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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yang, Yulin Liu, Shan Zhejiang Univ Polytech Inst Hangzhou 310015 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350321050

Aiming at the low robustness of image feature extractor in Image-Based Visual Servo (IBVS), a robot visual servo method based on object detection neural network YOLOv3 is proposed. By improving the output layer of YOLOv3 and adding attitude angle of camera, the pixel coordinate and depth information of feature points, the robustness of the IBVS system based on point features is improved while it can cope with multi-type and multi-instance objects, and the problem of the image Jacoby matrix falling into singularity caused by excessive rotation angle error of the optical axis is avoided. The visual servo convergence is accelerated. Meanwhile, the network training data generation algorithm of the desired image is used to replace the traditional manual data annotation, which reduces the cost of data acquisition, and the data enhancement method ensures the generalization performance of the training model.

关键词： Visual Servo Object Detection Manipulators

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A Multi-Head Self-Attention-based on GRU Encoder-Decoder Framework for Predicting Molten Iron Silicon Content 12

A Multi-Head Self-Attention-based on GRU Encoder-Decoder Fra...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Cai, Yu Yang, Chunjie Lou, Siwei Zeng, Zhenyu Liao, Huanyu Zhang, Bing Zhejiang Univ Coll Control Sci & Engn Hangzhou 310013 Peoples R China Alibaba Grp Hangzhou 311121 Peoples R China

ISBN: (纸本)9798350321050

Silicon content is a significant index in the process of blast furnace ironmaking. It is used to measure the quality of molten iron *** only meets the requirements if it is too high or too low. In the production process,the silicon content in molten iron needs to be controlled within a stable *** the same time,due to the time lag, nonlinear and dynamic characteristics of blast furnace itself, it is difficult to predict the silicon content accurately. This paper proposes a multi-head self-attention-based gate recurrent unit encoder-decoder framework that can better extract global dynamic features and local features, improve prediction accuracy and pass the experimental verification.

关键词： Blast Furnace Si Content Prediction Encoder-Decoder Multi-Head Attention Mechanism Gate Recurrent Unit

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learning Secure control Design for Cyber-Physical systems under False data Injection Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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ieee Transactions on Industrial Cyber-Physical systems 2024年 2卷 60-68页

作者： Fei, Cheng Shen, Jun Qiu, Hongling Zhang, Zhipeng Xing, Wei Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China

In this study, we employ two data-driven approaches to address the secure control problem for cyber-physical systems when facing false data injection attacks. Firstly, guided by zero-sum game theory and the principle of optimality, we derive the optimal control gain, which hinges on the solution of a corresponding algebraic Riccati equation. Secondly, we present sufficient conditions to guarantee the existence of a solution to the algebraic Riccati equation, which constitutes the first major contributions of this paper. Subsequently, we introduce two data-driven Q-learning algorithms, facilitating model-free control design. The second algorithm represents the second major contribution of this paper, as it not only operates without the need for a system model but also eliminates the requirement for state vectors, making it quite practical. Lastly, the efficacy of the proposed control schemes is confirmed through a case study involving an F-16 aircraft. © 2023 ieee.

关键词： Reinforcement learning

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learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement learning on Dynamical systems

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2023年第5期34卷 2513-2527页

作者： Zhou, Zhehua Oguz, Ozgur S. Leibold, Marion Buss, Martin Tech Univ Munich Chair Automat Control Engn D-80290 Munich Germany Univ Stuttgart Max Planck Inst Intelligent Syst D-70569 Stuttgart Germany

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms. However, finding a satisfying simplified system model for complex dynamical systems usually requires a considerable amount of effort. To overcome this limitation, we propose a general data-driven approach that is able to efficiently learn a low-dimensional representation of the safe region. By employing an online adaptation method, the low-dimensional representation is updated using the feedback data to obtain more accurate safety estimates. The performance of the proposed approach for identifying the low-dimensional representation of the safe region is illustrated using the example of a quadcopter. The results demonstrate a more reliable and representative low-dimensional representation of the safe region compared with previous works, which extends the applicability of the SRL framework.

关键词： Safety Computational modeling Heuristic algorithms Reinforcement learning control systems Probabilistic logic Nonlinear dynamical systems data-driven model order reduction deep learning in robotics and automation learning and adaptive systems safe reinforcement learning (SRL)

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Approximate Neural Network-based Nonlinear Model Predictive control of Soft Continuum Robots 20

Approximate Neural Network-based Nonlinear Model Predictive ...

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20th ieee/ASME International conference on Mechatronic and Embedded systems and Applications (MESA)

作者： Abdelaziz, Hend Ahmed, Abdullah El-Hussieny, Haitham Egypt Japan Univ Sci & Technol E JUST Dept Mechatron & Robot Engn Alexandria Egypt

ISBN: (纸本)9798331516246;9798331516239

This paper employs a deep learning approach to enhance the task space control of soft continuum robots. We built an approximate data-driven dynamics model of a soft robot using sampled Cartesian positions of the robot's tip and its actuator tensions. We then incorporated this surrogate model into a Model Predictive control (MPC) control scheme, enabling nonlinear control for task space trajectory tracking without relying on an exact analytical dynamics model of the robot or extensive computations. By involving constraints into the MPC, we addressed the robot's workspace and actuation limits. The numerical results of the simulation experiment show that deep learning dynamic models can improve robotic control. This leads to accurate trajectory tracking and suggests that deep learning could be used more in robot system control, especially for realtime control applications.

关键词： Soft Robots Continuum Robots Model Predictive control Deep learning

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data-driven-based Predictive Optimal for a class of Iterative learning control by Q-learning method 12

Data-driven-based Predictive Optimal for a class of Iterativ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Li, Jinze Tian, Senping Peng, Yunjian Gu, Panpan South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

ISBN: (纸本)9798350321050

The paper investigates predictive optimal for P-type iterative learning control (ILC) under unknown system's dynamic. First, with an Delta u(k+1)-based discounted linear quadratic cost function of control errors in ILC, we formulate the predictive optimal problem for P-type ILC and present a solution for such a problem in a discrete algebraic Riccati equation (DARE) with known system's dynamic, by which a predictive optimal algorithm can be established. Second, a reinforcement Q-learning method is proposed to find predictive optimal solution for P-type ILC under unknown system's dynamic, which is intended to develop a model-free and iterative learning algorithm to implement ILC with a higher convergence speed only by utilizing the input and output data of ILC. Based on the performance function, a Q-function is elaborately chosen in an iterative formulation with trials of Delta u(k+1) and control errors for critic P-type ILC performance with its updated converge rated. Third, the convergence of the algorithm is proved and discussed with it conditions. Last, an illustrative digital simulation at MATLAB verifies the effectiveness of the proposed algorithm and shows its advantages over the traditional method.

关键词： Iterative learning control Predictive optimal Reinforcement Q-learning data-driven adaptive optimal

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Event-Triggered Optimal Tracking control for Multiplayer Non-Zero-Sum Games of Nonlinear systems via Concurrent learning 12

Event-Triggered Optimal Tracking Control for Multiplayer Non...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Qin, Yi Wang, Lijie Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Peoples R China Qingdao Univ Inst Complex Sci Sch Automat Qingdao 266071 Peoples R China Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

ISBN: (纸本)9798350321050

This paper develops an event-triggered optimal tracking control algorithm to handle the problem of the N-player non-zero-sum (NZS) games for nonlinear systems with infinite horizon discount cost. A decay term is introduced to accelerate the convergence rate of the value function containing multiple control inputs. To lighten the correspondence burden, a state-dependent triggering condition is designed to ensure that the lower bound of the minimal triggering time interval is positive. Moreover, the single critic neural network is applied such that the computational complexity is greatly reduced. Unlike existing optimization results that require the continuous excitation condition, the concurrent learning technique is introduced into the update rate of neural network weights, thereby removing the limitation of additional noise excitation. Eventually, all signals of the closed-loop system are guaranteed to be uniformly ultimately bounded via the Lyapunov stability theory. Meanwhile, simulation results are presented to verify the feasibility of the proposed scheme.

关键词： Concurrent learning Event-triggered control Non-zero-sum Games Optimal Tracking control

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Research on Cooperative control of Traffic Signals based on Deep Reinforcement learning 12

Research on Cooperative Control of Traffic Signals based on ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Fan, Lingling Yang, Yusong Ji, Honghai Xiong, Shuangshuang Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China

ISBN: (纸本)9798350321050

With the increasing traffic pressure, the problem of road congestion needs to be solved urgently. In recent years, many researchers have used deep reinforcement learning to solve traffic signal control problems, but most of them are based on the situation of single intersection or adjacent intersection, with limitations. This paper proposes an index to evaluate the regional traffic performance, and uses this index to design a traffic signal coordination control algorithm based on deep Q network. The experimental results on the SUMO simulation platform show that the proposed algorithm has better performance in terms of average waiting time and average time loss compared with the other three signal control algorithms.

关键词： multi-agent deep reinforcement learning deep Q-network traffic signal control SUMO

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A Constructive Density Function Path Leading to Global Coverage Strategy for A Gaussian Random Field 12

A Constructive Density Function Path Leading to Global Cover...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yang, Yuxing Sue, Mao Fan, Huijin Liu, Lei Wang, Bo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Designing Inst Hubei Space Technol Acad Wuhan 430074 Peoples R China

ISBN: (纸本)9798350321050

The coverage of a given environment is an important issue in detection and rescue problems. This article presents a novel Lloyd algorithm for multi-agent coverage of a Gaussian random field characterized by a density function. The core issue is to construct a density function path (DFP) which ensures the distribution function of agents to converge to the one of targets iteratively. By proposing a triggering condition and a series of Gaussian distributionn functions, local optimum of traditional Lloyd algorithm can be avoided. The proposed algorithm make the coverage function reach a global solution with a small enough iteration interval. Simulation result is provided to illustrate the effectiveness of the proposed approach.

关键词： coverage control distributed optimization Gaussian random fields Lloyd algorithm

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Fault-tolerant Tracking control for Mobile Robots Based on the Framework of Intermediate Estimator and MPC 12

Fault-tolerant Tracking Control for Mobile Robots Based on t...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Ying, Liang-Huan Zhu, Jun-Wei Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350321050

During the actual motion of the wheeled mobile robot(WMR), actuator faults caused by ageing or system components' misoperation may significantly impact the real-time control performance. Therefore, this paper proposed a fault-tolerant tracking control approach based on MPC and intermediate estimator (IE) that the observer matching condition need not be satisfied. First, a set of reference trajectories is generated from the virtual system, and a nominal tracking error system is obtained based on the relative position of the actual system. Then, an IE is used to estimate the state error and actuator fault of WMR so that an estimation-based predictive model and a fault-compensated composite control law can be obtained to ensure stable control of WMR with an actuator fault. Finally, the simulation that compared to the nominal MPC showed that this fault-tolerant control algorithm has good performance in adapting to actuator faults, which verifies this algorithm's effectiveness.

关键词： WMR fault-tolerant tracking control intermediate estimator MPC

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