Abstract
ABSTRACTConventional Neural Network (NN) control for robots uses radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed within a very short duration of time. This consumes a large amount of computational memory and may subsequently result in system failure. To avoid this problem, this paper proposes an innovative NN robot control using a dimension compressed RBF (DCRBF) for a class of n-degree of freedom (DOF) robot with full-state constraints. The proposed DCRBF NN control scheme can compress the nodes and weighting matrix greatly and provide an output that meets the prescribed tracking performance. Additionally, adaption laws are designed to compensate for the internal and external uncertainties. Finally, the effectiveness of the proposed method has been verified by simulations. The results indicate that the proposed method, integral Barrier Lyapunov Functions (iBLF), avoids the existing defects of Barrier Lyapunov Functions (BLF) and prevents the constraint violations.
Highlights
dimension compressed RBF (DCRBF) Inspired by expression format of conventional radial basis function (RBF) Neural Network (NN) in (27), a dimension-split RBF NN of n − DOF is built in Figure2, which only has nm4 dots and nm4 weights for each degree of freedom of the outputs, which avoids the exponential growth with the DOF
We know that the prescribed trajectory tracking performance of proposed DCRBF with the implementation of integral Barrier Lyapunov Functions (iBLF) is satisfactory from Figure 4(a) and Figure 4(b)
This paper presents an innovative adaptive neural network control using DCRBF for n-DOF robot system with full-state constraints and unknown dynamics
Summary
With the widely use of complex robot manipulators which are nonlinear systems in our modern society and industry, research into robot technologies has attracted enormous attention Alford and Belyeu (1984); Cheng, Hou, Tan, and Zhang (2012); Gueaieb, Karray, and Al-Sharhan (2007); G.-W. Lee and Cheng (1996); T. Li, Duan, Liu, Wang, and Huang (2016); Na, Mahyuddin, Herrmann, Ren, and Barber (2015); Namvar and Aghili (2005)Alford and Belyeu (1984); Cheng et al (2012); Gueaieb et al (2007); G.-W. Lee and Cheng (1996); T. Li et al (2016); Na et al (2015); Namvar and Aghili (2005). The tracking control problem is studied in (He et al, 2016) for an uncertain n-link robot with full-state constraints and a BLF is designed to guarantee the uniform ultimate boundedness of the closed-loop system. It inherently takes care of the inevitable uncertainties in the dynamics of the robot. In order to avoid the violation of constraints while using BLF on n-link robots, a novel iBLF is utilized to design the control strategy which incorporates the output constraints and provides an enhanced system stability.
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