Robotic Manipulator Control in the Presence of Uncertainty

openThis research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications.This research focuses on the problem of manipulator control in the presence of uncertainty and aims to compare different approaches for handling uncertainty while developing robust and adaptive methods that can control the robot without explicit knowledge of uncertainty bounds. Uncertainty is a pervasive challenge in robotics, arising from various sources such as sensor noise, modeling errors, and external disturbances. Effectively addressing uncertainty is crucial for achieving accurate and reliable manipulator control. The research will explore and compare existing methods for uncertainty handling such as robust feedback linearization , sliding mode control and robust adaptive control. These methods provide mechanisms to model and compensate for uncertainty in the control system. Additionally, modified robust and adaptive control methods will be developed that can dynamically adjust control laws based on the observed states, without requiring explicit knowledge of uncertainty bounds. To evaluate the performance of the different approaches, comprehensive experiments will be conducted on a manipulator platform. Various manipulation tasks will be performed under different levels of uncertainty, and the performance of each control approach will be assessed in terms of accuracy, stability, and adaptability. Comparative analysis will be conducted to highlight the strengths and weaknesses of each method and identify the most effective approach for handling uncertainty in manipulator control. The outcomes of this research will contribute to the advancement of manipulator control by providing insights into the effectiveness of different approaches for uncertainty handling. The development of new robust and adaptive control methods will enable manipulators to operate in uncertain environments without requiring explicit knowledge of uncertainty bounds. Ultimately, this research will facilitate the deployment of more reliable and adaptive robotic systems capable of handling uncertainty and improving their performance in various real-world applications

Visual Control with Adaptive Dynamical Compensation for 3D Target Tracking by Mobile Manipulators

In this paper an image-based dynamic visual feedback control for mobile manipulators is presented to solve the target tracking problem in the 3D-workspace. The design of the whole controller is based on two cascaded subsystems: a minimum norm visual kinematic controller which complies with the 3D target tracking objective, and an adaptive controller that compensates the dynamics of the mobile manipulator. Both the kinematic controller and the adaptive controller are designed to prevent from command saturation. Robot commands are defined in terms of reference velocities. Stability and robustness are proved by using Lyapunov’s method. Finally, experimental results are presented to confirm the effectiveness of the proposed visual feedback controller.Fil: Andaluz, Víctor. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Salinas, Lucio Rafael. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Toibero, Juan Marcos. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Roberti, Flavio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin