Traditional Spring-Loaded Inverted Pendulum (SLIP) model is underactuated for ignoring the foot with ankle, which limits the movability and controllability of the robot in bipedal walking. This paper proposes a new walking template called Variable Spring-Loaded Inverted Pendulum Model with Finite-sized Foot (VSLIP-FF) for biped robots. By extending the SLIP model with a finite-sized foot and a 1-DoF ankle joint for each leg and making the leg stiffness adjustable, the VSLIP-FF model can be used to realize compliant bipedal walking in complex environments. Inspired by the characteristics of human walking, an adaptive leg stretching and contracting strategy for gait planning is proposed to play the role of the ankle joint. To ensure walking stability, the Foot-Rotation Indicator (FRI) point is used as the stability criterion to prevent the robot from falling. The differential evolution algorithm is used to generate the desired center of mass (CoM) and foot trajectories of a complete walking cycle. Simulation results suggest that, compared with the SLIP model, the step length range of the VSLIP-FF model with the gait planning method is increased by 19.35%. Based on this method, the complaint walking experiment for the biped robot to step over discrete terrain is realized.
{"title":"Compliant Bipedal Walking Based on Variable Spring-Loaded Inverted Pendulum Model with Finite-sized Foot*","authors":"Sicheng Xie, Xinyu Li, Haorang Zhong, Chenghao Hu, Liang Gao","doi":"10.1109/ICARM52023.2021.9536096","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536096","url":null,"abstract":"Traditional Spring-Loaded Inverted Pendulum (SLIP) model is underactuated for ignoring the foot with ankle, which limits the movability and controllability of the robot in bipedal walking. This paper proposes a new walking template called Variable Spring-Loaded Inverted Pendulum Model with Finite-sized Foot (VSLIP-FF) for biped robots. By extending the SLIP model with a finite-sized foot and a 1-DoF ankle joint for each leg and making the leg stiffness adjustable, the VSLIP-FF model can be used to realize compliant bipedal walking in complex environments. Inspired by the characteristics of human walking, an adaptive leg stretching and contracting strategy for gait planning is proposed to play the role of the ankle joint. To ensure walking stability, the Foot-Rotation Indicator (FRI) point is used as the stability criterion to prevent the robot from falling. The differential evolution algorithm is used to generate the desired center of mass (CoM) and foot trajectories of a complete walking cycle. Simulation results suggest that, compared with the SLIP model, the step length range of the VSLIP-FF model with the gait planning method is increased by 19.35%. Based on this method, the complaint walking experiment for the biped robot to step over discrete terrain is realized.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123404875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536101
Ye Jin, Qinying Chen, Jie Qian, Jialing Liu, Jianhua Zhang
Global localization on a given map is a vital problem for robot navigation tasks. Segments-based methods most rely on the dense point clouds, and do not work well when points are sparse. It needs robots to walk a certain distance to accumulate point clouds for segments, which is not safe for robots in an unknown environment. To solve this problem, we propose a novel global localization method which only needs the first single LiDAR scan at the initial stage when the robot starts. The first single LiDAR scan is treated as a query point cloud, the extracted descriptors of this query point cloud is compared with the prior Map’s descriptors in the database which are stored in a KD tree, and the most similar frame is selected for registration. In particular, we create a voting mechanism, a two-phase search strategy for place recognition, which reduces the query time. We evaluate our method on KITTI and MVSEC datasets, and our localization accuracy is increased by 52.8% compared with SegMap validated the effectiveness of our method.
{"title":"Global Localization for Single 3D Point Cloud using Voting Mechanism","authors":"Ye Jin, Qinying Chen, Jie Qian, Jialing Liu, Jianhua Zhang","doi":"10.1109/ICARM52023.2021.9536101","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536101","url":null,"abstract":"Global localization on a given map is a vital problem for robot navigation tasks. Segments-based methods most rely on the dense point clouds, and do not work well when points are sparse. It needs robots to walk a certain distance to accumulate point clouds for segments, which is not safe for robots in an unknown environment. To solve this problem, we propose a novel global localization method which only needs the first single LiDAR scan at the initial stage when the robot starts. The first single LiDAR scan is treated as a query point cloud, the extracted descriptors of this query point cloud is compared with the prior Map’s descriptors in the database which are stored in a KD tree, and the most similar frame is selected for registration. In particular, we create a voting mechanism, a two-phase search strategy for place recognition, which reduces the query time. We evaluate our method on KITTI and MVSEC datasets, and our localization accuracy is increased by 52.8% compared with SegMap validated the effectiveness of our method.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126761244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536165
Jie Deng, Weiwei Shang, Bin Zhang, Shengchao Zhen, S. Cong
Accurate dynamic model is of crucial importance for collaborative robot to achieve a satisfying performance in model-based control or other applications. Nevertheless, it is often difficult to identify dynamic parameters accurately due to the joint elasticity and nonlinear fricitonal impact. Considering viscous friction nonlinearity and a minor Stribeck effect commonly seen in the practical situations, we present a three-loop iterative method for the dynamic model identification of collaborative robots. The inner loop is well-designed to identify the mass-inertial parameters and linear friction parameters of the robot with a least-squares approach. As for the middle loop, a suitable nonlinear viscous friction model is applied to improve the identification results. The impact of the Stribeck effect is thoroughly studied in the outer loop. Experiments on a six-dof collaborative robot have proved the identification errors can be significantly reduced by our method.
{"title":"Dynamic Model Identification of Collaborative Robots Using a Three-Loop Iterative Method","authors":"Jie Deng, Weiwei Shang, Bin Zhang, Shengchao Zhen, S. Cong","doi":"10.1109/ICARM52023.2021.9536165","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536165","url":null,"abstract":"Accurate dynamic model is of crucial importance for collaborative robot to achieve a satisfying performance in model-based control or other applications. Nevertheless, it is often difficult to identify dynamic parameters accurately due to the joint elasticity and nonlinear fricitonal impact. Considering viscous friction nonlinearity and a minor Stribeck effect commonly seen in the practical situations, we present a three-loop iterative method for the dynamic model identification of collaborative robots. The inner loop is well-designed to identify the mass-inertial parameters and linear friction parameters of the robot with a least-squares approach. As for the middle loop, a suitable nonlinear viscous friction model is applied to improve the identification results. The impact of the Stribeck effect is thoroughly studied in the outer loop. Experiments on a six-dof collaborative robot have proved the identification errors can be significantly reduced by our method.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128180011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536147
Jiacheng Cai, P. Hang, Chen Lv
To ensure safe and efficient deployment in real world, autonomous vehicles (AVs) need to deal with complex interactions. This study deduced the rudiment of a meta decision-making model for connected vehicle interactions at urban intersections based on a game-theoretic framework. In this work, one of the key components is a newly proposed set of attributes, i.e. the Egoism, Aggressiveness and Rationality, abbreviated as the EAR. It has great potential to indicate how the interaction between two vehicle agents would progress further, which enables the multi-equilibria problem to be solved in a more efficient way. Besides, the Approximate-Equivalent-Trajectory method is utilized to ensure the generalization and computational efficiency of the model. Finally, the proposed method is validated using both simulations and real-world human driving dataset. The results and analysis demonstrate the feasibility and effectiveness of the proposed algorithms.
{"title":"Game Theoretic Modeling and Decision Making for Connected Vehicle Interactions at Urban Intersections","authors":"Jiacheng Cai, P. Hang, Chen Lv","doi":"10.1109/ICARM52023.2021.9536147","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536147","url":null,"abstract":"To ensure safe and efficient deployment in real world, autonomous vehicles (AVs) need to deal with complex interactions. This study deduced the rudiment of a meta decision-making model for connected vehicle interactions at urban intersections based on a game-theoretic framework. In this work, one of the key components is a newly proposed set of attributes, i.e. the Egoism, Aggressiveness and Rationality, abbreviated as the EAR. It has great potential to indicate how the interaction between two vehicle agents would progress further, which enables the multi-equilibria problem to be solved in a more efficient way. Besides, the Approximate-Equivalent-Trajectory method is utilized to ensure the generalization and computational efficiency of the model. Finally, the proposed method is validated using both simulations and real-world human driving dataset. The results and analysis demonstrate the feasibility and effectiveness of the proposed algorithms.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116868370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536071
Yang Zhou, Yuanxu Zhang, Jian Gao, Xuman An
To solve the dynamic positioning problem of underwater vehicles for executing autonomous operation tasks, an image moments-based six degrees of freedom (DOF) visual servo control method is proposed. At first, the equations of motion of underwater vehicles are presented, and the image moments of underwater objects are introduced. Then the Jacobian matrix of image moments is derived, and the image- based visual servo control algorithm is designed, in which the feedback states are constructed by the image moments and attitude angles of the vehicle. To estimate the pitch and roll angles, a multi-layer neural network is trained to approximate the angles with image moments. The stability of the proposed visual servo control is analyzed by a Lyapunov-based method. The simulation results prove that the proposed control method has satisfactory performances for decoupled control of different DOFs with underwater targets with different shapes.
{"title":"Visual Servo Control of Underwater Vehicles Based on Image Moments","authors":"Yang Zhou, Yuanxu Zhang, Jian Gao, Xuman An","doi":"10.1109/ICARM52023.2021.9536071","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536071","url":null,"abstract":"To solve the dynamic positioning problem of underwater vehicles for executing autonomous operation tasks, an image moments-based six degrees of freedom (DOF) visual servo control method is proposed. At first, the equations of motion of underwater vehicles are presented, and the image moments of underwater objects are introduced. Then the Jacobian matrix of image moments is derived, and the image- based visual servo control algorithm is designed, in which the feedback states are constructed by the image moments and attitude angles of the vehicle. To estimate the pitch and roll angles, a multi-layer neural network is trained to approximate the angles with image moments. The stability of the proposed visual servo control is analyzed by a Lyapunov-based method. The simulation results prove that the proposed control method has satisfactory performances for decoupled control of different DOFs with underwater targets with different shapes.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/icarm52023.2021.9536061
{"title":"[ICARM 2021 Front cover]","authors":"","doi":"10.1109/icarm52023.2021.9536061","DOIUrl":"https://doi.org/10.1109/icarm52023.2021.9536061","url":null,"abstract":"","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536063
J. Zheng, Guang-liang Wang
This paper presents a multi-objective optimization of planetary reducer based on an improved multi-objective genetic algorithm (IMOGA). Minimization of volume, maximization of transmission ratio and efficiency are set as three objectives. However, owing to the difference of difficulty in solving objective functions, the optimization model of planetary reducer has the problem of uneven distribution of competitive pressure, the conventional evolutionary algorithm has poor convergence at the partial Pareto front. Thus, an improved multi-objective genetic algorithm using infeasible solution guidance and hybrid crossover operator of cytoplasm and chromosome is proposed. Experimental results of six test functions verify the effectiveness of the proposed algorithm and show that IMOGA has faster convergence speed and better convergence in comparison with NSGA-II. Ultimately, a planetary reducer optimization problem is solved by IMOGA and NSGA-II. Comparison results illustrate the competitiveness of IMOGA and prove that IMOGA can provide better solutions for designer. The Pareto set of the planetary reducer is distributed in stepped. The solutions on the same step have similar efficiency and different steps have different distribution ranges in transmission ratio and volume.
{"title":"Multi-objective Optimization of Planetary Reducer Based on an Improved Genetic Algorithm","authors":"J. Zheng, Guang-liang Wang","doi":"10.1109/ICARM52023.2021.9536063","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536063","url":null,"abstract":"This paper presents a multi-objective optimization of planetary reducer based on an improved multi-objective genetic algorithm (IMOGA). Minimization of volume, maximization of transmission ratio and efficiency are set as three objectives. However, owing to the difference of difficulty in solving objective functions, the optimization model of planetary reducer has the problem of uneven distribution of competitive pressure, the conventional evolutionary algorithm has poor convergence at the partial Pareto front. Thus, an improved multi-objective genetic algorithm using infeasible solution guidance and hybrid crossover operator of cytoplasm and chromosome is proposed. Experimental results of six test functions verify the effectiveness of the proposed algorithm and show that IMOGA has faster convergence speed and better convergence in comparison with NSGA-II. Ultimately, a planetary reducer optimization problem is solved by IMOGA and NSGA-II. Comparison results illustrate the competitiveness of IMOGA and prove that IMOGA can provide better solutions for designer. The Pareto set of the planetary reducer is distributed in stepped. The solutions on the same step have similar efficiency and different steps have different distribution ranges in transmission ratio and volume.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116888151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536090
Xishuo Zhu, Lvyan Wang, Zhangguo Yu, Xuechao Chen, Lianqiang Han
Body balance and velocity tracking are important for biped robots, especially under-actuated robots, walking on uneven ground. This paper presents a motion control strategy for the torque control of an under-actuated biped robot. In the strategy, we decompose the motion control into body balance control and velocity tracking. The body balance control is used to realize body balance by controlling the body posture and symmetrical movement of the two legs. The velocity tracking is adopted to achieve the desired velocity, where the velocity and acceleration feedback to modify the target angles of the hip joints. With the proposed method, a torque-controlled robot can walk with a balanced body and stable velocity, as validated in experiments conducted for the under-actuated bipedal robot CRANE.
{"title":"Motion Control for Underactuated Robots Adaptable to Uneven Terrain by Decomposing Body Balance and Velocity Tracking","authors":"Xishuo Zhu, Lvyan Wang, Zhangguo Yu, Xuechao Chen, Lianqiang Han","doi":"10.1109/ICARM52023.2021.9536090","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536090","url":null,"abstract":"Body balance and velocity tracking are important for biped robots, especially under-actuated robots, walking on uneven ground. This paper presents a motion control strategy for the torque control of an under-actuated biped robot. In the strategy, we decompose the motion control into body balance control and velocity tracking. The body balance control is used to realize body balance by controlling the body posture and symmetrical movement of the two legs. The velocity tracking is adopted to achieve the desired velocity, where the velocity and acceleration feedback to modify the target angles of the hip joints. With the proposed method, a torque-controlled robot can walk with a balanced body and stable velocity, as validated in experiments conducted for the under-actuated bipedal robot CRANE.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115495136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536141
Chengyu Lin, Yuxuan Tang, Yong Zhou, Kuangen Zhang, Zixuan Fan, Yang Yang, Yuquan Leng, Chenglong Fu
Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.
{"title":"Foot Gesture Recognition with Flexible High-Density Device Based on Convolutional Neural Network *","authors":"Chengyu Lin, Yuxuan Tang, Yong Zhou, Kuangen Zhang, Zixuan Fan, Yang Yang, Yuquan Leng, Chenglong Fu","doi":"10.1109/ICARM52023.2021.9536141","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536141","url":null,"abstract":"Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123226594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1109/ICARM52023.2021.9536200
Zuojun Zhu, Xiangrong Xu, Yongfei Zhu, A. Rodic, P. Petrovic
In industrial production, the robot arm often carries out repetitive operations such as moving objects, which leads to the problem of motion accuracy decline. Combining the advantages of fuzzy control and iterative learning control, this paper presents a fuzzy self-adaptive PD-type iterative learning control method. Taking the double joint manipulator as the research object and the Fuzzy control rules are written by using the Fuzzy toolbox. The fuzzy controller is used to modify PD parameters in real-time to improve the adaptability of the system. The trajectory tracking control model of the manipulator is built in Simulink. The two control strategies of PD iterative learning control and the proposed method are compared. The simulation results show that the error generated by the proposed control method is less than the former one, and the error convergence speed is faster, and the overall control effect is quite well.
{"title":"Research on Fuzzy Adaptive and PD-Type Iterative Learning Control for Robot Manipulator","authors":"Zuojun Zhu, Xiangrong Xu, Yongfei Zhu, A. Rodic, P. Petrovic","doi":"10.1109/ICARM52023.2021.9536200","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536200","url":null,"abstract":"In industrial production, the robot arm often carries out repetitive operations such as moving objects, which leads to the problem of motion accuracy decline. Combining the advantages of fuzzy control and iterative learning control, this paper presents a fuzzy self-adaptive PD-type iterative learning control method. Taking the double joint manipulator as the research object and the Fuzzy control rules are written by using the Fuzzy toolbox. The fuzzy controller is used to modify PD parameters in real-time to improve the adaptability of the system. The trajectory tracking control model of the manipulator is built in Simulink. The two control strategies of PD iterative learning control and the proposed method are compared. The simulation results show that the error generated by the proposed control method is less than the former one, and the error convergence speed is faster, and the overall control effect is quite well.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124866962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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