Control
Zahra Hassani; Vahab Nekoukar
Abstract
Background and Objectives: Currently, the control engineering community is increasingly focusing on research related to Unmanned Aerial Vehicles (UAVs) due to their versatile capabilities. Among the various applications, target detection and tracking stand out as crucial. Recent advancements in Artificial ...
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Background and Objectives: Currently, the control engineering community is increasingly focusing on research related to Unmanned Aerial Vehicles (UAVs) due to their versatile capabilities. Among the various applications, target detection and tracking stand out as crucial. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have the potential to enhance the synergy between vision and control in UAV operations. By integrating AI algorithms with control methods, the accuracy of target information can be significantly improved in UAVs. This research introduces an autopilot system for quadcopters to search for and track a predetermined target.Methods: The autopilot system utilizes the YOLO network, a robust convolutional neural network-based system, for real-time target detection. To enhance object tracking robustness, the Kalman filter is integrated into the system. Furthermore, Proportional-Derivative (PD) controllers are utilized to calculate suitable control commands, enabling the quadcopter to effectively track both stationary and moving targets. Additionally, an object retrieval strategy is proposed to locate and recover lost objects during the tracking phase. Results: The effectiveness of the proposed system was evaluated through real-time experimental trials involving diverse scenarios encompassing both stationary and moving targets. The integration of the YOLOv5 network with the Kalman filter substantially improved detection accuracy and stability. Furthermore, the object retrieval mechanism demonstrated high reliability in recovering lost targets, thereby increasing overall system resilience. The PD-based control scheme enabled responsive and precise trajectory adjustments, contributing to consistent target tracking performance across all test cases.Conclusion: Integration of a YOLOv5-based detection module, a Kalman filter for robust tracking, and PD controllers for flight control provides an autonomous quadcopter system capable of detecting and tracking both stationary and moving targets with unknown dynamics. The proposed approach shows promise for real-time autonomous tracking applications and offers a foundation for future development in more complex, outdoor scenarios.
Control
H. Chenarani; M.M. Fateh
Abstract
Background and Objectives: This paper presents a robust passivity-based voltage controller (PBVC) for robot manipulators with n degree of freedom in the presence of model uncertainties and external disturbance.Methods: The controller design procedure is divided into two steps. First, a model-based controller ...
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Background and Objectives: This paper presents a robust passivity-based voltage controller (PBVC) for robot manipulators with n degree of freedom in the presence of model uncertainties and external disturbance.Methods: The controller design procedure is divided into two steps. First, a model-based controller is designed based on the PBC scheme. An output feedback law is suggested to ensure the asymptotic stability of the closed-loop error dynamics. Second, a regressor-free adaptation law is obtained to estimate the variations of the model uncertainties and external disturbance. The proposed control law is provided in two different orders.Results: The suggested controller inherits both advantages of the passivitybased control (PBC) scheme and voltage control strategy (VCS). Since the proposed control approach only uses the electrical model of the actuators, the obtained control law is simple and also has an independent-joint structure. Moreover, the proposed PBVC overcomes the drawbacks of torque control strategy such as the complexity of manipulator dynamics, practical problems and ignoring the role of actuators. Moreover, computer simulations are carried out for both tracking and regulation purposes. In addition, the proposed controller is compared with a passivity-based torque controller where the simulation results show the appropriate efficiency of the proposed approach.Conclusion: The robust PBVC is proposed for EDRM in presence of external disturbance. To the best of our knowledge, it is the first time that a regressorfree adaptation law is obtained to approximate the lumped uncertainties according to the passivity-based VCS. Moreover, the electrical model of the actuators is utilized in a decentralized form to control each joint separately.
