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43rd Chinese Control Conference (CCC)

作者： Qiu, Zhilin Xing, Yashan Na, Jing Gao, Guanbin Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Intelligent Control & Applicat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Parameter estimation of Polymer Electrolyte Membrane Fuel Cell (PEMFC) is important for its modeling and controller design. However, the nonlinear parameterization property of PEMFC makes it difficult to obtain the precise value of the parameter embedded in the nonlinear function. In this paper, we propose an adaptive nonlinear parameter estimation algorithm to solve the nonlinear problem of PEMFC. To separate the parameter embedded in the nonlinear function, a linear parameterization method based on direct differential and filter design is first applied to the PEMFC model. In this way, the nonlinear function is transformed into a linearly parameterized function with an exponentially decaying remainder that can guarantee the unknown parameters' convergence. Because the direct differential operation introduces the derivation signal of the system input in the linearly parameterized function, the prescribed-time robust differentiator is used to achieve its fast convergence. Later, the adaptive parameter estimation algorithm driven by the parameter estimation error is applied. Because there is no need to construct any observer/predictor, the calculation efficiency is enhanced. Finally, the comparative experiment of the differentiator and the model validation experiment are conducted. The experiment results show that the prescribed-time robust differentiator has a fast convergence rate and the parameter estimation algorithm achieves the nonlinear parameter estimation with a high accuracy.

关键词： Adaptive Parameter Estimation Polymer Electrolyte Membrane Fuel Cell nonlinear parameterization

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Robust I&I adaptive tracking control of systems with nonlinear parameterization: An ISS perspective

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AUTOMATICA 2023年 158卷

作者： Wang, Lei Kellett, Christopher M. Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Australian Natl Univ Sch Engn Canberra Australia

This paper studies the immersion and invariance (I&I) adaptive tracking problem for a class of nonlinear systems with nonlinear parameterization in the ISS framework. Under some mild assumptions, a novel I&I adaptive control algorithm is proposed, leading to an interconnection of an ISS estimation error subsystem and an ISS tracking error subsystem. Using an ISS small-gain condition, the desired uniform global asymptotic stability of the resulting interconnected "error" system can be achieved and a sumtype strict Lyapunov function can be explicitly constructed. Taking advantage of this ISS-based design framework, it is shown that the corresponding robustness with respect to the input perturbation can be rendered ISS. To remove the need to solve the immersion manifold shaping PDE, a new filter-based approach is proposed, which preserves the ISS-based design framework. Finally, we demonstrate the validness of the proposed framework on a tracking problem for series elastic actuators.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： nonlinear parameterization Immersion and invariance Adaptive control Input-to-state stability

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Adaptive Parameter Estimation for a Polymer Electrolyte Membrane Fuel Cell with nonlinear parameterization

Adaptive Parameter Estimation for a Polymer Electrolyte Memb...

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第43届中国控制会议

作者： Zhilin Qiu Yashan Xing Jing Na Guanbin Gao Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Parameter estimation of Polymer Electrolyte Membrane Fuel Cell(PEMFC) is important for its modeling and controller design. However, the nonlinear parameterization property of PEMFC makes it difficult to obtain the precise value of the parameter embedded in the nonlinear function. In this paper, we propose an adaptive nonlinear parameter estimation algorithm to solve the nonlinear problem of PEMFC. To separate the parameter embedded in the nonlinear function, a linear parameterization method based on direct differential and filter design is first applied to the PEMFC model. In this way, the nonlinear function is transformed into a linearly parameterized function with an exponentially decaying remainder that can guarantee the unknown parameters' convergence. Because the direct differential operation introduces the derivation signal of the system input in the linearly parameterized function, the prescribed-time robust differentiator is used to achieve its fast convergence. Later, the adaptive parameter estimation algorithm driven by the parameter estimation error is applied. Because there is no need to construct any observer/predictor, the calculation efficiency is enhanced. Finally, the comparative experiment of the differentiator and the model validation experiment are conducted. The experiment results show that the prescribed-time robust differentiator has a fast convergence rate and the parameter estimation algorithm achieves the nonlinear parameter estimation with a high accuracy.

关键词： Adaptive Parameter Estimation Polymer Electrolyte Membrane Fuel Cell nonlinear parameterization

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Adaptive output feedback control of stochastic nonholonomic systems with nonlinear parameterization

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AUTOMATICA 2018年 98卷 247-255页

作者： Wang, Hui Zhu, Quanxin Hunan Normal Univ Coll Math & Stat Key Lab HPC SIP MOE Changsha 410081 Hunan Peoples R China Nanjing Normal Univ Sch Math Sci Nanjing 210023 Jiangsu Peoples R China Nanjing Normal Univ Inst Finance & Stat Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Math & Stat Nanjing 210044 Jiangsu Peoples R China

The problem of adaptive output-feedback control of nonlinearly parameterized stochastic nonholonomic systems is studied in this paper. Since many unknowns (e.g., unknown control coefficients and unknown nonlinear parameters in systems' nonlinearities) occur into systems, we utilize an adaptive control method, together with a parameter separation technique, to construct an adaptive output feedback controller to regulate the whole systems. During the design procedure, a new form of reduced-order K-filters is given to compensate the unmeasured states of the systems. A switching strategy is proposed explicitly to stabilize the entire systems in the control scheme. Finally, a bilinear model with stochastic disturbances is presented to demonstrate our theoretical results. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Stochastic nonholonomic system Globally asymptotic stability nonlinear parameterization Adaptive control Output-feedback

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Adaptive Control of Pure-feedback Systems with nonlinear parameterization via Time-scale Separation

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2017年第1期15卷 196-204页

作者： Asadi, Mehrnoosh Shandiz, Heydar Toossian Shahrood Univ Technol Sch Elect & Robot Engn Shahrood Iran

In this paper an adaptive control approach for completely non-affine pure-feedback systems with nonlinear parameterization is proposed. By using the parameter separation technique and coupling it effectively with combination of backstepping and time scale separation, a fast dynamical equation is derived from the original subsystem, where the solution is sought to approximate the corresponding ideal virtual/actual control input. In this approach, since designing state predictor to derive adaptation law of unknown parameters is omitted, our design is more accurate and less complex. The closed loop stability and the state regulation of nonlinear parameterization pure-feedback systems are all proved by new theorem in singular perturbation theory. Finally the simulation results are provided to demonstrate the effectiveness of the proposed approach.

关键词： Non-affine nonlinear parameterization pure-feedback systems parameter separation time scale separation

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Adaptive state-feedback stabilization of stochastic high-order nonholonomic systems with nonlinear parameterization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2015年第4期37卷 536-549页

作者： Du Qinghui Wang Chaoli Wang Gang Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China

This paper investigates the adaptive stabilization of stochastic high-order nonholonomic systems with nonlinear parameterization. An adaptive state feedback controller is designed by using the parameter separation technique and input-state scaling technique, and adding a power integrator backstepping approach. The switching strategy is proposed to eliminate the phenomenon of uncontrollability and to guarantees that the closed-loop system has an almost surely unique solution for any initial state, the equilibrium of interest is globally stable in probability, and the state can almost surely be regulated to the origin. Simulation examples demonstrate the effectiveness of the proposed scheme.

关键词： Adaptive state-feedback stabilization adding a power integrator input-state-scaling nonlinear parameterization stochastic high-order nonholonomic systems

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nonlinear Schur-Type Audio Signal parameterization for Convolutional Networks

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1665-1669页

作者： Biernacki, Pawel Libal, Urszula Wroclaw Univ Sci & Technol Dept Acoust Multimedia & Signal Proc PL-50370 Wroclaw Poland

This article introduces a novel signal parameterization approach, termed nonlinear Schur-type signal parameterization, designed to enhance machine learning tasks such as signal classification and recognition. Traditional linear parameterization methods often struggle with the complex, nonlinear nature of real-world data. The mathematical foundation of the proposed parameterization method is extraction of Schur coefficients. The presented method is scalable and can be adjusted to the signal nature. The nonlinear Schur parameterization produces a matrix of Schur coefficients in time, dedicated to be a 2D input of convolutional neural networks (CNN). The performed experiments for the audio signals from open access datasets show that the signal representation in the form of the Schur coefficients is very efficient for recognition performance. The results obtained by CNN show an improvement in the classification accuracy in comparison with solutions based on preprocessing in frequency domain as FFT or MFCC.

关键词： Vectors Decorrelation Convolutional neural networks Signal processing algorithms Feature extraction Rotors Reflection coefficient Accuracy Training Prediction algorithms nonlinear parameterization reflection coefficients

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Adaptive FTPP control of switched stochastic nonlinearly parameterized systems with asymptotic tracking performance

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APPLIED MATHEMATICS AND COMPUTATION 2025年 495卷

作者： Liu, Yanli Sun, Yihua Hao, Li-Ying Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Liaoning Peoples R China

The article discusses the asymptotic tracking control for switched stochastic systems with nonlinear parameterization. Firstly, to achieve the finite-time control, a finite-time prescribed performance function (FTPPF) is introduced into the regular control frame which refrains from the fractional-order controller design. Secondly, an error transformation is employed which renders the transition from primordial output tracking error to a new error associated with the FTPPF for achieving objective. Thirdly, an improved filter is introduced to deal with the sharp increase in computation on account of the repeated differentiation of the virtual controllers. By using the Barbalat's Lemma, the designed control strategy can insure the output tracks the reference signal asymptotically, and the performance function boundaries are satisfied within a finite time under the designed control strategy. Finally, simulations are given to demonstrate the feasibility of the constructed strategy.

关键词： Switched stochastic nonlinear systems Finite-time prescribed control Asymptotic tracking nonlinear parameterization Improved dynamic surface control

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Finite-Time Adaptive Neural Prescribed Performance Control for High-Order nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization

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IEEE ACCESS 2024年 12卷 4618-4630页

作者： Zhang, Jiao-Jun Zhou, Yong-Hua Sun, Qi-Ming Zhejiang Sci Tech Univ Dept Math Sci Hangzhou 310018 Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

This study focuses on the adaptive prescribed-time neural control for a class of high-order switched systems with nonlinear parameterization in presence of unmodeled dynamics and quantized input. Different from the existing results on finite-time control on basis of adding a power integrator technique, the controller construction and stability analysis are simplified, and the tracking error remains within a set range over any prescribed time. Under the frame of backstepping design, a state feedback controller is designed. During the controller design procedure, Radial basis function (RBF) neural networks with minimal learning parameters are employed to identify the unknown compounded nonlinear functions, and the control input is quantized. Based on Lyapunov stability theory, the closed-loop system's signals are all assured to be semi-globally uniformly bounded (SGUB), and the tracking error is kept inside a prescribed zone at a finite time. Finally, a numerical simulation is provided to demonstrate the viability and efficacy of the control strategy.

关键词： High-order nonlinear systems nonlinear parameterization switched systems prescribed-time control unmodeled dynamics input quantization

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Partial-state feedback adaptive stabilization for a class of uncertain nonholonomic systems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2024年第7期38卷 2532-2553页

作者： Yu, Jiangbo Liu, Yungang Li, Chengdong Wu, Yuqiang Shandong Jianzhu Univ Sch Sci Jinan 250101 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Qufu Normal Univ Inst Automat Qufu Peoples R China

In this paper, we investigate the global adaptive stabilization problem via partial-state feedback for a class of uncertain chained-form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input-to-state stability (ISS) and ISS-Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input-to-state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm.

关键词： adaptive stabilization dynamic uncertainty input-to-state stable nonholonomic systems nonlinear parameterization

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