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A Duality-Based Approach to Inverse Kalman Filtering

IFAC-PapersOnLine 2023年第2期56卷 10258-10263页

作者： Yibei Li Bo Wahlberg Lihua Xie Xiaoming Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Optimization and Systems Theory Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

关键词： Inverse filtering Kalman filters duality covariance estimation identifiability

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Frequency Domain Identification of Multirate systems: A Lifted Local Polynomial Modeling Approach

Frequency Domain Identification of Multirate Systems: A Lift...

引用

IEEE Conference on Decision and control

作者： Max van Haren Lennart Blanken Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Eindhoven The Netherlands Sioux Technologies Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Frequency-domain representations of multirate systems are essential for controller design and performance evaluation of multirate systems and sampled-data control. The aim of this paper is to develop a time-efficient closed-loop identification approach for multirate systems in the frequency-domain. The developed method utilizes local polynomial modeling for lifted representations of LPTV systems, which enables direct identification of closed-loop multirate systems in a single identification experiment. Unlike LTI identification techniques, the developed method does not suffer from bias due to ignored LPTV dynamics. The developed approach is demonstrated on a multirate example, resulting in accurate and fast identification in the frequency domain.

关键词： Performance evaluation Linear systems Time-frequency analysis Transfer functions Estimation Benchmark testing control systems

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Frequency Domain Identification of Multirate systems: A Lifted Local Polynomial Modeling Approach

arXiv

引用

arXiv 2022年

作者： van Haren, Max Blanken, Lennart Oomen, Tom The Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Sioux Technologies Eindhoven Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

关键词： Frequency domain analysis

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Adaptive PI-Fuzzy Like control of a Stack Pneumatic Actuators Testbed for Multi-Configuration Small Scale Soft Robotics 6

Adaptive PI-Fuzzy Like Control of a Stack Pneumatic Actuator...

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6th International Conference on Manipulation, Automation, and Robotics at Small Scales, MARSS 2023

作者： Al Mhdawi, Ammar K. Wright, Nick Humaidi, Amjad J. Azar, Ahmad Taher Newcastle University Department of Electrical and Electronic Engineering Newcastle United Kingdom Edge Hill University United Kingdom Prince Sultan University Asscl Lab Saudi Arabia University of Technology Control and Systems Engineering Department Baghdad Iraq College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Egypt

ISBN: (纸本)9798350330397

It has become evident that pneumatic muscle-based robots are the most compliant of all soft and semi-soft robotic testbeds under investigation, despite the fact that the artificial muscle-based robot is the most challenging due to its hyperelastic behavior resulting from the constitutive elastic material. The purpose of this paper is to examine the development of a physical pneumatic actuation testbed and the application of control strategies for precision small scale soft robotics control. Moreover, an adaptive PI-Fuzzy like control is proposed for reference signal tracking. It is imperative to note that in this application, the controller's performance is determined by its ability to track a reference signal as well as its ability to minimize the absolute tracking error. The Fuzzy Logic controller (FLC) controller, including the nature of the membership functions and the rule base, is described in detail. Simulations and hardware testing have revealed that the adaptive PI-Fuzzy like controller outperforms the traditional FLC controller in terms of experimental results. Additionally, the actuation speed is controlled under different loading conditions. It is considered that the proposed testbed is capable of controlling and managing multi-configurations of medium to small scale soft-based robots efficiently and effectively. © 2023 IEEE.

关键词： controllers

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Multi-objective Optimization of A CCHP System Model Integrated with P2G for Decarbonization 6

Multi-objective Optimization of A CCHP System Model Integrat...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Wei, Pengyu Bamisile, Olusola Cai, Dongsheng Li, Jian Adun, Humphrey Huang, Qi Chengdu University of Technology School of Nuclear Technology and Automation Engineering Chengdu China University of Electronic Science and Technology of China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control Chengdu China Cyprus International University KKTC Energy Systems Engineering Department Mersin10 Turkey

ISBN: (纸本)9798350347159

To realize the dual carbon policy, the goal of energy saving, and emission reduction, it is important to develop ways to better promote the low carbon economic operation of energy systems. Therefore, this research proposes an optimal operation strategy considering the electric-gas-cooling-heat demand response. Considering the demand response of each participant, an integrated energy system model with photovoltaic, wind energy, and traditional grid as energy supply. Also, electric energy, natural gas, cooling, and heating subsystems with co-production are established. With minimizing the overall system operation cost and optimizing environmental protection, the improved particle swarm optimization algorithm is used to solve the multi-constrained objective optimization problem. Finally, the optimized model is tested. The results from this study show that the energy system under cogeneration can find a balance between economy and environmental friendliness, and provide a reference for the subsequent energy supply configuration. © 2022 IEEE.

关键词： Environmental protection

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Converse negative imaginary theorems

arXiv

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arXiv 2023年

作者： Khong, Sei Zhen Zhao, Di Lanzon, Alexander Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China Control Systems Centre Department of Electrical and Electronic Engineering School of Engineering University of Manchester ManchesterM13 9PL United Kingdom

Converse negative imaginary theorems for linear time-invariant systems are derived. In particular, we provide necessary and sufficient conditions for a feedback system to be robustly stable against various types of negative imaginary (NI) uncertainty. Uncertainty classes of marginally stable NI systems and stable strictly NI systems with restrictions on their static or instantaneous gains are considered. It is shown that robust stability against the former class entails the strictly NI property, whereas the latter class entails the NI property. We also establish a non-existence result that no stable system can robustly stabilise all marginally stable NI uncertainty, thereby showing that the uncertainty class of NI systems is too large as far as robust feedback stability is concerned, thus justifying the consideration of subclasses of NI systems with constrained static or instantaneous gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear time-invariant system

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An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification*

引用

IFAC-PapersOnLine 2023年第2期56卷 4204-4209页

作者： Rodrigo A. González Angel L. Cedeño María Coronel Juan C. Agüero Cristian R. Rojas Department of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Electronic Engineering Department Universidad Técnica Federico Santa María Valparaíso Chile Advanced Center for Electrical and Electronic Engineering AC3E Valparaíso Chile Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the output data is exploited in this work to derive an expectation-maximization (EM) algorithm for parameter estimation of the state-space and noise covariance matrices. For this purpose, we use the incremental discrete-time equivalent of the system, which leads to EM iterations of the continuous-time state-space matrices that can be computed by standard filtering and smoothing procedures. The effectiveness of the identification method is tested via Monte Carlo simulations.

关键词： System identification continuous-time systems event-based sampling expectation-maximization

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Overrun Thruster control with Disturbance Rejection Under Model Uncertainties Using Mrac Adaptive control and Enhanced Sdn Integration

SSRN

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SSRN 2024年

作者： Al Mhdawi, Ammar K. Al-Raweshidy, Hamed Humaidi, Amjad J. The School of Engineering and Sustainable Development De Montfort University United Kingdom Electronic and Electrical Engineering Brunel University LondonUB83PH United Kingdom Department of Control and Systems Engineering Univeristy of Technology 10066 Iraq

AUVs play a crucial role in advancing our under- standing of the oceans, as they can access remote and challenging underwater environments that are difficult or dangerous for humans to reach. In general, the typical data centre power consumption can be between (10MW-40MW) and the associated contractual obliga- tions that are related to the acquisition of DC operational space facility can be at least 100,000 of square feet minimum. Therefore, in this paper, we propose the Ecological Navigation (EcoNAV) Autonomous Underwater Vehicle (AUV) robotic platform that is composed of interconnected cluster with Software-Defined Networking capabilities to be used a a mobile data center system. Moreover, AUVs are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a control system to maintain AUV stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear adaptive control of the over-actuated thrusters under the effects of model uncertainties are presented. We proposed a Model Reference Adaptive control(MRAC) for thruster overrun problem in the AUV that is a concurrent issue in AUVs path tracking and control. We have implemented MRAC with two adjustment mechanisms that are Lyapunov and Massachusetts Institute of technology(MIT) rule The control strategy proven to be efficient disturbance rejection and maintain stability to thrusters of the AUV. Lyapunov showed 34.5% time of disturbance rejection while MIT showed higher response time to disturbances of 82%. © 2024, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

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Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise

arXiv

引用

arXiv 2024年

作者： Liao, Jing-Xiao He, Chao Li, Jipu Sun, Jinwei Zhang, Shiping Zhang, Xiaoge Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China

Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's desirable feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing classifiers? This issue arises because the traditional BD method is solely designed for feature extraction with its own optimizer and objective function. When BD is combined with downstream deep learning classifiers, the different learning objectives will be in conflict. To address this problem, this paper introduces classifier-guided BD (ClassBD) for joint learning of BD-based feature extraction and deep learning-based fault classification. Firstly, we present a time and frequency neural BD that employs neural networks to implement conventional BD, thereby facilitating the seamless integration of BD and the deep learning classifier for co-optimization of model parameters. Specifically, the neural BD incorporates two filters: i) a time domain quadratic filter to utilize quadratic convolutional networks for extracting periodic impulses;ii) a frequency domain linear filter composed of a fully-connected neural network to amplify discrete frequency components. Subsequently, we develop a unified framework to use a deep learning classifier to guide the learning of BD filters. In addition, we devise a physics-informed loss function composed of kurtosis, l2∕l4 norm, and a cross-entropy loss to jointly optimize the BD filters and deep learning classifier. Consequently, the fault labels provide useful information to direct BD to extract features that distinguish classes amidst strong noise. To the best of our knowledge, this is the first of its kind that BD is successfully applied to bearing fault diagnosis. Experimental results from three datasets demonstrate that ClassBD outperforms other state-of-the-art methods under noisy conditions

关键词： Feature extraction

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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