Recalde, Luis F.
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Preferred name
Recalde, Luis F.
Main Affiliation
Ambato
Email
fernandorecalde@uti.edu.ec
ORCID
Scopus Author ID
57190424410

Research Output
Now showing 1 - 3 of 3
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Constrained Visual Servoing of Quadrotors Based on Model Predictive Control

2022 , Recalde, Luis F. , Varela Aldas, José , Guevara B.S. , Andaluz V. , Gimenez J. , Gandolfo D.

One of the main issues of visual servoing schemes occurs when the target objects leave out the field of view (FOV) of the camera, which causes failure or poor performance of the controller. Solving this problem can be a challenge due to traditional controllers cannot include system's constraints. This work presents model predictive control (MPC) for constrained image-based visual servoing (IBVS) applied in quadrotors, considering constraints in FOV and restrictions in the control actions. To handle with image constraints the MPC considers: (1) the target objects stay only in camera's FOV and this work converts these restrictions in state constraints, (2) merge image instantaneous kinematics and the dynamic of commercial quadrotors (Mavic Pro 2) in a general mathematical model in order to satisfy the bounded control actions and image constraints. Due to commercial quadrotors allow velocities like control inputs, this work considers the reduced dynamic model in general velocities space and it was identified using Dynamic Mode Decomposition with control (DMDc) algorithm. This work uses Webots to evaluate the performance of the proposed controller. Finally the controller is compared with a classical IBVS scheme in order to verify the efficacy of the proposed controller and systematically evaluate the performance considering the system constraints. © 2022 Elsevier B.V.. All rights reserved.

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Adaptive NMPC-RBF with Application to Manipulator Robots

2023 , Recalde, Luis F. , Varela J. , Guevara B.S. , Andaluz V. , Gandolfo D.

Control algorithms must deal with model uncertainties and disturbances, making them perfect for real-world applications. In addition, increased computational power in the industrial field allows the implementation of advanced control algorithms such as nonlinear model predictive control (NMPC), which is an optimal control scheme that includes system and control constraints imposed by robot dynamics and the environment. Nevertheless, modeling the robot and its environment is a complicated task due to high nonlinearities, such as model uncertainties in the form of complex unmodeled dynamics, varying payloads, and parameter mismatch, leading to fast degradation of NMPC; therefore, online adaptation laws that improve the performance even in unknown environments are needed. Due to the facts presented before, this work combines the universal approximation of RBFNN and the optimality offered by NMPC in a unified adaptive framework that guarantees good performance even under uncertainties and unmodeled dynamics. The proposed framework is tested in simulation using a 2-link planar robotic arm (SCARA Bosch SR-800), where optimization techniques were used to identify the robot's dynamics. Finally, a comparison of the proposed architecture with a baseline nominal NMPC is made with particular attention to trajectory tracking performance, demonstrating the reduction of the tracking error over non-adaptive NMPC. © 2023 IEEE.

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Quadratic Programming Optimization Applied to Robotics Systems

2023 , Recalde, Luis F. , Velasco-Sánchez E. , Guevara B.S. , Gandolfo D. , Candelas F.A.

Reactive kinematic control in velocity space is closely related to the Jacobian presented in the system. However, if the Jacobian is rank-deficient, certain task-space velocities become unachievable, leading to controller degradation. This work presents an approach to controlling robotic systems based on a quadratic programming formulation that yields solutions in just a few milliseconds. The proposed framework is tested in simulation using a two-link planar robotic arm and a mobile robot. Simulations are implemented in MuJoCo and Webots, respectively, to demonstrate the efficiency of the formulation. © 2023 IEEE.

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