Model-free discrete control for robot manipulators using a fuzzy estimator. Issue 3 (29th April 2014)

• Record Type: Journal Article
• Authors: Mehdi Fateh, Mohammad; Azargoshasb, Siamak; Khorashadizadeh, Saeed
• Editors: Luc Dupré and Dr Guillaume Crevecoeur, Professor
• Is Part Of: Compel. Volume 33:Issue 3(2014)
• Page Start: 1051
• Page End: 1067
• Publication Date: 2014-04-29
• Subjects: Modelling -- Robotics -- Uncertainty estimation -- Adaptive fuzzy logic control -- Function approximation; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-05-2013-0185
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: – Discrete control of robot manipulators with uncertain model is the purpose of this paper. Design/methodology/approach: – The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm. Findings: – The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control. Originality/value: – A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.