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Prescribed-Time Tracking control for Robotic systems with Uncertain Dynamics 12

Prescribed-Time Tracking Control for Robotic Systems with Un...

引用

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

作者： Yang, Hang Huang, Yingbo Na, Jing Chen, Qiang Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Yunnan Int Joint Lab Intelligent Control & Applic Kunming 650500 Yunnan Peoples R China Zhejiang Univ Technol Coll Informat Engn Data Driven Intelligent Syst Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350321050

This paper presents a prescribed-time tracking control scheme for robotic systems with unknown dynamics. One salient feature is that the robotic systems can achieve prescribed-time stability with the prescribed transient performance, which is different with the traditional work that achieves uniformly ultimately bounded (UUB) and/or asymptotic stability. To realize this purpose, the prescribed performance dynamics (PPD) instead of the traditional prescribed performance function (PPF) is suggested, by which both the transient and steady-state tracking performance can be guaranteed within a specified region inpriori. To avoid using the function approximators (i.e., neural networks (NNs), fuzzy logic systems (FLSs)), an approximation-free-based prescribed-time controller is established with the inertial matrix of robotic systems and the finite-time function merely, which can not only address the system unknown uncertainties but also regulate the control error to zero in a prescribed-time. Furthermore, the Lyapunov-based theoretical analysis is conducted to prove the prescribed-time stability of the closed-loop system. Finally, comparative numerical simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： Robotic systems Prescribed-time control Prescribed performance Unknown dynamics

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Chiller System Modeling using PSO Optimization based NARX approach 12

Chiller System Modeling using PSO Optimization based NARX ap...

引用

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

作者： Song, Zilong Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing 100124 Peoples R China Commun Univ China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9798350321050

The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (PSO) algorithm are used to establish a nonlinear autoregressive neural network model (PSO-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the PSO-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.

关键词： NARX Modeling chiller system data-driven PSO Algorithm

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Target Tracking and Path Planning of Mobile Sensor Based on Deep Reinforcement learning 12

Target Tracking and Path Planning of Mobile Sensor Based on ...

引用

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

作者： Zhang, Kun Hu, Yuanjiang Huang, Deqing Yin, Zijie Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China Southwest Jiaotong Univ Inst Syst Sci & Technol Chengdu 610031 Peoples R China

ISBN: (纸本)9798350321050

Path planning is a classical problem of artificial intelligence, with a wide range of applications in defense and military, road traffic, and robotics simulation. However, most of the existing path planning algorithms have the problems of a single environment, discrete action space, and manual modeling. As a machine learning method that does not require artificially providing training data to interact with the environment, the deep reinforcement learning obtained by reinforcement learning has further enhanced the ability to solve practical problems. This paper proposes to use the DDPG (Deep Deterministic Policy Gradient) algorithm on the mobile sensor to achieve path planning on the target. The DDPG algorithm combines strategies such as DQN, ActorCritic, and PolicyGrient, which introduce deep reinforcement learning to continuous action space and further enable decision-making judgments in complex continuous environments.

关键词： Mobile sensing units Reinforcement learning Target tracking and navigation DDPG

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Special Agents Policy Gradient In Value Decomposition-based Approach 12

Special Agents Policy Gradient In Value Decomposition-based ...

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

作者： Kang, Qitong Wang, Fuyong Liu, Zhongxin Chen, Zengqiang Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Brain Sci & Intelligent Rehabil Tianjin 300350 Peoples R China

ISBN: (纸本)9798350321050

In many real-world environments, such as soldiers and general in a battlefield, or teammates and goalkeeper in a soccer field, the "general" has a significantly stronger role than the "soldier", so that it is logical to assign higher "intelligence" and "flexibility" to the "general", we define it as special agent. Here, we propose a multi-agent reinforcement learning algorithm that provides stronger intelligence to special agent in a fully cooperative heterogeneous multi-agent environment. Similar to QMIX, we design a common monotonicity critic for all agents, but a separate actor network to improve its "intelligence" for the special agent. In this way we can improve the group's ability to cooperate by giving special agent greater ability, while ensuring that the group remains cooperative. We evaluate the above algorithm on two sets of StarCraft 2 micromanagement tasks, and the experimental results show that the algorithm has a significant advantage over baseline algorithms for tasks with significant heterogeneity.

关键词： Multi-agent Reinforcement learning Deep learning Policy Gradient,Value Decomposition-based

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Improved BP neural network based active disturbance rejection control for magnetic sensitivity calibration system 12

Improved BP neural network based active disturbance rejectio...

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

作者： Wang, Minlin Dong, Xueming Ren, Xuemei Changcheng Inst Metrol & Measurement Dept Inertia Beijing 100095 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9798350321050

In the magnetic sensitivity calibration system, the calibration accuracy of inertial sensor is directly related to the control accuracy of the magnetic induction intensity. Since the helmholtz coils in the calibration system have large parameter uncertainties and the magnetic field sensor has some time-delay, the traditional PID controller cannot satisfy the accuracy requirement of the magnetic induction intensity. Therefore, an improved neural network based active disturbance rejection controller (ADRC) is proposed, which utilizes the conjugate gradient algorithm and Fletcher-Reeves linear search method to adjust the parameters of ADRC for achieving the optimal control efforts. Moreover, the extended state observer of ADRC can compensate for the parameter uncertainties and time-delay exactly such that the control accuracy of the magnetic induction intensity can be largely improved. The simulations are conducted to show the effectiveness and superiority of the proposed control algorithm.

关键词： magnetic sensitivity calibration system parameter uncertainties and time-delay ADRC neural network conjugate gradient algorithm Fletcher-Reeves linear search method

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Dynamic route planning method based on deep reinforcement learning and velocity obstacle 12

Dynamic route planning method based on deep reinforcement le...

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

作者： Lou Mengmeng Yang Xiaofei Xiang Zhengrong Wang Qi Hu Jiabao Jiangsu Univ Sci & Technol Sch Automat Zhenjiang 212003 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

ISBN: (纸本)9798350321050

Route planning is a key technology for unmanned surface vessel (USV) autonomous navigation. Traditional route planning algorithm usually has the shortcomings of complex calculation, long time and single algorithm function. In this paper, aiming at the shortcomings of traditional algorithm, the route planning strategy of USV based on deep reinforcement learning (DRL) and velocity obstacle (VO) is designed. Using electronic nautical chart to build visual environment model;Based on the kinematics of USV, Markov decision process is established, and combined with the advantages of VO method and DRL, a general reward mechanism is designed, so that USV can achieve fast and safe route planning strategy in the complex marine environment where dynamic obstacles and static obstacles exist at the same time. In order to prove our method, a simulation experiment is introduced, and the results confirm the correctness and effectiveness of the proposed method.

关键词： Unmanned surface vessel Reinforcement learning Velocity obstacle Route planning

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A new deep learning-based food recognition system for mobile terminal 12

A new deep learning-based food recognition system for mobile...

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

作者： Chen, Wenze Song, Ruizhuo Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350321050

With the improvement of people's health awareness, people pay more attention to their health. In recent years, the intelligent health management system based on food recognition technology has become popular, which can help users maintain healthy eating habits. However, applying the current deep learning method in mobile phones and other terminal devices is difficult, mainly because the terminal devices have the low computing power and the network needs to perform many calculations during operation. In this paper, we have adopted the methods of parameter reconstruction and calculation graph fusion to reduce the network computing load so that it can run in real-time in terminal devices, and the detection speed on Snapdragon 778G SOC exceeds 7 FPS. Besides, experiments on the VIPER-FoodNet (VFN) dataset show that our model has a high mean average precision ( mAP) of 9.17% compared with the current advanced model.

关键词： Food recognition Deep learning Computer vision Object detection and Mobile applications

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Equivalent Circuit-based Modeling of Ultrasonic Motors for Hammerstein systems 12

Equivalent Circuit-based Modeling of Ultrasonic Motors for H...

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

作者： Qian, Yihui Huo, Xin Liu, Qingquan Liu, Jingbo Zhao, Hui Harbin Inst Technol Control & Simulat Ctr Harbin 150080 Peoples R China

ISBN: (纸本)9798350321050

In order to analyze and design an ultrasonic motor (USM) control system, an accurate and reliable mathematical model should be established. Due to the nonlinear characteristics of the motor, which are resulted from special structure and complicated working principle, traditional modeling methods are no longer applicable. Nonlinear Hammerstein model structure is proved to be a better choice for describing nonlinearities in operation after years of study and practice. The Hammerstein model of the equivalent circuit model can be established to simulate the process of modeling the actual motor since both of the two models are in a one-to-one correspondence with the motor. In this paper, following the establishment of the equivalent circuit model, a model based on this idea is established with two-phase driving voltage as its inputs. The complete modeling process is given and the method is applied to an actual ultrasonic motor, which verifies the effectiveness of the proposed method.

关键词： Ultrasonic Motor Hammerstein Model Equivalent Circuit Model Numerical Simulation

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A Model-Free H∞ control Method Based on Q-learning for Linear Discrete-time System 13

A Model-Free H∞ Control Method Based on Q-learning for Line...

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13th ieee data driven control and learning systems conference, DDCLS 2024

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

ISBN: (纸本)9798350361674

The paper delves into the H∞ 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∞ 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. © 2024 ieee.

关键词： Discrete time control systems

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High Precision Position control of Magnetic Levitation Ball System Based on Generalized Disturbance Estimation 12

High Precision Position Control of Magnetic Levitation Ball ...

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

作者： Lu, Qinkun Wane, Junxiao Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350321050

In this paper, a position control method based on generalized disturbance estimation is proposed to solve the problem of position accuracy of magnetic levitation ball system under the influence of mismatched multiple disturbances. Firstly, an Luenberger observer is designed to estimate the state variables of the system. Considering the known disturbance information, a generalized disturbance estimator is designed to estimate the disturbance using the internal model principle. Then, the disturbance estimation and its derivative are introduced into the control law design to eliminate the influence of the mismatched multiple disturbances on the position output, and the disturbance compensation gain is designed for the control law. At the same time, the reference input compensation gain is designed to solve the problem of tracking the time-varying reference position under mismatched disturbance. Then, the stability and disturbance rejection performance of the proposed method are analyzed, and it is proved that the proposed method can achieve high precision position control of the magnetic levitation ball system under mismatched multiple disturbances. In order to verify the effectiveness of the proposed method, MATLAB/Simulink is used to simulate and verify the proposed method.

关键词： Magnetic Levitation System Mismatched Multiple Disturbances Generalized Disturbance Estimation Disturbance Rejection

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