Neural network

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 2, MARCH 2002

343

Neural Networks for Advanced Control of Robot Manipulators
H. Daniel Patiño, Member, IEEE, Ricardo Carelli, Senior Member, IEEE, and Benjamín R. Kuchen, Member, IEEE
Abstract—This paper presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performancerobot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs (bank of fixed neural networks), and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in thebank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model (or network model) to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we provethat the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zeroconvergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given. Index Terms—Adaptive control, feedforward neural nets, neurocontrollers, nonlinear systems, robot manipulators, stability.

I. INTRODUCTION N recent years, much attention has been paid to neural-network (NN)-based controllers. The nonlinear mapping andlearning properties of NNs are key factors for their use in the control field. These types of controllers take advantage of the capability of an NN for learning nonlinear functions and of the massive parallel computation, required in the implementation of advanced control algorithms. This learning capability of NNs is used to make the controller learn a certain function, highly nonlinear,representing the direct dynamics, inverse dynamics or any other characteristics of the system. This is usually performed during a normally long training period when commissioning the controller in a supervised or unsupervised manner [30]. If the learning capability of the NN is not switched off after the training phase, once the controller is commissioned, the NN-based controller works as an adaptivecontroller.
Manuscript received December 7, 1999; revised January 10, 2001. This work was supported in part by ANPCyT (National Agency for Promotion of Science and Technology), CONICET (National Research Council), and Universidad Nacional de San Juan, Argentina. The authors are with the Instituto de Automática, Facultad de Ingneniería, Universidad Nacional de San Juan, 5400 San Juan, Argentina (e-mail:dpatino@inaut.edu.ar). Publisher Item Identifier S 1045-9227(02)01809-X.

I

This paper deals with a neural network-based controller for motion dynamic control of robot manipulators. The dynamical behavior of a rigid manipulator can be characterized by a system of highly coupled and nonlinear differential equations. The nonlinear effects are emphasized for robots working at high speeds withdirect drive motors or low ratio gear transmissions. Advanced control strategies [4], [36], and [38], generally based on an exact cancellation of the nonlinear dynamics have to be used for these types of robots. The uncertainties on the robot dynamic parameters, such as inertia and payload conditions, have motivated the design of adaptive controllers [17], [25], [37]. This type of controller is...