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IEEE inst.umentation and Measurement technology Conference

作者： Yan Wang Qiang Wang Jianyin Fan Department of Control Science and Engineering Harbin Institute of Technology Harbin PR China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Human biological structures and neural mechanisms have provided many great inspirations for the design and improvement of human-like robots. McKibben muscle, as a new type of flexible actuator, can be used to manufacture humanoid robots. In this paper, a target tracking system based on muscle-skeleton robotic arm and MLP-driven pressure prediction is proposed. The robotic arm, which is made of multifilament muscles, can make similar movements to human arms, and its performance is verified in the simulation environment. We use the Kinect v2 depth camera and combine the depth image and color image to calculate the spatial coordinates of the target object. Finally, we use a neural network to predict the air pressure to control the robotic with a pressure controller. The experimental results show that the robotic arm can complete the spatial target tracking tasks with an average error of 2.14cm.

关键词： Target tracking Accuracy Robot kinematics Neural networks Robot vision systems Muscles Pneumatic systems

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Convex optimization-based model predictive control for Mars ascent vehicle guidance system

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IEEE/CAA Journal of Automatica Sinica 2024年

作者： Li, Kun Guo, Yanning Ran, Guangtao Lyu, Yueyong Ma, Guangfu Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

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Car Paint Defect Detection with YOLOv5 Based on Phase Measuring Deflectometry

Car Paint Defect Detection with YOLOv5 Based on Phase Measur...

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IEEE inst.umentation and Measurement technology Conference

作者： Jiaxuan Liu She Zhao Jing Jin Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin PR China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

In the car production process, it is essential to conduct paint surface defect detection to ensure product quality. Manual inspection incurs high costs and low efficiency. Computer vision offers a superior solution. In this paper, we proposed a car paint defect detection algorithm that combines Phase Measuring Deflectometry(PMD) with YOLOv5. YOLOv5 is a state-of-the-art object detection model known for its efficiency and accuracy in real-time image processing tasks. A dataset containing 2502 absolute phase maps of paint defects was collected and annotated based on PMD. Then, we used YOLOv5 to detect defects in the absolute phase maps. To compare the detection results before and after PMD processing, we also collected a dataset using the stripe light. Then, the common indicator mAP@0.5 was used for evaluation. The results indicate a significant enhancement in paint defect detection performance with PMD, as the average mAP@0.5 increased from 0.586 to 0.829. Notably, breakage, scratch, run and condensate achieved mAP@0.5 scores of 0.923, 0.970, 0.949, and 0.922.

关键词： YOLO Phase measurement Production Real-time systems Quality assessment Automobiles Task analysis

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High-Precision control of Humanoid Muscle-skeleton Robotic Arm Using Reinforcement Learning and Large Language Models

High-Precision Control of Humanoid Muscle-skeleton Robotic A...

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Annual Conference of Industrial Electronics Society

作者： Yan Wang Qiang Wang Jianyin Fan Department of Control Science and Engineering Harbin Institute of Technology Harbin PR China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In recent years, humanoid robotics have achieved significant advancements, including those embodied by muscle-skeleton robots. Due to the nonlinear characteristics of these robots, reinforcement learning is a popular method for muscle-skeleton robot control. However, the design of the reward function and the adjustment of hyper parameters is a difficult task when using reinforcement learning. In this paper, we combine reinforcement learning with a large language model to achieve precise control of a humanoid muscle-skeleton robotic arm. We first tell the large language model task, states, and hyper parameters, and then continuously feedback the training results. We can finally get a controller that can control the robot to complete the target-tracking task. The experimental results show that the large language model can propose a suitable reward function based on experience, and adjust the hyper parameters to better training results within several questions and answers.

关键词： Training Industrial electronics Service robots Large language models Robot control Humanoid robots Reinforcement learning Oral communication Manipulators Robots

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Conditioned Super-Twisting Sliding Mode control for Speed control of PMSMs 42

Conditioned Super-Twisting Sliding Mode Control for Speed Co...

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42nd Chinese control Conference, CCC 2023

作者： Hao, Wenbo Lin, Xinpo Liu, Jin Yan, Qingyu Shen, Xiaoning Liu, Jianxing Electric Power Research Institute State Grid Heilongjiang Electric Power Company Limited Harbin China Harbin Institute of Technology Department of Control Science and Engineering Harbin China State Grid Heilongjiang Electric Power Company Limited Harbin China

ISBN: (纸本)9789887581543

In this paper, the conditioned super-twisting sliding mode algorithm is applied in PMSMs speed control. Firstly, the PMSMs' mathematic model in dq frame is given. Considering the current input saturation, the speed controller is designed based on super-twisting sliding mode algorithm, solving the problem of windup of traditional super-twisting sliding mode controller. Compared to other nonlinear anti-windup controllers, the biggest advantage of it is that it can maintain the original form of the controller when the control input saturation is inactive. Simulations are given to verify the advantages of the presented approach. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Speed control

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Optimal Pole-Transition Current Distribution for Constant Torque Maintenance and Stator Copper Loss Minimization in Variable-Pole Machines

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IEEE Transactions on Transportation Electrification 2025年

作者： Lin, Xinpo Wu, Yixuan Olson, Gustaf Falk Liu, Jianxing Wu, Ligang Peretti, Luca Department of Control Science and Engineering Harbin Institute of Technology Weihai264200 China Division of Electric Power and Energy Systems Kth Royal Institute of Technology Stockholm10044 Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

Variable phase-pole machines possess the capability to expand their operational range by employing magnetic pole change while adhering to the same electrical voltage and current limits. This paper proposes a strategy to achieve a pole change with constant torque while simultaneously optimizing current distributions for stator copper loss minimization. The overall outcome is the ability to facilitate smooth dq0 current transitions while reducing the energy consumption of variable phase-pole machine drives. Experimental results affirm the efficacy of this approach during the pole change process. © 2025 IEEE.

关键词： Electric machine control

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Attitude-trajectory Coupled control of Mars Pinpoint Landing using Nonlinear Model Predictive control 41

Attitude-trajectory Coupled Control of Mars Pinpoint Landing...

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第41届中国控制会议

作者： Qitian Chen Shaoyuan Li Yuanyuan Zou Yanning Guo Department of Automation Shanghai Jiao Tong University Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The trajectory guidance and attitude control of Mars lander is a key point in Mars pinpoint landing *** land precisely and efficiently at the target point,an attitude-trajectory coupled control methodology is designed using nonlinear model predictive control(NMPC) in this *** advantage is that it can optimize both trajectory and attitude with online *** addition,it can deal with various physical *** achieve this autonomous control algorithm,a complex dynamics model coupled with the translational and rotational motions of the lander is ***,some physical constraints such as sight-line constraint are ***,a quadratic cost function is designed to minimize the *** simulations verify the feasibility of this autonomous control *** details are presented to demonstrate the effectiveness of the proposed algorithm.

关键词： pinpoint landing nonlinear model predictive control trajectory optimization attitude-trajectory coupled control

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM 9

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

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9th International Conference on control science and Systems engineering, ICCSSE 2023

作者： Xu, Kun Zhang, Boheng Li, Jiangang Wang, Guizhong Sun, Mingjian School of Information Science and Engineering Harbin Institute of Technology Weihai China Harbin Institute of Technology Department of Control Science and Engineering Harbin China Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China

ISBN: (纸本)9798350339055

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility. © 2023 IEEE.

关键词： Sensor data fusion

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Data-driven Adaptive Observer-based Predictive control for an Inverter with Output LC Filter 48

Data-driven Adaptive Observer-based Predictive Control for a...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Xu, Xiaoyi Vazquez, Sergio Luo, Hao Garcia Franquelo, Leopoldo Zafra, Eduardo Harbin Institute of Technology Control Science and Engineering Harbin China Universidad de Sevilla Electronic Engineering Department Sevilla Spain

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The control of three-phase inverters with output LC filter is of great importance in many applications such as renewable energy, energy storage systems and uninterruptible power supplies, where the generation of sinusoidal output voltages with required amplitude, frequency and lowest harmonic distortion is desired. However, the inevitable parameter uncertainties and disturbances caused by manufacturing tolerance, aging of electrical components and load changes will degrade controller performance, which have always been challenges to traditional model predictive control methods. To deal with these challenges, a data-driven predictive control scheme is proposed in this paper. All unknown parameters and load current can be estimated online with a moving window adaptive observer. The estimation results are used to build a predictive model. Then, predictive control is applied to minimize the error between the output voltage and the reference value. Simulation results verify that the proposed scheme has a better ability to deal with parameter uncertainties and load changes than the traditional model predictive control scheme. © 2022 IEEE.

关键词： Model predictive control

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Multi-Robot Real-time Game Strategy Learning based on Deep Reinforcement Learning

Multi-Robot Real-time Game Strategy Learning based on Deep R...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Deng, Ki Li, Yanjie Lu, Songshuo Mu, Yongjin Pang, Xizheng Liu, Qi Harbin Institute of Technology Shenzhen Department of Control Science and Engineering Shenzhen China

ISBN: (纸本)9781665481090

With the booming development of artificial in-telligence, the autonomous decision-making of robots on the battlefield has become a future trend. In the field of en-tertainment games, some inst.tutions have already attained outstanding results in multi-agent decision-making tasks by using reinforcement learning algorithms. But in the field of robot confrontation game, the robot confrontation strategies are still based on the simple logic of artificial design in most cases, which hinders the further improvement of robot strategies. In this paper, a reinforcement-learning-based multi-robot control method is proposed to improve the strategy performance. We enhanced the reinforcement learning algorithm PPO with the self-play method by easy-to-implement tricks for multi-agent training and improve training efficiency. In simulated experiments based on the rules of the RoboMaster University AI Challenge, we show our method has a much higher winning percentage than traditional approaches like behavior tree. © 2022 IEEE.

关键词： Reinforcement learning

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