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Model Predictive control of Decomposition Furnace based on Equivalent-Input-Disturbances

Model Predictive Control of Decomposition Furnace based on E...

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2024 China Automation Congress, CAC 2024

作者： Wu, WeiKang Yu, Gang He, Wangyong School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Wuhan China

ISBN: (纸本)9798350368604

In the cement production process, the decomposer outlet temperature control faces challenges such as large time delay and multiple disturbances. A general control system cannot handle time delay and disturbances effectively at the same time. Therefore, in this paper, we consider the problem of disturbances in time-delay systems and suppress the disturbances present in the outlet temperature control of the decomposer by combining equivalent-input-disturbance (EID) and Model Predictive control (MPC), where EID can estimate the disturbances and apply them to the input channels of the system, which do not require any information about the disturbances. Finally simulations are carried out in the catabolizer system and the results indicate that this system is able to suppress the perturbations effectively in time delay systems. © 2024 IEEE.

关键词： Time delay

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Auto-Regressive with Exogenous Input Modelling Based Model Predictive control of Cement Grate Cooler

Auto-Regressive with Exogenous Input Modelling Based Model P...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Tian Yu, Gang He, Wangyong Gui, Renjie Wu, Weikang School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Wuhan China

ISBN: (纸本)9798350368604

In the cement production process, the speed of grate coolers directly affects the residence time and cooling effect of cement clinker. Its effective control is of great significance to ensuring cement quality. But the adjustment of grate speed is a complex process, involving moderate cooling air, uniform clinker thickness, regulated output and other constraints. In this study, an Auto-Regressive with exogenous input (ARX) grate cooler prediction model is developed using a data-driven approach based on a company’s cement production line, a model predictive control (MPC) approach is employed for the purpose of optimising the speed of the grate cooler based on the ARX model. The results demonstrate that the method of MPC exhibits greater stability than the method of PID, and that MPC technology has good efficacy and superiority in the adjustment of grate speed. © 2024 IEEE.

关键词： Cements

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Sensorless control of submersible permanent magnet synchronous motor under long-line cable model 4

Sensorless control of submersible permanent magnet synchrono...

引用

2023 4th International Conference on Electrical Technology and Automatic control, ICETAC 2023

作者： Yue, Youjun Zheng, Lihua College of Electrical Engineering and Automation Tianjin Key Laboratory of Control Theory and Application for Complex Systems Tianjin University of Technology Tianjin300384 China

In the process of oil production, the submerged oil motor works 1-3 km downhole and is connected to the inverter by a long-wire cable, which generates overvoltage at the motor end due to the problem of matching the impedance of the motor with the impedance of the power cable. The downhole environment is not suitable for the installation of precision devices, such as position sensors. To solve these problems, a sensorless control method of submerged oil motors based on a high-frequency voltage injection method is proposed. A motor terminal voltage and current observer under a long-wire cable model is designed to estimate the actual values of motor terminal voltage and current by collecting the voltage and current at the inverter terminal. A three-level inverter is used to provide energy to the submersible oil electric pump through the long-wire cable, which can effectively suppress the motor terminal overvoltage compared to the two-level inverter. The control algorithm is verified by modeling the whole system using simulation software. © Published under licence by IOP Publishing Ltd.

关键词： Cables

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Modified Deep Deterministic Policy Gradient Based Optimal Speed Tracking Strategy for PMSMs 13

Modified Deep Deterministic Policy Gradient Based Optimal Sp...

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

作者： Fan, Zhong-Xin Tang, Lintao Li, Shihua Liu, Rongjie School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Florida State University Department of Statistics TallahasseeFL32304 United States

ISBN: (纸本)9798350361674

Achieving optimal speed regulation for permanent magnet synchronous motors (PMSMs) remains a challenging task, particularly in selecting the most suitable controller to meet desired objectives. This paper considers the optimal speed tracking problem of PMSMs. We employ a reinforcement learning algorithm to obtain optimal controller with a linear form. The reinforcement learning approach incorporates a critic-actor network structure, which approximates the value function and optimal control, respectively. Specifically, a weight updating method for the critic network is introduced, where the update of the actor network is guided by the critic network. Through data collection and neural network training, dynamic acquisition of the optimal controller gain is achieved. In addition, a novel activation function is implemented in the network, leading to improved control performance concerning dataset size and reduced training time. At final, a real-world PMSM application is presented along with comparisons to demonstrate effectiveness. The desire idea of resulting optimal controllers is not only applicable to linear systems but also capable of approximating arbitrary nonlinear optimal controllers, thus offering practical engineering versatility. © 2024 IEEE.

关键词： Reinforcement learning

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Model Prediction Based Adaptive Variable Impedance control of Manipulator with Passivity

Model Prediction Based Adaptive Variable Impedance Control o...

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第43届中国控制会议

作者： Yang Xiao Wenbo Liu Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the compliance control of rigid manipulators in human-robot interaction scenarios, a new variable impedance control(VIC) model is proposed to improve the adaptive ability of the manipulator to adjust compliance in different phases of the task. The parameter selection problem is transformed into a controller design problem. Impedance constraints are set by introducing the prior knowledge of the person, information about the state and contact force, and manipulability. The one-step model predictive control(MPC) method is used to search for the optimal solution of impedance parameters, so that the manipulator can adaptively select different balance relations between tracking accuracy and compliance. Moreover, in order to avoid the damage of time-varying impedance parameters to the stability, a tank-based method is implemented to ensure the passivity of the impedance system. Finally, the simulation results verify the effectiveness of the proposed control scheme.

关键词： Variable Impedance control(VIC) Model Predictive control(MPC) Tank-based method Passivity

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Research on Vibration Suppression for Industrial Robots Based on Dynamic Feedforward control

Research on Vibration Suppression for Industrial Robots Base...

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第43届中国控制会议

作者： Bin Huang Yahui Gan Fang Fang Bo Zhou Xianzhong Dai School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To address the challenges of low position tracking accuracy and high vibrations in mechanical arms controlled by PID closed-loop systems, this paper introduces a torque feedforward control scheme based on dynamic models. The paper initially analyzes the causes of vibrations during robot motion, establishing a mathematical model to quantify the trajectory accuracy and repeatability of vibrations. Subsequently, a torque feedforward control scheme based on dynamic models is designed, and its feasibility is theoretically analyzed. In this motion control scheme, precise dynamic models are utilized to calculate and output the desired torque. The torque values are then converted into motor current values, which are superimposed on the control output of the current loop in the servo to achieve real-time torque compensation. Finally, through simulation experiments, the paper validates that the dynamics-based torque feedforward control effectively suppresses robot vibrations, enhances tracking accuracy,and improves dynamic performance. The findings demonstrate the efficacy of the proposed control strategy in addressing the challenges associated with PID-controlled robotic arm systems.

关键词： Industrial robots feedforward control vibration suppression

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

引用

43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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Smooth and Time-optimal Improved-Bezier Trajectory Planning Based on Dynamics for Industrial Robots

Smooth and Time-optimal Improved-Bezier Trajectory Planning ...

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第43届中国控制会议

作者： Wei Cai Yahui Gan Fang Fang Bo Zhou Xianzhong Dai School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper presents a smooth and time-optimal improved-Bezier trajectory planning algorithm that addresses the avoidance of solving switching points and solves the problem of discontinuous torque on the basis of the time-optimal path parameterization(TOPP) algorithm. The proposed approach employs the TOPP-RA algorithm with a friction model, to construct the optimal velocity trajectory under the constraints of the geometric paths and joint torques. Moreover, the proposed algorithm utilizes an enhanced Bezier curve instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The modified Bezier curve optimizes the peaks of the pseudo-velocity curve in the phase plane to change the discontinuity of joint torque. The proposed algorithm ensures absolute time optimality and achieve a s mooth tr ajectory. The results of the MATLAB simulation experiment on a two-link motion system indicate the validity of the proposed algorithm.

关键词： time-optimal smooth trajectory planning improved-Bezier trajectory

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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