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2023 China Automation Congress, CAC 2023

作者： Xiao, Jinkun Zhu, Haibo Zhou, Fan Sun, Kaibiao Zhao, Jun Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment Ministry of Education China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350303759

Fluidized catalytic cracking (FCC) units are crucial equipment in the petroleum refining process, and the level of control directly impacts product quality and yield. To improve the control level of the desorption tower in FCC, this paper proposes a dynamic matrix control (DMC) approach based on the active disturbance rejection strategy. Based on DMC, a linear extended state observer (LESO) is constructed to compensate for model plant mismatch (MPM) and unknown disturbances. The observer bandwidth parameters are determined to optimize robustness, enabling the implementation of active disturbance rejection model predictive control in FCC units. A comparative experimental study is conducted, comparing the proposed method with the traditional DMC approach that does not account for uncertainties and disturbances. The impact of different disturbances on the control strategy is discussed. Experimental results demonstrate that the proposed method exhibits superior control performance and stronger disturbance rejection capability for the liquid level in the desorption tower of the FCC unit. © 2023 IEEE.

关键词： Fluidization

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On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach ⁎

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IFAC-PapersOnLine 2024年第15期58卷 289-294页

作者： R. Schwan N. Schmid E. Chassaing K. Samaha C.N. Jones Automatic Control Lab EPFL Lausanne Switzerland Risk Analytics and Optimization Chair EPFL Lausanne witzerland

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, allowing it to float and be controlled freely on an air hockey table. We learn parametrized physics-inspired nonlinear models directly from data trajectories, leveraging gradient-based optimization techniques prevalent in machine learning research. The chosen model structure allows us to control the position of the hovercraft precisely on the air hockey table. We then analyze the prediction performance and demonstrate the closed-loop control performance on the real system.

关键词： Nonlinear system identification Learning for control Hovercraft Parametric modeling Air hockey Physics-inspired modeling

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An Efficient Solution to the 2D Visibility Problem in Cartesian Grid Maps and its Application in Heuristic Path Planning

arXiv

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arXiv 2024年

作者： Ibrahim, Ibrahim Gillis, Joris Decré, Wilm Swevers, Jan Motion Estimation Control and Optimization Lab. KU Leuven Belgium

This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of lines-of-sight from a source point to all other grid cells in a single pass with no preprocessing and independently of the number and shape of obstacles. It has a compute and memory complexity of O(n), where n = nx × ny is the size of the grid, and requires at most ten arithmetic operations per grid cell. In the proposed approach, we use a linear first-order hyperbolic partial differential equation to transport the visibility quantity in all directions. In order to accomplish that, we use an entropy-satisfying upwind scheme that converges to the true visibility polygon as the step size goes to zero. This dynamic-programming approach allows the evaluation of visibility for an entire grid orders of magnitude faster than typical ray-casting algorithms. We provide a practical application of our proposed algorithm by posing the visibility quantity as a heuristic and implementing a deterministic, local-minima-free path planner, setting apart the proposed planner from traditional methods. Lastly, we provide necessary algorithms and an open-source implementation of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Visibility

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Fault identification of rotating machinery based on multiscale convolution and deep residual networks 9

Fault identification of rotating machinery based on multisca...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wang, Huihui Fan, Tianqi Qiu, Sen Liu, Long School of Intelligence and Electronic Engineering Dalian Neusoft University of Information Dalian116023 China Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781510672802

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly available datasets, and the results demonstrate the excellent performance of the proposed method. © 2024 SPIE.

关键词： Convolution Feature extraction Deep learning Machine learning Vibration Data modeling Lithium Convolutional neural networks Data centers Signal processing

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RBFNN-Based ADRC Design for Continuous-Time Systems with Unknown Nonlinear Dynamics Subject to Time-Varying Disturbance 42

RBFNN-Based ADRC Design for Continuous-Time Systems with Unk...

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42nd Chinese control Conference, CCC 2023

作者： Yan, Shuai Hao, Shoulin Yu, Haichen Liu, Tao Yan, Bin Gong, Yihui Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789887581543

In this paper, a radial basis function neural network (RBFNN) based active disturbance rejection control (ADRC) scheme is proposed for continuous-time systems with unknown nonlinear dynamics and time-varying disturbance. By using RBFNN to online approximate the unknown nonlinear dynamics, a novel nonlinear extended state observer (ESO) is firstly designed to estimate the total disturbance consisting of time-varying external disturbance and RBFNN approximation error. Then, an anti-disturbance feedback control law together with a tracking differentiator is designed to counteract the total disturbance in a feedforward manner. The bounded convergence of the closed-loop system and ESO as well as the estimation errors of the weighting vector are rigorously analyzed based on the Lyapunov stability theory. A case study is carried out to demonstrate the effectiveness and merit of the proposed design, in contrast to the conventional ADRC. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Continuous time systems

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Best Response Convergence for Zero-sum Stochastic Dynamic Games with Partial and Asymmetric Information

arXiv

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arXiv 2025年

作者： Guan, Yuxiang Shames, Iman Summers, Tyler H. Control Optimization and Networks Lab University of Texas Dallas United States CIICADA Lab Australian National University Australia

We analyze best response dynamics for finding a Nash equilibrium of an infinite horizon zero-sum stochastic linear quadratic dynamic game (LQDG) with partial and asymmetric information. We derive explicit expressions for each player’s best response within the class of pure linear dynamic output feedback control strategies where the internal state dimension of each control strategy is an integer multiple of the system state dimension. With each best response, the players form increasingly higher-order belief states, leading to infinite-dimensional internal states. However, we observe in extensive numerical experiments that the game’s value converges after just a few iterations, suggesting that strategies associated with increasingly higher-order belief states eventually provide no benefit. To help explain this convergence, our numerical analysis reveals rapid decay of the controllability and observability Gramian eigenvalues and Hankel singular values in higher-order belief dynamics, indicating that the higher-order belief dynamics become increasingly difficult for both players to control and observe. Consequently, the higher-order belief dynamics can be closely approximated by low-order belief dynamics with bounded error, and thus feedback strategies with limited internal state dimension can closely approximate a Nash equilibrium. Copyright © 2025, The Authors. All rights reserved.

关键词： Nash equilibrium

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Model Reduction of Consensus Network Systems via Selection of Optimal Edge Weights and Nodal Time-Scales

Model Reduction of Consensus Network Systems via Selection o...

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2022 American control Conference, ACC 2022

作者： Sabbagh, Ralph Jaoude, Dany Abou Maroun Semaan Faculty of Engineering and Architecture American University of Beirut Control and Optimization Lab Department of Mechanical Engineering Lebanon

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This paper proposes model reduction approaches for consensus network systems based on a given clustering of the underlying graph. Namely, given a consensus network system of time-scaled agents evolving over a weighted undirected graph and a graph clustering, a parameterized reduced consensus network system is constructed with its edge weights and nodal time-scales as the parameters to be optimized. {mathcal{H}infty }-and {mathcal{H}2}-based optimization approaches are proposed to select the reduced network parameters such that the corresponding approximation errors, i.e., the {mathcal{H}infty }-and {mathcal{H}infty }-norms of the error system, are minimized. The effectiveness of the proposed model reduction methods is illustrated via a numerical example. © 2022 American Automatic control Council.

关键词： Numerical methods

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ESO-Based Data Driven Set-Point Learning control for Nonlinear batch Processes with PD-Type Feedback control Structure Subject to Nonrepetitive Uncertainties* 13

ESO-Based Data Driven Set-Point Learning Control for Nonline...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Ahmad, Naseem Hao, Shoulin Liu, Tao Gong, Yihui Zhang, Jiyan Wang, Haixia Zhu, Yong Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China School of Biomedical Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example. © 2024 IEEE.

关键词： State estimation

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On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach

arXiv

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arXiv 2024年

作者： Schwan, R. Schmid, N. Chassaing, E. Samaha, K. Jones, C.N. Automatic Control Lab EPFL Lausanne Switzerland Risk Analytics and Optimization EPFL Lausanne Switzerland

关键词： Deep learning

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Data-Driven Stabilization for a Class of Unknown Switched Linear Systems with Asynchronism 43

Data-Driven Stabilization for a Class of Unknown Switched Li...

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43rd Chinese control Conference, CCC 2024

作者： Qi, Wan-Ling Liu, Kun-Zhi Cheng, Xiao-Lin Wang, Rui Dalian University of Technology School of Control Science and Engineering Dalian116024 China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Dalian University of Technology School of Aeronautics and Astronautics Dalian116024 China

ISBN: (纸本)9789887581581

This brief investigates the data-driven stabilization for a class of unknown switched linear systems in both continuous and discrete time. We assume that the switching signal is known but the system matrices of each subsystem are unknown and belong to some sets described by finite noisy input-state data. Data-driven parameterization of each unknown subsystem is built on the instantaneous bound of the external disturbance rather than the energy bound. The closed-loop stability is guaranteed by adopting a controller-mode-dependent Lyapunov function and the resulting stability conditions are data-based rather than model-based. The switching signals are arbitrary as long as they satisfy the average dwell time (ADT) condition that derived from noisy data. Two numerical examples are presented to illustrate how to enhance the feasibility of the proposed methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Stabilization

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