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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351-360 订阅

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Iterative learning for Nonlinear systems with Variable Pass Lengths: Fault Estimation in the Presence of Actuator Faults

Iterative Learning for Nonlinear Systems with Variable Pass ...

引用

data driven control and learning systems (ddcls)

作者： Shaodong Gu Chuang Chen Jiantao Shi College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this research addresses the faults estimation (FE) of the actuator faults in nonlinear dynamic systems with varying pass lengths. Additionally, the adverse impact of the bounded disturbances and measurement noise on the systems is also considered. A nonlinear iterative learning observer (NILO) is designed to achieve fault estimation. To tackle information loss and redundancy caused by variable pass lengths, a novel enhanced weighted averaging operator for the NILO is proposed. this approach enables fast tracking of fault signals and enhances system robustness. the method ensures the final boundedness of tracking error. Finally, the simulation results are given to verify that the proposed approach is validated by numerical simulation studies.

关键词： Actuators Simulation Redundancy Estimation Observers Numerical simulation Robustness

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Optimal control of Discrete-Time Stochastic systems with Wiener and Poisson Noises: A Model-Free Reinforcement learning Approach

Optimal Control of Discrete-Time Stochastic Systems with Wie...

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data driven control and learning systems (ddcls)

作者： Zhiguo Yan Tingkun Sun Guolin Hu Shandong Academy of Sciences School of Information and Automation Qilu University of Technology Jinan China Shandong Academy of Sciences School of Mathematics and Statistics Qilu University of Technology Jinan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper investigates the problem of optimal control of discrete stochastic Poisson systems. First, the discrete stochastic algebraic Riccati equation containing Poisson noises is proved, and a model-based iterative method is proposed to solve it. Second, by using the system reconstruction method, a model-free reinforcement learning algorithm is presented, which operates without necessitating system matrixs and relies solely on the state trajectory information to derive the optimal solution. third, it demonstrates that the incorporation of exploration noises does not impact the results of the algorithm. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.

关键词： Stochastic systems Noise Riccati equations Stochastic processes Optimal control Reinforcement learning Mathematical models

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Predefined-Time Attitude Tracking control of Spacecraft Using Incremental Backstepping control Approach

Predefined-Time Attitude Tracking Control of Spacecraft Usin...

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data driven control and learning systems (ddcls)

作者： Haichao Zhang Bing Xiao School of Automation Northwestern Polytechnical University Xi'an P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this work, a predefined-time attitude tracking control scheme is developed using the incremental control method. the Taylor series is employed to transform the attitude control system into a discrete-time plant with the control input in an incremental form. A new stable dynamical system with a predefined convergence time is devised for the first time. As a stepping stone, the backstepping control method and the incremental dynamics system are used to obtain the predefined-time attitude incremental controller. Unlike conventional backstepping controllers, it does not require knowing about the system drift in advance. the Lyapunov methodology guarantees that the attitude control system, when applied with the incremental backstepping controller, will remain predefined-time stable regardless of the initial conditions. the efficacy of the suggested control scheme is validated using numerical simulation.

关键词： Space vehicles learning systems Backstepping Attitude control Transforms Taylor series Numerical simulation

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Improved Residual Reinforcement learning for Dynamic Obstacle Avoidance of Robotic Arm

Improved Residual Reinforcement Learning for Dynamic Obstacl...

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data driven control and learning systems (ddcls)

作者： Zhenting Liu Shan Liu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper proposes an improved residual deep reinforcement learning method for robot arm dynamic obstacle avoidance and position servo. the proposed method first simplifies the state space by constructing key points and pre-trains a model capable of completing obstacle avoidance tasks using the simplified state. then, when training with real state information, a guiding network is used to help accumulate good samples, which improves the training efficiency. To overcome the convergence difficulty of residual DQN in robot arm obstacle avoidance, this paper incorporates the action of the feedback controller into the action space and uses incremental reward values to evaluate the actions. Simulation results demonstrate that the proposed method can effectively achieve robot arm dynamic obstacle avoidance and position servo.

关键词： Training learning systems Simulation Aerospace electronics Manipulators Deep reinforcement learning Collision avoidance

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Adaptive Neural Network-Based Terminal Sliding Mode control for AUV

Adaptive Neural Network-Based Terminal Sliding Mode Control ...

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data driven control and learning systems (ddcls)

作者： Wenjie Jia Shubo Wang Qingyi Gao College of Automation Qingdao Unversity Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper proposes a fast terminal sliding mode control based on adaptive neural network to address the problem of external interference and internal uncertainty in trajectory tracking control for a six degree of freedom autonomous underwater vehicle. A kind of radial basis neural network is employed to estimate external disturbances and internal uncertainties. In constructing the sliding mode surface, an improved funnel function is defined and integrated into the sliding mode surface. A rapid terminal sliding mode controller based on adaptive neural networks is designed to ensure the output error remains within a predefined range and can rapidly converge to zero. Simulation results validate the effectiveness of the proposed control approach.

关键词： learning systems Sea surface Adaptive systems Uncertainty Trajectory tracking Simulation Neural networks

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A Novel data-driven Hybrid Adaptive control

A Novel Data-Driven Hybrid Adaptive Control

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data driven control and learning systems (ddcls)

作者： Jun Hao Guoshan Zhang Mengxuan Zhang Yu Wang College of Automation Jiangsu University of Science and Technology Jiangsu China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

As complementary methods to classical model-based control, data-driven control methods can avoid modeling the dynamics of complex systems but achieve good control performance, hence, data-driven control methods are gradually becoming an important and hot topic in control science and engineering. However, some issues in data-driven control methods still need to be further addressed such as physical interpretability, excessive hyperparameters, control parameter valid initialization, etc. In this paper, a novel algorithm namely data-driven hybrid adaptive control (DHAC) is proposed, which fully integrates parameter tuning theory and artificial neural network theory. Specifically, DHAC algorithm as a model-free method has the advantages of classical control methods and adaptive neural networks methods, while eliminating the drawbacks of both two types of methods. Hence, DHAC algorithm has the potential to provide new method to solve the issues of control parameters valid initializations, physical interpretability, excessive hyperparameters in the area of model free control.

关键词： learning systems Adaptation models Trajectory tracking Solids Stability analysis Reliability Nonlinear systems

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Model-Free $H_{2}/H_{\infty}$ Predictive control for Discrete-Time System via Q-learning

Model-Free $H_{2}/H_{\infty}$ Predictive Control for Discret...

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data driven control and learning systems (ddcls)

作者： Yihong Lin Peng He Haiying Wan Zhuangyu Liu Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a model-free $H_control/H_{\infty}$ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed $H_control/H_{\infty}$ control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach.

关键词： Discrete-time systems Q-learning Simulation Heuristic algorithms Predictive models Prediction algorithms Approximation algorithms

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Adaptive learning control for High-Order Uncertain Nonlinear Multiagent systems under Replay Attacks

Adaptive Learning Control for High-Order Uncertain Nonlinear...

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data driven control and learning systems (ddcls)

作者： Ying Li Youqing Wang Dong Zhao College of Information Science and Technology Beijing University of Chemical Technology Beijing China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this study, we focus on the adaptive learning control of high-order uncertain nonlinear multiagent systems (MASs) under replay attacks. the neural-network-based adaptive learning scheme is proposed to approximate the nonlinear MASs uncertainty dynamics. Under the undirected graph of MASs communication, the distributed controller and parameter estimator are designed by using the backstepping method. In particular, the boundary condition for the MASs in the presence of replay attacks is analyzed. It is shown that the follower can track the leader asymptotically under replay attacks if the boundary condition is satisfied. Simulation results demonstrate the effectiveness of the proposed theoretical approach.

关键词： learning systems Adaptive learning Uncertainty Backstepping Simulation Neural networks Boundary conditions

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data-driven Q-learning control for Nonlinear systems Involving Parallel Multi-Step Deduction

Data-Driven Q-Learning Control for Nonlinear Systems Involvi...

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data driven control and learning systems (ddcls)

作者： Jiangyu Wang Ding Wang Mingming Zhao Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

When facing large amounts of data, it is a challenging task to optimize policies by using all data at once. In this paper, a data-driven Q-learning scheme with parallel multi-step deduction is developed to improve learning efficiency using small batch data for discrete-time nonlinear control. Specifically, a data-driven model is established by making use of all data in advance. then, the proposed algorithm can parallel deduce the small batch data to effectively accelerate the learning process. Furthermore, we can adjust the step size of multi-step deduction to balance the utilization between data and model. the near-optimal policy can be obtained ultimately by using hybrid data from the real system and data-driven model. Finally, a torsional pendulum plant is given to demonstrate the effectiveness of the proposed method.

关键词： learning systems Adaptation models Q-learning Adaptive systems control systems data models Task analysis

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data-driven Model Categorization: Advancing Physical systems Analysis through Graph Neural Networks 13

Data-Driven Model Categorization: Advancing Physical Systems...

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13th International conference on data Science, Technology and Applications, data 2024

作者： Grbavac, Andrija Angerbauer, Martin Grill, Michael Kulzer, André Casal University of Stuttgart Pfaffenwaldring 12 Stuttgart Germany University of Stuttgart Stuttgart Germany

ISBN: (纸本)9789897587078

Efficiently categorizing physical system models is crucial for data science applications in scientific and engineering realms, facilitating insightful analysis, control, and optimization. While current methods, often relying on Convolutional Neural Networks (CNNs), effectively handle spatial dependencies in image data, they struggle with intricate relationships inherent in physical system models. Our research introduces a novel approach employing Graph Neural Networks (GNNs) to enhance categorization. GNNs excel in modeling complex relational structures, making them apt for analyzing interconnected components within physical systems represented as graphs. Leveraging GNNs, our methodology treats entities as system components and edges as their arrangements, effectively learning and exploiting inherent dependencies and interactions. the proposed GNN-based approach outperforms CNN-based methods across a dataset of 55 physical system models, eliminating limitations observed in CNN approaches. the results underscore GNNs’ ability to discern subtle interdependencies and capture non-local patterns, enhancing the accuracy and robustness of model categorization in a data science framework. this research contributes to advancing model categorization, emphasizing the application of data science for understanding and controlling complex physical systems. the innovative use of GNNs opens new avenues for revolutionizing the categorization of intricate physical system models in scientific and engineering domains. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Neural network models

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