Designing and making the intelligence assistant robot and controlling it by the fuzzy procedure

Zabbah, Iman; Foolad, Shima; Chaharaqran, Bahram; Mazlooman, Reza

doi:10.1109/icecco.2013.6718255

Cited by 1 publication

(2 citation statements)

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“…where: Based on the Ziegler-Nichols [13] tuning technique, the constants KP, Tr and Td are proposed as a function of the critical period (Pc) and the critical gain (Kc). For the controller structure shown in Equation ( 8), the PID constants are obtained from Equations ( 9)-( 11):…”

Section: Pid Controlmentioning

confidence: 99%

“…Other applications are in the area of mobile robots, as in [12], where fuzzy control rules were used to heuristically adjust the angles of the wheels and the data from an encoder used in the balance and drive of the mechanical wheel of a ball robot. Something similar was proposed in [13], where a fuzzy controller was used to control the speed and direction of an intelligent mobile robot that tracked and obtained the trajectory of a specific target. While in [14], a fuzzy adaptive PI controller for the non-linear control of the motion of a four-wheeled omnidirectional mobile robot was employed, the fuzzy adaptive algorithm adjusted the PI controller's parameters, and the fuzzy inference rules were set using the tracking error and its derivative.…”

Section: Introductionmentioning

confidence: 99%

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Proposal of a Decoupled Structure of Fuzzy-PID Controllers Applied to the Position Control in a Planar CDPR

et al. 2021

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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.

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Section: Pid Controlmentioning

confidence: 99%