formulation of the SVSF with Applications to Nonlinear Target Tracking Problems 24

Square-Root Formulation of the SVSF with Applications to Non...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV

作者： Gadsden, S. A. Al-Shabi, M. Kirubarajan, T. Univ Maryland Baltimore Cty Baltimore MD 21250 USA Philadelphia Univ Amman Jordan McMaster Univ Hamilton ON L8S 4L7 Canada

ISBN: (纸本)9781628415902

The smooth variable structure filter (SVSF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter's algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.

关键词： Square-root Kalman filter smooth variable structure filter target tracking

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A Robust Nonlinear filter for Doppler Radar Target Tracking

A Robust Nonlinear Filter for Doppler Radar Target Tracking

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Yaowen Li, Gang Liu, Yu Zhang, Xiao-Ping He, You Tsinghua University Department of Electronic Engineering Beijing China Research Institute of Information Fusion Naval Aviation University Yantai China Ryerson University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9781665498142

The smooth variable structure filter (SVSF) offers robust state estimation in the presence of model uncertainty but requires a linear measurement model and a full-rank measurement matrix. Doppler radar reports target position and range rate in the polar or spherical coordinates without tangential velocity, leading to a nonlinear and underdetermined measurement model in Cartesian coordinates and thus precluding the use of the standard SVSF. In this paper, a novel robust estimator, referred to as the sequential smooth variable structure filter (SSVSF), is proposed for Doppler radar target tracking. The Doppler radar measurement vector is partitioned into two parts, i.e., the position (containing range, azimuth, and elevation) and the range rate, and is used for a two-stage sequential processor. In the first stage, the target position measurement is transformed into Cartesian coordinates via the unbiased converted measurement (UCM) strategy, so that a generalized SVSF is implemented for robust target state estimation with a linear measurement model. In the second stage, a pseudo measurement is constructed from the product of range and range rate, and accordingly, an approximate linear minimum mean square error (LMMSE) estimator is developed to update the first-stage estimation. Simulation results validate the effectiveness of the proposed algorithm in Doppler radar tracking, and superior robustness to the conventional nonlinear Bayesian filter in the case of target motion uncertainty. © 2021 IEEE.

关键词： Doppler radar tracking nonlinear estimation robust estimation smooth variable structure filter

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Nonlinear Estimation Techniques Applied on Target Tracking Problems

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2012年第5期134卷 54501-54501页

作者： Gadsden, Andrew Habibi, Saeid Dunne, Darcy Kirubarajan, T. McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L7 Canada

This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observations. These two target tracking problems provide a good benchmark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its stability and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters performances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accuracy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlinear target tracking problems, which in the past have typically been solved by Kalman or particle filters. [DOI: 10.1115/1.4006374]

关键词： estimation target tracking Kalman filter particle filter smooth variable structure filter sliding mode estimation

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