Article
Automation & Control Systems
Kaixin Lu, Shuaishuai Han, Jun Yang, Haoyong Yu
Summary: Compliant actuators have great advantages in safe robot control, but achieving optimal trajectory tracking control remains a challenge. This study proposes an inverse optimal adaptive neural control scheme, which uses a tuning functions-based adaptive learning mechanism to improve control efficiency and simplify implementation in practical engineering systems.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Mathematics, Applied
Lina Rojas-Garcia, Marco Mendoza, Isela Bonilla, Cesar Chavez-Olivares
Summary: This paper presents an explicit force control structure for precise regulation of robot-environment interaction, addressing issues such as safe operation, compensation of parametric uncertainty, and bounded response. The proposed control scheme is based on generalized saturation functions and is supported by stability analysis using Lyapunov's theory. A numerical simulation is conducted to validate its correct performance.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Gaorong Lin, Jinpeng Yu, Jiapeng Liu
Summary: An adaptive fuzzy impedance control method is proposed in this paper to improve the performance and safety of robots by approximating system uncertainties, while utilizing finite-time control and command filtered techniques to optimize the interaction performance of the system and handle computational complexity. Simulations are conducted to demonstrate the effectiveness of the proposed control method in physical human-robot interaction.
Article
Physics, Multidisciplinary
Shihao Wang, Shiqi Zheng, Yushu Deng, Zhouxiang Jiang, Bao Song, Xiaoqi Tang
Summary: This paper focuses on the study of logic-based switching adaptive control and explores two different cases. Firstly, a new logic-based switching adaptive control method is proposed to achieve finite time stability for a class of nonlinear systems, which contain both fully unknown nonlinearities and unknown control direction. Secondly, a sampled-data logic-based switching mechanism is proposed for a class of nonlinear systems with an uncertain linear growth rate. Control parameters and sampling time can be adaptively adjusted to ensure the exponential stability of the closed loop system. The proposed results are verified through applications in robot manipulators.
Article
Automation & Control Systems
Bin Hu, Zhi-Hong Guan, Frank L. Lewis, C. L. Philip Chen
Summary: This paper develops a neural network adaptive control framework for addressing control difficulties in cooperative robotic systems. By modeling the cooperative robotic systems with Markovian switching networks, the article shows that the position and velocity tracking errors are practically uniformly exponentially stable in the mean-square sense, ensuring second-order practical tracking.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Mathematics
Rui Li, Liang Yang, Yong Chen, Guanyu Lai
Summary: This paper presents a new adaptive sliding mode controller for robot manipulators with unknown disturbances and system failures, achieving asymptotic tracking and avoiding singularity problems.
Article
Robotics
Gao Wang, Zhuo Wang, Bo Huang, Yahui Gan, Feiyan Min
Summary: To improve the accuracy of torque estimation and compliance control without force sensors, a torque fusion method based on extended Kalman filter (EKF) is proposed, considering both motor current data and harmonic reducer torsional deformation. Firstly, a nonlinear EKF is designed based on the motor side dynamic model and joint deformation nonlinear model. Experimental results show that the method overcomes interference from the environment and nonlinear system factors. Then, an active compliance controller with a nested loop framework is designed based on the fused torque. The proposed torque estimation scheme reduces the root mean square error (RMSE) to 0.1278N·m and the maximum error of joint estimated torque to 0.34N·m, while the force tracking accuracy of the compliant controller reaches 3.6N and can be extended to more redundant joints.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Gianluca Garofalo, Xuwei Wu, Christian Ott
Summary: This letter presents a solution to the problem of multi-task control by considering both task priorities and the importance of adaptive control. By using hierarchical multi-task impedance control for trajectory tracking and geometric methods for model identification, the controller tendency towards enforcing strict task priorities as the parameters tend to their real values is addressed. Experiments are conducted to validate the stability analysis.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Lei Qiao, Min Zhao, Chao Wu, Tong Ge, Rui Fan, Weidong Zhang
Summary: This article introduces two novel adaptive PID controllers for trajectory tracking of robotic manipulators with robustness against uncertainties and adaptiveness to unknown parameters. The controllers guarantee eventual asymptotic convergence of tracking errors and offer better robustness compared to existing controllers. Simulation studies and comparisons demonstrate the superiority of the proposed controllers.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Interdisciplinary Applications
Kelly Merckaert, Bryan Convens, Chi-ju Wu, Alessandro Roncone, Marco M. Nicotra, Bram Vanderborght
Summary: This paper introduces a computationally efficient control scheme for safe human-robot interaction, relying on the Explicit Reference Governor (ERG) formalism to enforce input and state constraints in real-time. The proposed solution has been theoretically supported and experimentally validated on the Franka Emika Panda robotic manipulator.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Automation & Control Systems
ThangLong Mai, HuuToan Tran
Summary: An approach with position control scheme is proposed for a mobile manipulator robot, using adaptive strategies and backstepping control technique. The improved backstepping controller solves the fixed problem of main control parameters through adaptive self-updating process. The control system performances are guaranteed by designing all adaptive updating algorithms based on the Lyapunov theorem.
Article
Automation & Control Systems
JunMin Park, Wookyong Kwon, PooGyeon Park
Summary: This article proposes a new adaptive sliding mode control (ASMC) scheme for robot manipulators. The scheme compensates the time-delay estimation (TDE) error induced by time-delay control (TDC) and ensures the positive condition of the adaptive parameter. The stability analysis shows that the sliding variable for the manipulators controlled by the proposed ASMC scheme is uniformly ultimately bounded. Simulation and experimental results demonstrate the effectiveness of the proposed control schemes.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Guanyu Lai, Haojie Xie, Aoqi Liu, Liang Yang, Meng Chen, Hanzhen Xiao, Zhi Liu
Summary: An adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators in which an uncalibrated camera and the dynamics behavior of the manipulator are considered. A robust adaptive estimation approach is developed to address the uncertainty in camera parameters and the nonlinearity in robot dynamics, achieving boundedness of closed-loop signals and asymptotic convergence of image error to zero. Comparative simulation tests confirm the obtained results.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Mechanical
Lina Rojas-Garcia, Isela Bonilla-Gutierrez, Marco Mendoza-Gutierrez, Cesar Chavez-Olivares
Summary: This paper presents a force/position control structure with two characteristics for robot-environment interaction tasks. The structure ensures safe operation of the robot within a specified region and compensates for parameter uncertainties.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Chao Zeng, Shuang Li, Zhaopeng Chen, Chenguang Yang, Fuchun Sun, Jianwei Zhang
Summary: In this work, a human-in-the-loop learning-control approach is proposed for acquiring compliant grasping and manipulation skills of a multifinger robot hand. The approach uses a markerless vision-based teleoperation system for task demonstration and trains a neural network model to map the pose of the human hand to the joint angles of the robot hand in real-time. An adaptive force control strategy is designed to predict the desired force control commands based on the pose difference between the robot hand and the human hand. The approach has been verified in both simulation and real-world scenarios, showing more reliable performances than the current widely used position control mode for compliant grasping and manipulation behaviors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Filipe Rocha, Gabriel Garcia, Raphael F. S. Pereira, Henrique D. Faria, Thales H. Silva, Ricardo H. R. Andrade, Evelyn S. Barbosa, Andre Almeida, Emanuel Cruz, Wagner Andrade, Wenderson G. Serrantola, Luiz Moura, Hector Azpurua, Andre Franca, Gustavo Pessin, Gustavo M. Freitas, Ramon R. Costa, Fernando Lizarralde
Summary: This paper introduces a novel procedure using ground robot ROSI to inspect conveyor structures, designed for long-term operations in harsh outdoor environments. The robot's mechanical design and control strategies enable it to effectively perform the required inspection tasks with improved efficiency.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2021)
Article
Automation & Control Systems
Marcus O. Couto, Arthur G. Rodrigues, Fernando Coutinho, Ramon R. Costa, Antonio C. Leite, Fernando Lizarralde, Joao C. Payao Filho
Summary: This study presents a method for online extraction of geometry characteristics of deposited beads in WAAM using monocular cameras. The method involves measurement and feature extraction using various image processing algorithms, including segmentation and edge detection. Experimental results demonstrate the performance and effectiveness of the proposed method.
JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS
(2022)
Article
Robotics
Nicolas Lizarralde, Fernando Coutinho, Fernando Lizarralde
Summary: This paper investigates the trajectory tracking control for wire arc additive manufacturing (WAAM). A task-priority based kinematic control scheme is proposed to coordinate the manipulator and the positioning table, achieving trajectory following and orientation control for the welding torch. Lyapunov stability analysis is conducted to account for the unmodeled dynamics in the kinematic control loop. Experimental results demonstrate the effectiveness of the proposed method on a WAAM robotic system.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Jonathan Fried, Antonio Candea Leite, Fernando Lizarralde
Summary: This work presents an adaptive image-based visual servoing approach for uncertain robot manipulators using an eye-to-hand camera configuration, which does not require any calibration procedure and image velocity measurement. By combining the indirect adaptive visual servoing approach with a direct adaptive motion controller, the uncertain robot dynamics are considered, leading to a stable robot vision system.
CONTROL ENGINEERING PRACTICE
(2023)
Proceedings Paper
Automation & Control Systems
Henrique D. Faria, Fernando Lizarralde, Ramon R. Costa, Ricardo H. R. Andrade, Thales H. Silva, Raphael F. S. Pereira, Evelyn S. Barbosa, Filipe Rocha, Andre Franca, Gustavo M. Freitas, Gustavo Pessin
Proceedings Paper
Automation & Control Systems
Antonio C. Leite, Francisco L. Cruz, Fernando Lizarralde
Proceedings Paper
Automation & Control Systems
Jonathan Fried, Fernando Lizarralde, Antonio C. Leite
Proceedings Paper
Automation & Control Systems
Jhomolos G. Alves, Fernando Lizarralde, Joao C. Monteiro
Proceedings Paper
Automation & Control Systems
Rodolpho Ribeiro, Liu Hsu, Ramon Costa, Fernando Lizarralde
2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
(2019)
Proceedings Paper
Automation & Control Systems
Danilo Cunha, Fernando Lizarralde
2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
(2019)
Proceedings Paper
Automation & Control Systems
Timon Asch Keijock, Eduardo V. L. Nunes, Liu Hsu
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Paulo Padrao, Liu Hsu, Michael Vilzmann, Konstantin Kondak
2019 19TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Gabriel Garcia, Filipe Rocha, Marcos Torre, Wenderson Serrantola, Fernando Lizarralde, Andre Franca, Gustavo Pessin, Gustavo Freitas
2019 19TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Andre Coelho, Christian Ott, Harsimran Singh, Fernando Lizarralde, Konstantin Kondak
2019 19TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR)
(2019)
