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ROBUST NONLINEAR CONTROL AND ESTIMATION OF A PRRR ROBOT SYSTEM

INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2019年第6期34卷 632-644页

作者： Al-Shabi, Mohammad Hatamleh, Khaled S. Gadsden, Stephen A. Soudan, Bassel Elnady, A. Univ Sharjah Coll Engn Dept Mech & Nucl Engn Sharjah 27272 U Arab Emirates Jordan Univ Sci & Technol Dept Mech Engn Irbid 22110 Jordan Univ Guelph Coll Engn & Phys Sci Dept Mech Engn Guelph ON N1G 2W1 Canada Univ Sharjah Dept Elect & Comp Engn Sharjah U Arab Emirates

In this paper, a newly proposed implementation of an unscented Kalman (UK)-based smooth variable structure filter (UK-SVSF) is introduced. The method is combined with a sliding mode controller (SMC) to compensate for modelling uncertainties. The robustness and tracking accuracy of the proposed controller and estimation strategy are demonstrated on a four-degree-of-freedom robotic system with one prismatic and three rotary joints. The effectiveness of the proposed combination is proven through comparisons with three different non-linear estimation algorithms: the standard unscented Kalman filter, SVSF, and a previously published UK-SVSF. The robot's trajectory following accuracy and efficiency are used as the performance parameters to study and compare the different strategies. Modelling uncertainties are added to the system to provide a more thorough evaluation of the robustness of the different non-linear control and estimation strategies.

关键词： Sliding mode controller estimation control unscented Kalman filter smooth variable structure filter

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A hybrid observer for SOC estimation of lithium-ion battery based on a coupled electrochemical-thermal model

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2019年第15期16卷 1527-1538页

作者： Xu, Xing Lin, Yuzhen Wang, Feng Yang, Shichun Zhou, Zhiguang Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang Jiangsu Peoples R China Beihang Univ Transportat Sci & Engn Beijing Peoples R China Chery Automobile Co New Energy Dev Dept Wuhu Anhui Peoples R China

An accurate prediction of the state of charge (SOC) of lithium-ion battery provides crucial information for the Battery Management System (BMS). In this paper, a simplified uneven electrochemical model coupled to a bulk thermal model of Lithium-ion battery is built up. The parameters of battery model identified in previous research can provide a more accurate description of the battery dynamic characteristics. This implementation of the coupled model is then applied to amalgamation with the extend Kalman filter combined with smoothing variable structure filter (EK-SVSF) to obtain an accurate and robust SOC result. MATLAB/Simulink and experiments, including 1C pulse discharge and NEDC cycle tests are adopted to evaluate the performance of the proposed hybrid algorithm. The simulation results indicate that the SOC error is less than 2%, therefore the algorithm is suitable for the new energy vehicle power BMS.

关键词： A coupled electrochemical-thermal model a hybrid observer extended Kalman filter lithium-ion battery smooth variable structure filter state of charge estimation

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THE smooth PARTICLE variable structure filter

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2012年第2期36卷 177-193页

作者： Gadsden, S. Andrew Habibi, Saeid R. Kirubarajan, Thia McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method, referred to as the smooth particle variable structure filter (SPVSF), utilizes the estimates and state error covariance of the SVSF to formulate the proposal distribution which generates the particles used by the PF. The SPVSF method is applied on two computer experiments, namely a nonlinear target tracking scenario and estimation of electrohydrostatic actuator parameters. The results are compared with other popular Kalman-based estimation methods.

关键词： particle filter smooth variable structure filter Kalman filter tracking nonlinear estimation

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Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

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IEEE ACCESS 2020年 8卷 223495-223508页

作者： Ren, Xiaolin Li, Hongwen Li, Yuanchun Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

The challenge in addressing uncalibrated visual servoing (VS) control of robot manipulators with unstructured environments is to obtain appropriate interaction matrix and keep the image features in the field of view (FOV), especially when the non-Gaussian noise disturbance exists in the VS process. In this article, a hybrid control algorithm which combines bidirectional extreme learning machine (B-ELM) with smooth variable structure filter (SVSF) is proposed to estimate interaction matrix and tackle visibility constraints. For VS, the nonlinear mapping between image features and interaction matrix is approximated using the B-ELM learning. To increase the capability of anti-interference, the SVSF is employed to re-estimate interaction matrix. A constraint function presenting feature coordinates and region boundaries is given and added to the velocity controller, which drags image features away from the restricted region and ensures the smoothness of the velocities. Since the camera and robot model parameters are not required in developing the control strategy, the servoing task can be fulfilled flexibly and simply. Simulation and experimental results on a conventional 6-degree-of-freedom manipulator verify the effectiveness of the proposed method.

关键词： Jacobian matrices Robots Cameras Robot kinematics Visualization Visual servoing Calibration Bidirectional extreme learning machine smooth variable structure filter constraint function restricted region visual servoing

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A Novel smooth variable structure smoother for Robust Estimation

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SENSORS 2020年第6期20卷 1781-1781页

作者： Chen, Yu Xu, Luping Yan, Bo Li, Cong Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Univ Bologna Dept Elect Elect & Informat Engn I-47521 Cesena FC Italy Acad Space Elect Informat Technol Xian 710100 Peoples R China

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

关键词： robust estimation smooth variable structure filter Kalman smoother target tracking uncertain system

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A Multiple Model Adaptive SVSF-KF Estimation Strategy 28

A Multiple Model Adaptive SVSF-KF Estimation Strategy

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

作者： Goodman, Jacob M. Wilkerson, Stephen A. Eggleton, Charles Gadsden, S. Andrew Univ Maryland Baltimore Cty Autonomous Syst Res Lab 1000 Hilltop Circle Baltimore MD 21250 USA York Coll Penn Dept Mech Engn 441 Country Club Rd York PA 17403 USA Univ Guelph Intelligent Control & Estimat Lab 50 Stone Rd East Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781510627024

State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The smooth variable structure filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF - KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.

关键词： smooth variable structure filter SVSF-KF Kalman filter MMAE fault detection robust estimation

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Offline Parameter Identification and SOC Estimation for New and Aged Electric Vehicles Batteries

Offline Parameter Identification and SOC Estimation for New ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ahmed, Ryan Rahimifard, Sara Habibi, Saied McMaster Univ Dept Mech Engn Hamilton ON Canada

ISBN: (纸本)9781538693100

The hybrid ( HEVs) and battery electric vehicles (BEVs) represent a sustainable alternative in compare to conventional, fossil fuel-based vehicles. Battery pack is a major and the most expensive part of electric vehicles. It requires accuracy, real-time monitoring, and control. Parameters such as state of charge (SOC) and state of health (SOH) have to monitor accurately to guarantee battery safety and reliability and avoid overcharge or under-discharge conditions. These conditions can lead to irreversible capacity degradation and power fade. An accurate battery model with robust estimation method is needed for battery condition monitoring. In this paper, a way to estimate battery parameters is proposed for the first order equivalent circuit battery model (OCV-R-RC) using genetic algorithm (GA) optimization at various ages of the battery to track the changes. The smooth variable structure filter (SVSF) strategy has been considered to estimate the state of charge based on the optimized model. An aging test has been conducted over a period of 12 months using real-world driving scenarios. Experimental results are provided to show the efficacy of the proposed approach.

关键词： Lithium-Ion Batteries genetic algorithm optimization equivalent circuit-based battery model parameter identification smooth variable structure filter state of charge

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Estimation theory and Neural Networks revisited: REKF and RSVSF as optimization techniques for Deep-Learning

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NEURAL NETWORKS 2018年 108卷 509-526页

作者： Ismail, Mahmoud Attari, Mina Habibi, Saeid Ziada, Samir McMaster Univ Dept Mech Engn Hamilton ON Canada

Deep-Learning has become a leading strategy for artificial intelligence and is being applied in many fields due to its excellent performance that has surpassed human cognitive abilities in a number of classification and control problems (Ciregan, Meier, & Schmidhuber, 2012;Mnih et al., 2015). However, the training process of Deep-Learning is usually slow and requires high-performance computing, capable of handling large datasets. The optimization of the training method can improve the learning rate of the Deep-Learning networks and result in a higher performance while using the same number of training epochs (cycles). This paper considers the use of estimation theory for training of large neural networks and in particular Deep-Learning networks. Two estimation strategies namely the Extended Kalman filter (EKF) and the smooth variable structure filter (SVSF) have been revised (subsequently referred to as RSVSF and REKF) and used for network training. They are applied to several benchmark datasets and comparatively evaluated. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Kalman filter smooth variable structure filter Deep-Learning Neural Networks REKF RSVSF

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A novel robust filtering strategy for systems with Non-Gaussian noises

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2018年 97卷 154-164页

作者： Zhou, Hongping Xu, Lin Chen, Wenliang Guo, Kai Shen, Fei Guo, Zhongyi Hefei Univ Technol Sch Comp & Informat Tunxi Rd 193 Hefei Anhui Peoples R China

For estimating the states of moving targets in the nonlinear system with non-Gaussian noise, the combination of Gaussian Sum filter (GSF) and other nonlinear filters has been chosen as the filtering algorithm conventionally. The smooth variable structure filter (SVSF) is a new predictor-corrector method used for state and parameter estimation, which has good stability and robustness. In this paper we propose a new strategy called the modified GS-EKF-SVSF, which inherits good robustness of Gaussian Sum and smooth variable structure filter (GS-SVSF) and high accuracy of Gaussian Sum and Extended Kalman filter (GS-EKF). A nonlinear system with non-Gaussian noise for target tracking is used to test the proposed new strategy. The simulation results demonstrate that our proposed strategy has higher accuracy and better robustness when there are modelling uncertainties existing in the system. (C) 2018 Elsevier GmbH. All rights reserved.

关键词： Gaussian sum filter Non-Gaussian noise smooth variable structure filter Target tracking

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Adaptive state estimation for tracking of civilian aircraft

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第6期12卷 777-784页

作者： Patra, Nilanjan Sadhu, Smita Ghoshal, Tapan Kumar Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Tracking of manoeuvring subsonic aerospace vehicles has traditionally been handled by state estimators. Ordinary state estimators perform poorly as the concerned process model can only be defined imprecisely. This contribution evaluates and compares the performance of adaptive single mode non-linear estimators against several versions of other estimators. The primary comparison is with the recently introduced smooth variable structure filter (SVSF) which is claimed to inherit the robustness of variable structure approach. Both the above types of estimators are then benchmarked with a well-known version of interacting multiple model (IMM) estimator which treats the manoeuvring aircraft as a hybrid system consisting of multiple modes. Monte Carlo simulation has been used and several descriptors have been used for comparison. The comparison demonstrates that a version of non-linear adaptive estimators incorporating sigma points and a single constant turn model performs substantially better than the SVSF and its tracking performance approaches that obtainable by the IMM estimator. Novelty of this contribution lies in providing a detailed comparison of the above three families of estimators, which provides adequate insight for selecting tracking estimators by trading off estimation accuracy, algorithm complexity, tuning requirement, and computational load.

关键词： Monte Carlo methods nonlinear estimation aircraft adaptive estimation state estimation nonlinear adaptive state estimation manoeuvring subsonic aerospace vehicle tracking civilian aircraft tracking adaptive single mode nonlinear estimator smooth variable structure filter SVSF interacting multiple model estimator IMM estimator Monte Carlo simulation

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