Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound Naveen Kumar a , Vikas Panwar b,⇑ , Jin-Hwan Borm c , Jangbom Chai c a Department of Mathematics, National Institute of Technology (NIT), Kurukshetra 136119, Haryana, India b Department of Applied Mathematics, Gautam Buddha University, Greater Noida 201308, India c Department of Mechanical Engineering, Ajou University, Suwon 443749, Republic of Korea article info Keywords: Model based controller RBF neural network Adaptive bound Reconstruction error Asymptotically stable abstract In this paper the design issues of trajectory tracking controller for robot manipulators are considered. The performance of classical model based controllers is reduced due to the presence of inherently existing uncertainties in the dynamic model of the robot manipula- tor. An intermediate approach between model based controllers and neural network based controllers is adopted to enhance the precision of trajectory tracking. The performance of the model based controller is enhanced by adding an RBF neural network and an adaptive bound part. The controller is able to learn the existing structured and unstructured uncer- tainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive bound part is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable. Finally, the numerical simulation results are produced with various controllers and the effectiveness of the proposed controller is shown in a comparative study for the case of a Microbot type robot Manipulator. Ó 2013 Elsevier Inc. All rights reserved. 1. Introduction In the past three decades, the design of trajectory controllers for robot manipulators has evolved as an active area of extensive research activities. Since the dynamics of robot manipulators are highly nonlinear in nature and involve uncertain elements such as friction, unknown payload mass and external disturbances, the researchers have investigated various con- trol schemes to achieve the precise tracking control of robot manipulators. Classically, many control schemes incorporated PID type controllers due to their simple structure, ease of use and low cost [1,2]. Designs of enhanced version of nonlinear PID controller are also reported in the literature [3,4]. However, since the dynamics of robot manipulators are highly non- linear, the performance of linear controllers is very limited. To overcome the shortcomings of the linear controllers, model based nonlinear controllers like computed torque (CT) controller are also proposed [5]. Though for trajectory control of a robot manipulator, the model based control design requires an accurate dynamic model and precise parameters of the manipulator. In real world applications, every dynamic model is subject to various uncertainties of both kinds, structured and unstructured. These uncertainties result in positioning and/or trajectory tracking errors and even cause instability of the system. The control of uncertain systems is usually accomplished using either an adaptive control scheme or a robust 0096-3003/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.amc.2013.12.082 ⇑ Corresponding author. E-mail addresses: navindma@gmail.com (N. Kumar), vikasdma@gbu.ac.in (V. Panwar). Applied Mathematics and Computation 231 (2014) 320–328 Contents lists available at ScienceDirect Applied Mathematics and Computation journal homepage: www.elsevier.com/locate/amc