Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space

Abstract

End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this article. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed-loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented.

Task space control of the robot manipulators with adaptive fuzzy global fast terminal sliding mode control in presence of dynamic and kinematic uncertainties

“…F I G U R E 20 Desired path tracking in X direction in presence of the hard challenges under AFGFTSMC (43).…”

“…In addition to showing the qualitative behavior of the proposed controllers, for more clarity, a quantitative comparison between GFTSMC (21) and AFGFTSMC (43) schemes is performed in Table 2. The definitions of the used indicators in this comparison are as follows:…”

“…In recent years, to get over some above-mentioned problems, researchers have proposed nonlinear and adaptive control techniques for task space tracking control in the presence of dynamic and kinematic uncertainties and external disturbances, which are likely in the robot manipulators and their actuators. [40][41][42][43][44][45] In References 40,41, the proposed adaptive schemes have been developed to overcome the challenges of task space tracking control in the presence of external disturbances and parametric uncertainties. To model uncertain dynamical terms or estimate the desired global task trajectory, some researcher combined their proposed algorithms with nonlinear observers or fuzzy approximators.…”

“…To model uncertain dynamical terms or estimate the desired global task trajectory, some researcher combined their proposed algorithms with nonlinear observers or fuzzy approximators. 42,43 Besides this, to achieve an accurate upper bound of existing uncertainties, some nonlinear and fuzzy-based approximators have been proposed in References 44,45. The reviewed works in this paragraph have provided proper solutions that increase system efficiency and have the following features:…”

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

“…Sainte Catherine and Lucet ( 2020 ) combined with the improved Hybrid Reciprocating Speed Obstacle (HRVO) method of tracking error estimation, and adapting the speed obstacle paradigm to agents with dynamic constraints and unreliable velocity estimation. Yilmaz et al ( 2021 ) uses the fuzzy logic network to model dynamic uncertainty, and proposes a new definition of the error-like vector containing the pseudo-inverse of the Jacobian matrix. The current method only considers the fault-tolerance of path planning and does not apply the mechanism of the cumulative error to avoid tracking drift, i.e., does not consider the impact of the motion after planning.…”