The design of robot systems controlled by cables can be relatively difficult when it is approached from the mathematical model of the mechanism, considering that its approach involves non-linearities associated with different components, such as cables and pulleys. In this work, a simple and practical decoupled control structure proposal that requires practically no mathematical analysis was developed for the position control of a planar cable-driven parallel robot (CDPR). This structure was implemented using non-linear fuzzy PID and classic PID controllers, allowing performance comparisons to be established. For the development of this research, first the structure of the control system was proposed, based on an analysis of the cables involved in the movement of the end-effector (EE) of the robot when they act independently for each axis. Then a tuning of rules was carried out for fuzzy PID controllers, and Ziegler–Nichols tuning was applied to classic PID controllers. Finally, simulations were performed in MATLAB with the Simulink and Simscape tools. The results obtained allowed us to observe the effectiveness of the proposed structure, with noticeably better performance obtained from the fuzzy PID controllers.
Numerous academic works have addressed the identification and control problem for complex dynamic systems. In recent decades, the use of control algorithms based on neural networks (NNs) has been highlighted, which have shown satisfactory results in the trajectory tracking control for a class of discretetime nonlinear systems. The present work proposes an efficient learning law for discrete-time recurrent high order neural networks (RHONNs), using a training algorithm based on an unscented Kalman filter (UKF). This learning law is applied through a decentralized neural block control using UKF (DNBC-UKF), for trajectory tracking control of a class of discrete-time nonlinear systems. The proposed controller is experimentally evaluated by means of real-time tests in a two degrees of freedom (DOF) vertical directdrive robotic manipulator against a decentralized neural block control using EKF (DNBC-EKF). The Lyapunov stability theory is used to show that the identification errors of RHONNs are semi-globally uniformly ultimately bounded (SGUUB), the RHONN weights remains bounded, and the closed-loop tracking errors go to zero.
Currently, a large number of investigations are being carried out in the area of robotics focused on proposing solutions in the field of health, and many of them have directed their efforts on issues related to the health emergency due to COVID-19. Considering that one of the ways to reduce the risk of contagion is by avoiding contact and closeness between people when exchanging supplies such as food, medicine, clothing, etc., this work proposes the use of a planar cable-driven parallel robot for the transport of supplies in hospitals whose room distribution has planar architecture. The robot acts in accordance with a procedure proposed for each task to be carried out, which includes the process of disinfection (based on Ultraviolet-C light) of the supplies transported inside the robot’s end effector. The study presents a design proposal for the geometry of the planar cable-driven parallel robots and its end effector, as well as the software simulations that allow evaluating the robot’s movement trajectories and the responses of the position control system based on Fuzzy-PID controllers.
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