Abstract
In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n-degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints’ velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.
Highlights
Researchers in academia and industry have shown a significant deal of interest in robotic manipulators in recent years due to scientific advancements and industrial needs [1]
It is worth noting that the friction states rzη and rzη of the robotic manipulator and the estimated velocity of the joints obtained from robust exact differentiator functioned as inputs of three-layer feedforward neural network (TLFFNN)
A finite-time fast integral terminal sliding mode control (FIT-sliding mode control (SMC)) is proposed for five-DoF Autonomous Articulated Robotic Educational Platform (AUTAREP) robotic manipulators, followed by a robust exact differentiator (RED) observer and feedforward neural network (FFNN) approach
Summary
Researchers in academia and industry have shown a significant deal of interest in robotic manipulators in recent years due to scientific advancements and industrial needs [1]. The dynamic models capture physical characteristics and reactions by adding up the extra state variables To put it the static and dynamic friction models vary primarily in the predicted frictional effects, computing efficiency, and implementation complexity [20]. For dynamic friction compensation with backstepping control in [28], a robust observer for friction and a recurrent fuzzy neural network (RFNN) were designed. The adaptive sliding control (A-SC) algorithm with friction compensation for robotic manipulator established on fuzzy random vector function is VOLUME 10, 2022 described in [31]. A robust adaptive control technique based on fuzzy wavelet neural networks (FWNNs) dynamic structure is presented in [33]. The contents in the remaining article are organized as follows: Section II presents a robotic manipulator state-space model, including the LuGre friction model.
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