Fuzzy robust backstepping with estimation for the control of a robot manipulator

Abstract

In this study, a new fuzzy robust backstepping controller with estimation is proposed for the control of a robot manipulator. Backstepping control is preferred because the Lyapunov function that is used in stability analysis and the feedback control law that is used for control purpose are defined systematically during controller design. Fuzzy logic units are designed to update the gains of the backstepping controller. Then, the proposed controller is applied to a robot manipulator that is to track a trajectory in a three-dimensional space while it is subjected to external disturbances and parameter variations. The numerical results demonstrate and verify the successful performance of the proposed controller.

Adaptive Fuzzy Backstepping Control Based on Dynamic Surface Control for Uncertain Robotic Manipulator

“…Ignoring the detailed derivation based on the Lagrange equation, the dynamics equation for a rigid n-link robotic manipulator can be simply shown as [25][26]:…”

“…( )   n n M q R is an inertial matrix of the robotic manipulator, ( , )   n h q q R couples the Coriolis, centrifugal forces ( , )    n C q q q R and gravitational forces ( )  n G q R used in other studies,   n R is the generalized control torque. As authors did in [25][26], the influence of uncertainties on the manipulator system has been increasingly considered in studies. In this paper, these uncertainties are also considered, so dynamics (1) can be rewritten as:…”

Tansig type NLESO-based integral terminal sliding mode control for modular robot manipulators with uncertain disturbance: Theory and experimental verification

“…To improve the control performance of nonlinear systems, numerous control strategies have been proposed, such as model predictive control (Li et al, 2021), fuzzy backstepping algorithm (Hacioglu and Yagiz, 2019), and neural network control (Mahmoud and Elshenawy, 2016). The abovementioned control algorithms require a complex calculation process, which affect the real-time control performance and may cause instability during the strategy updating and learning process.…”

Wavelet neural network sliding mode control of two rigid joint robot manipulator

“…In the literature, several methodologies have been developed in order to increase the tracking performance, and reliability of robot manipulators. In the initial approaches, PID controller, 3 optimal control, 4 learning control, 5 robust control, 6 adaptive control, 7 backstepping control, 8 fuzzy control, 9 sliding mode control, 10 and neural network control, 11,12 have been developed. Among these controllers, the Sliding Mode Control (SMC) has proven to be very robust against uncertainties and disturbances for non-linear systems.…”