Uncertainty Analysis of a 2-DOF Planar Parallel Robot by Means of Fuzzy Dynamic Approach

Lara-Molina, Fabian Andres; Dumur, Didier; Koroishi, Edson Hideki

doi:10.1109/lars-sbr.2016.53

Cited by 4 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these robotic manipulators some joints may be passive, thus resulting into an underactuated system (Yu, 2006; Bourbonnais et al, 2015). The related control problem is non-trivial, and several approaches require the transformation of the robot’s dynamic model into a form where the computation of the stabilizing control inputs is finally enabled (Le et al, 2013a; Lara-Molina et al, 2016; Lara-Molina et al, 2014; Le et al, 2011, 2013b). Indicative results on model-based and mode-free control of parallel robotic manipulators can be found in Harandi et al (2021), Xie et al (2021), Morlock et al (2021), Huang et al (2020), Jiang et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for a five-link parallel robotic manipulator

Rigatos

Abbaszadeh

2022

Journal of Vibration and Control

View full text Add to dashboard Cite

Control and stabilization of parallel robotic manipulators is a non-trivial problem because of nonlinearities and the multi-variable structure. In this article, a nonlinear optimal control approach is proposed for the dynamic model of such robotic systems, using as a case-study the model of a five-link parallel robot. The dynamic model of the parallel robotic manipulator undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands as a solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control without the need to measure the entire state vector of the parallel robotic manipulator, the H-infinity Kalman filter is used as a robust state estimator. Among the advantages of this control method, one can note that (i) unlike the popular computed-torque method for robotic manipulators, the new control approach is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference set points under minimal energy consumption by the robot’s actuators.

show abstract