Adaptive trajectory tracking neural network control with robust compensator for robot manipulators. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Cuong, Pham; Nan, Wang
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 525
• Page End: 536
• Publication Date: 2016-02
• Subjects: Robot manipulator -- Neural network -- RBF network -- Sliding mode control -- Adaptive control; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1873-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for ann -link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of ann -link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.