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nonlinear Data-Driven Control via State-Dependent Representations 60

Nonlinear Data-Driven Control via State-Dependent Representa...

引用

60th IEEE conference on Decision and Control (CDC)

作者： Dai, T. Sznaier, M. Northeastern Univ ECE Dept Boston MA 02115 USA

ISBN: (纸本)9781665436595

Recently, there has been renewed interest in data-driven control, that is, the design of controllers directly from observed data. In the case of linear time-invariant (LTI) systems, several approaches have been proposed that lead to tractable optimization problems. On the other hand, the case of nonlinear dynamics is considerably less developed, with existing approaches limited to at most rational dynamics and requiring the solution to a computationally expensive Sum of Squares (SoS) optimization. Since SoS problems typically scale combinatorially with the size of the problem, these approaches are limited to relatively low order systems. In this paper, we propose an alternative, based on the use of state-dependent representations. This idea allows for synthesizing data-driven controllers by solving at each time step an on-line optimization problem whose complexity is comparable to the LTI case. Further, the proposed approach is not limited to rational dynamics. The main result of the paper shows that the feasibility of this on-line optimization problem guarantees that the proposed controller renders the origin a globally asymptotically stable equilibrium point of the closed-loop system. These results are illustrated with some simple examples. The paper concludes by briefly discussing the prospects for adding performance criteria.

关键词： Controllers

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On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks 26

On affine quasi-LPV System Identification with unknown state...

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26th International conference on System Theory, Control and Computing (ICSTCC)

作者： Rehmer, Alexander Kroll, Andreas Univ Kassel Kassel Dept Measurement & Control Kassel Germany

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

The main contribution of this paper is a model structure for the identification of affine quasi linear parameter-varying (qLPV) models in state-space (SS) form. The special case where the state is unknown and part of the scheduling vector is considered. In this case the model is always recurrent in the states which demands for an internal dynamics approach, e.g. a Recurrent Neural Network (RNN). The proposed approach is based on a structured RNN developed by Lachhab et al. [1], but extends the original model via so-called gates, neural network structures which are responsible for the recent success of RNNs on various areas of application in Machine Learning, such as the Long short-term memory (LSTM) network and the Gated Recurrent Unit (GRU). The use of gates has multiple advantages: The complexity of the models scheduling map and hence its approximation capabilities are increased, while preserving its affine quasi-LPV structure and the applicability of the results on global asymptotic stability (GAS) by Lachhab et al. [1] at the same time. The performance of the proposed approach is demonstrated by comparison with two other RNN-based approaches on two nonlinear system identification benchmark problems.

关键词： System Identification and Modeling Machine Learning nonlinear Systems LPV

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Fuzzy C-Means Model Based Multimodeling for nonlinear Distributed Parameter Systems

Fuzzy C-Means Model Based Multimodeling for Nonlinear Distri...

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IEEE International conference on Control Science and Systems Engineering (CCSSE)

作者： Zhuoqin Zhang Hai Dong Yang Kangkang Xu Chengjiu Zhu School of Electromechancal Engineering Guangdong University of Technology Guangzhou China

Complex nonlinear distributed parameter systems are commonly found in real industrial thermal processes. These systems are characterized by strong nonlinearity and time-variability, making it difficult to apply traditional single spatiotemporal models. To address this, we propose a multivariate spatiotemporal modeling method for complex nonlinear DPSs based on Fuzzy C-means model (FCM) and principal component regression (PCR). The method consists of three parts: FCM-based operation space separation, locally linear embedding (LLE) based local spatiotemporal modeling, and PCR-based local spatiotemporal model integration. The FCM-based multiple modeling approach reduces nonlinearity complexity, while LLE better retains local linear features. Therefore, the model can effectively track and handle complex nonlinear dynamics. The simulation results of lithium-ion batteries demonstrated the effectiveness of the proposed method.

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Secondary Structure Classification of Low-homology Proteins with Graph Neural Networks 30

Secondary Structure Classification of Low-homology Proteins ...

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30th European Signal Processing conference (EUSIPCO)

作者： Zervou, Michaela Areti Doutsi, Effrosyni Tsakalides, Panagiotis Fdn Res & Technol Hellas Inst Comp Sci Iraklion Greece Univ Crete Dept Comp Sci Iraklion Greece

ISBN: (纸本)9789082797091

Acquiring knowledge of the dynamics and properties that force each protein into a unique secondary structure is highly important in medicine and biotechnology. The majority of existing prediction methods rely on complicated processes to extract numerous representative features that are later paired with state-of-the-art classifiers. A recent study proposes a recurrent neural network architecture demonstrating for the first time the potential of deep learning architectures for secondary structure classification of low-homology proteins. According to the latest state-of-the-art, proteins can be successfully described as graphs for the task of classification providing substantial results while at the same time addressing serious challenges including dealing with datasets with different sample sizes, among others. Thus, this study employs an efficient graph representation of low-homology proteins along with graph neural networks which have exhibited cutting-edge performance in a variety of graph-based applications, including protein sequence classification. Overall, this work proposes a novel, simple and fast architecture based on graph neural networks, with a significantly reduced representative feature set, when compared to the literature, that derives directly through the sequence-to-graph procedure. Experimental evaluation on real protein datasets demonstrates the superiority of our proposed architecture, with respect to accuracy and overall complexity when compared against the state-of-the-art.

关键词： Secondary structure classification Graph neural networks Multidimensional Horizontal visibility graph nonlinear time series analysis

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Estimating Fuel Characteristics from Simulated Circulating Fluidized Bed Furnace Data 9

Estimating Fuel Characteristics from Simulated Circulating F...

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9th International conference on Systems and Control (ICSC)

作者： Neuvonen, Markus Selek, Istvan Ikonen, Enso Univ Oulu Intelligent Machines & Syst Oulu Finland

ISBN: (纸本)9781665407823

This paper proposes a soft sensor to estimate the elementary fuel characteristics in combustion-thermal power plants. The proposed approach is data-driven. The input-output data is generated by a digital twin. Application targets circulating fluidized bed boiler, where furnace (combustion) side is considered only. First, the nonlinear dynamics of the furnace is approximated with a linear time-invariant dynamic model. Then two separate methods, Kalman filter and internal governor, are applied for state estimation. Results show that the approach is viable and has low computational complexity, but the weakly observable modes are difficult to predict accurately.

关键词： linear and nonlinear systems estimations and identification monitoring and diagnostics

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Fully Distributed Resilient Secondary Control of Microgrids

Fully Distributed Resilient Secondary Control of Microgrids

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International conference on

作者： Minglei Jia Qian Li Duo Yang Jiaoqi Li Shoudong Zhang Dawei Yan Yingkou Power Supply Company State Grid Liaoning Electric Power Co. Ltd. Yingkou China Liaoning Agricultural Vocational and Technical College Yingkou China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

We propose a resilient voltage regulation strategy for microgrids based on a directed communication network. Initially, we employ feedback linearization techniques to address the inherent nonlinear complexity in distributed generation (DG) dynamics. Additionally, we design a resilient secondary voltage controller that may be susceptible to false data injection (FDI) attacks. This control strategy reduces reliance on global information and boundaries of attack signals within the communication network, thereby enhancing system resilience and efficiency. Finally, through comprehensive simulation experiments, we evaluate the effectiveness and performance of the proposed strategy. Simulation results validate the success of the method in voltage regulation and fault management. These findings are of significant importance for the development of voltage regulation strategies in distributed generation systems.

关键词： Fault tolerance Simulation Fault tolerant systems Microgrids Distributed power generation nonlinear dynamical systems Feedback linearization Communication networks Voltage control Resilience

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VMF: Enhancing Speed Control System of BLDC motor via Estimating the Total Bias based on Variation Model Filter

VMF: Enhancing Speed Control System of BLDC motor via Estima...

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Modeling, Simulation & Intelligent Computing (MoSICom), International conference on

作者： Abdul Rahman Abdul Majid Dept. of Electrical & Computer Engineering. College of Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Brushless DC (BLDC) motors are widely adopted for their efficiency, precision, and dynamic performance, yet their nonlinear and multivariable dynamics pose significant challenges for conventional control methods. This paper introduces a novel Variation Model Filter (VMF)-based control approach to address these issues. Unlike traditional PID controllers, which struggle with the time-varying nature of BLDC systems, and advanced methods like ADRC that involve high complexity, the proposed VMF technique estimates and compensates for total bias within the system, enhancing robustness and response. Simulation results demonstrate that the VMF-based PID control, also called variation-based control system, outperforms both linear ADRC and conventional PID control in speed regulation and load disturbance rejection. This work presents a robust and simplified solution, offering substantial improvements in BLDC motor control efficiency and cost-effectiveness, with potential for further optimization and practical applications.

关键词： Robust control PI control Velocity control Motors Robustness Regulation complexity theory PD control Time-varying systems Load modeling

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Practical nonlinear Model Predictive Control for Piezoelectric Actuators Based on an Echo State Network

Practical Nonlinear Model Predictive Control for Piezoelectr...

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Chinese Control conference (CCC)

作者： Fei Zhao Yinan Wu Huawang Liu Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Tianjin China

The piezoelectric actuator (PEA) is widely used to realize high precise motion of the micro-systems. However, the inherent nonlinear property such as hysteresis, creep, thermal drift and vibration impairs the overall performance and leads to the system instability. In this paper, a practical nonlinear model predictive control (PNMPC) approach based on an echo state network is proposed for the displacement tracking of PEAs. The proposed method has the advantage of omitting the common building process of an inversion model for hysteresis compensation, which avoids the problem of model inversion and the inversion imprecision. Specifically, an echo state network (ESN) is adopted to model the nonlinear dynamics of the PEA system. On this basis, the PNMPC method is designed via utilizing the linearized ESN model, which reduces the computational complexity of the optimization problem caused by nonlinear model predictive control. Finally, the tracking performance of the proposed controller is verified through experimental results.

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Research on Neural Network Adaptive Dynamic Sliding Mode Formation Algorithm for Multiple Unmanned Surface Vehicles

Research on Neural Network Adaptive Dynamic Sliding Mode For...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Hao Meng Zijie Liu Hui Meng System Engineering Research Institute of China State Shipbuilding Corporation Limited Beijing China Northwestern Polytechnical University Xi'an China School of Software Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

This paper investigates an adaptive dynamic sliding mode control algorithm based on Chebyshev neural networks, applied to the formation control problem of multiple unmanned surface vehicles (USVs). The algorithm combines the self-learning capability of neural networks with the robustness of sliding mode control, enabling stable formation control under uncertain environments, external disturbances, and actuator hysteresis. The Chebyshev neural network is utilized to estimate nonlinear functions in the system, reducing computational complexity and improving real-time performance. Additionally, the dynamic sliding mode control method effectively suppresses chattering. Simulation results demonstrate that the proposed method excels in control accuracy, real-time performance, and robustness, providing an effective solution for the formation control of multiple USVs.

关键词： Adaptive systems Heuristic algorithms Neural networks Chebyshev approximation Formation control Stability analysis Robustness nonlinear dynamical systems Vehicle dynamics Sliding mode control

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Design and Implementation of a Cascade Control Scheme for the TCLab Temperature Device

Design and Implementation of a Cascade Control Scheme for th...

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IEEE International conference on Automatica (ICA-ACCA)

作者： Luis Caiza Diego S. Benítez Oscar Camacho Colegio de Ciencias e Ingenierías Universidad San Francisco de Quito USFQ Quito Ecuador

ISBN: (数字)9798350378115

ISBN: (纸本)9798350378122

This paper explores the implementation of cascade control strategies for the Temperature Control Laboratory (TCLab) temperature device. Chemical processes in industrial applications are inherently complex and nonlinear and often involve higher-order dynamics, making them susceptible to disturbances and uncertainties. The TCLab, a widely used educational and research tool, exemplifies these challenges. In this study, we design and analyze a cascade control system to improve the regulation of temperature within the TCLab setup. The primary controller addresses the overall temperature control, while a secondary controller mitigates the effects of disturbances and enhances response time. Experimental results demonstrate the effectiveness of the cascade control approach in achieving superior performance compared to traditional single-loop control methods. This work provides valuable insights into the practical application of advanced control techniques in temperature regulation, contributing to a broader understanding of control systems in chemical process engineering.

关键词： Performance evaluation Uncertainty Education Control systems Regulation Temperature control Chemical processes nonlinear dynamical systems complexity theory Time factors

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