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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是361-370 订阅

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Particle Swarm Optimization Tuned Compensation Function Observer Based control Method for Ship Motion

Particle Swarm Optimization Tuned Compensation Function Obse...

引用

data driven control and learning systems (ddcls)

作者： Jia Ren Zhaoxia Wang Zengqiang Chen Mingwei Sun Qinglin Sun College of Artificial Intelligence Nankai University Tianjin China School of Computing and Information Systems Singapore Management University Singapore Singapore Key Lab of Intelligent Robotics of Tianjin Tianjin China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To achieve more stable and rapid control of ship motion, we proposed the Compensation Function Observer (CFO)-based control algorithm and used the Particle Swarm Optimization (PSO) to adjust its parameters. the performance of the proposed method is compared with PSO based Active Disturbance Rejection control (ADRC) and traditional ADRC. the simulation results show that the ship motion system controlled by our proposed method has smaller deviation and jitter. Even under different interference, it still has better performance.

关键词： Robust control learning systems Industries Simulation Interference Observers Jitter

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Model-Free Adaptive control of Nonlinear systems with Input Backlash and Constraints

Model-Free Adaptive Control of Nonlinear Systems with Input ...

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data driven control and learning systems (ddcls)

作者： Leilei Hao Xiaoqing Jiang Xiaona Liu Yumeng Zhong Hang Li Ruixian Su School of Information and Control Engineering China University of Mining and Technology Xuzhou China Collage of information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

For nonlinear Backlash systems with input constraints that are difficult to model accurately, a model-free adaptive control algorithm (MFAC)based on data-driven technology is proposed. the criterion function of MFAC algorithm is applied to set up a quadratic programming problem with input constraints, and the optimal control input signal is obtained by solving the quadratic programming problem. the tracking differentiator is used to process the measured output signal to reject the adverse effect of random noise on the solution of the optimal input signal and the control stationarity. the stability of the control algorithm is also analyzed and proved. Finally, the superiority of the proposed control algorithm is verified by example test.

关键词： Adaptation models Stability criteria Noise Optimal control Process control Quadratic programming Noise measurement

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Asynchronous $l_{2}-l_{\infty}$ Filtering for Discrete-Time Singular Nonhomogeneous Markov Jump systems

Asynchronous $l_{2}-l_{\infty}$ Filtering for Discrete-Time ...

引用

data driven control and learning systems (ddcls)

作者： Chenxin Wu Mingang Hua College of Artificial Intelligence and Automation Hohai University Changzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this study investigates the design of $l_{2}-l_{\infty}$ filters for asynchronous discrete-time Singular nonhomogeneous Markov jump systems. Using a polytope set to characterize the time-varying transition probability. To describe the asynchronous phe-nomenon between system modes and filter modes, hidden Markov models are proposed. Based on the selected Lyapunov function, sufficient conditions are provided to ensure that the filtering error system is regular, causal and stochastically stable with a given $l_{2}-l_{\infty}$ performance index. By solving the proposed conditions, an expected filter that satisfies prescribed $l_{2}-l_{\infty}$ performance index can be constructed. In the end, the effectiveness and potential of this method were verified through two illustrative examples.

关键词： learning systems Sufficient conditions Filters Hidden Markov models control systems data models Performance analysis

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Improved D-Type Iterative learning control for Hyperbolic Time-Delay Distributed Parameter systems Under Sensor/Actuator Networks

Improved D-Type Iterative Learning Control for Hyperbolic Ti...

引用

data driven control and learning systems (ddcls)

作者： Jianxiang Zhang Weitai Gong Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou P. R. China School of Automation Guangxi University of Science and Technology

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper investigates the problem of iterative learning control in sensor/actuator networks for hyperbolic distributed parameter systems with time delays. For systems with known delays, this study firstly proposes an improved D-type iterative learning algorithm and proves the convergence of the iterative learning control strategy for nonlinear systems with control delays based on the principle of contraction mapping. Furthermore, by examining a nonlinear hyperbolic distributed parameter system with state delays, this paper verifies the convergence of the proposed algorithm through detailed mathematical derivations. To empirically validate the effectiveness and feasibility of the proposed algorithm, the paper finally demonstrates the performance of the iterative learning control strategy based on the proposed convergence conditions through numerical simulations. the results of the numerical simulations strongly support the applicability of the designed iterative learning algorithm in controlling hyperbolic distributed parameter systems with time delays.

关键词： Sufficient conditions Delay effects Numerical simulation control systems Iterative algorithms Delays Sensors

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A Deep learning Method for the Endpoint Carbon Prediction in BOF Steelmaking Process

A Deep Learning Method for the Endpoint Carbon Prediction in...

引用

data driven control and learning systems (ddcls)

作者： Bingkun Liang Kang Wang Xiaoli Li Faculty of Information Technology Beijing University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In many basic oxygen furnace (BOF) steelmaking processes, if the furnace endpoint carbon can be monitored in real time, it is a breakthrough for BOF steelmaking intelligence. this paper presents a deep learning model used to predict the endpoint carbon content in BOF steelmaking process. A convolution long short-term memory network based on attention mechanism (CNN-LSTM-AM) model is proposed for time-series data in BOF process to extract spatial-temporal characteristics of time sequence features and a back propagation (BP) model is proposed for pre-furnace data to auxiliary increase the accuracy of the model. the BOF steelmaking data from an actual process were used for the testing, the result shown that 84.34% of prediction result were within the ±0.02 range, which is better than use those two types of data and model individually.

关键词： Temperature sensors Deep learning Temperature measurement Temperature distribution Furnaces Predictive models Feature extraction

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ANCP: Attention-Based Neural Circuit Policies for Robot Spatiotemporal Reasoning in Crowd Navigation

ANCP: Attention-Based Neural Circuit Policies for Robot Spat...

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data driven control and learning systems (ddcls)

作者： Tianyong Ao Yongmeng Tian Le Fu Yi Zhou School of Artificial Intelligence Henan University Zhengzhou Henan China International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan University Zhengzhou Henan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper explores the complexities of autonomous navigation for mobile robots in densely populated environments, where the accuracy of crowd behavior modeling is crucial. Recent studies have increasingly leveraged deep reinforcement learning and biologically inspired methodologies, which demonstrate enhanced learning capabilities and adaptability in intricate settings. this work proposes a novel attention-based neural circuit policies (ANCP) that merges biological neural mechanisms with attention processes to facilitate spatiotemporal reasoning in robot navigation. We evaluated the algorithm in demanding simulation settings, and the findings indicate that our approach adapts more effectively to crowded scenarios compared with the baseline algorithms.

关键词： learning systems Navigation Neural circuits Deep reinforcement learning control systems Cognition Biology

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Multiple Uncertainty Rejection-Based Power Lifting Adaptive Fixed-Time Tracking control

Multiple Uncertainty Rejection-Based Power Lifting Adaptive ...

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data driven control and learning systems (ddcls)

作者： Yang Liu Hongru Ren Yu Hui Hongyi Li College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao P. R. China School of Automation and Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this work investigates an adaptive fixed-time tracking control issue of nonlinear systems with the multiple uncertainty. A practically fixed-time lemma is established, and the singularity problem of fixed-time tracking control is handled by exploiting the power lifting technique. Moreover, a nonlinear fixed-time tracking scheme is proposed under the framework of the backstepping, and a simple design process is presented, which can simultaneously tackle uncertain parameters and disturbances. Finally, the numerical simulation is to explain the effectiveness of the proposed method.

关键词： learning systems Uncertainty Backstepping Simulation control systems Numerical simulation Nonlinear systems

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Scalable Reliable control for Cyber- Physical Microgrids Using Chordal Graph

Scalable Reliable Control for Cyber- Physical Microgrids Usi...

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data driven control and learning systems (ddcls)

作者： Yunlong Zhao Yang Song Aimin Wang Yonghui Chen Kamyar Mehran School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China School of Electronic Engineering and Computer Science Queen Mary University London United Kingdom

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

We consider a microgrid system with sparse structure, where the security is susceptible to the impact of potential faults. To enhance the reliability and scalability of the microgrid system, this paper investigates the structured controller design problem for the microgrid system under fault conditions using chordal decomposition. By establishing a microgrid fault model with a sparse structure, the controller design problem for large-scale microgrid systems is decomposed into a set of smaller-scale problems using chordal decomposition, and then solved clique-by-clique in a sequential approach. the results demonstrate that this approach reduces the sensitivity of the microgrid system to faults and improves solution efficiency and scalability.

关键词： learning systems data privacy Sensitivity Scalability Microgrids Reliability engineering Stability analysis

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Stability and Stabilization of data-driven Discrete-time Linear Impulsive systems

Stability and Stabilization of Data-driven Discrete-time Lin...

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data driven control and learning systems (ddcls)

作者： Dongpeng Zhou Wu-Hua Chen Xiaomei Lu School of Electrical Engineering Guangxi University Nanning Guangxi P. R. China School of Mathematics and Information Science Guangxi University Nanning Guangxi

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper studies the problems of stability and stabilization for data-driven linear discrete-time impulsive systems (LDTISs). Firstly, open-loop and closed-loop data-based system representations of LDTISs are given by utilizing persistently exciting data. While the open-loop data-based system representation is an identification-like result, the closed-loop case bypasses the system identification, which can be used for directly data-driven control law design. Secondly, by utilizing time-dependent Lyapunov function technique, stability criteria for data-based closed-loop system are derived, and a data-driven state feedback controller are designed. Such criteria are independent of system matrices and can be formulated into a set of linear matrix inequalities plus a class of linear matrix equations, which can be solved efficiently by CVX toolbox. Finally, an example is presented to verify the efficiency of the established data-driven control law.

关键词： learning systems State feedback Stability criteria Linear matrix inequalities System identification Closed loop systems Lyapunov methods

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A Model-Free $H_{\infty}$ control Method Based on Q-learning for Linear Discrete-time System

A Model-Free $H_{\infty}$ Control Method Based on Q-learning...

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data driven control and learning systems (ddcls)

作者： Jiaxin Zhang Jian Liu School of Mechano-electronic Engineering Xidian University China Center for Complex Systems School of Mechano-electronic Engineering Xidian University China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the paper delves into the $H_{\infty}$ control problem of linear discrete-time systems under the circumstances of unknown system models and the presence of disturbances. this paper proposes a model-free $H_{\infty}$ control method based on Q-learning, in which, the dynamics of the system are unknown, meaning that apart from the accessible state and input variables of the system, other information about the system matrices remains unavailable. We have developed a reinforcement Q-learning algorithm that leverages historical input-output data to learn the optimal state feedback strategy. Furthermore, a full-information feedback controller is considered, which significantly enhances the system's capacity to handle disturbances, thereby improving overall system performance. It is shown that the developed Q-learning algorithm is convergent along the iteration. the effectiveness of this algorithm is demonstrated through simulation experiments.

关键词： Discrete-time systems State feedback Q-learning System dynamics Heuristic algorithms Atmospheric modeling System performance

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