Robust Adaptive Control of Robotic Systems using Additive Recurrent Neural Network
نویسندگان
چکیده
In this paper, an innovative robust adaptive tracking control method for robotic systems with unknown dynamics using a nonlinearly parameterized Additive Recurrent Neural Network (ARNN) is proposed. The ARNN uses the Gaussian Radial Basis Functions (GRBF) as activation functions. Through this method the training laws of all GRBF parameters are determined. Additionally, the system is augmented with sliding control to offset the higher-order terms in the Taylor series of RBF output. Such a development is necessary for the linearization of the GRBF with respect to the parameters and, therefore, to obtain the training laws of the ARNN. The study of the total system stability is based on the Lyapunov’s theory. Finally, the effectiveness of the ARNN-based control approach is verified through simulations on a six-link robot manipulator. Key-Words: Recurrent neural networks, adaptive control, Gaussian radial basis functions, Lyapunov stability, robotic systems.
منابع مشابه
Dynamic modeling and control of a 4 DOF robotic finger using adaptive-robust and adaptive-neural controllers
In this research, first, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Denavit-Hartenberg convention and Lagrange’s formulation. To model the muscles, several springs and dampers are placed between the finger links. Then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncer...
متن کاملDesigning a Robust Control Scheme for Robotic Systems with an Adaptive Observer
This paper introduces a robust task-space control scheme for a robotic system with an adaptive observer. The proposed approach does not require the availability of the system states and an adaptive observer is developed to estimate the state variables. These estimated states are then used in the control scheme. First, the dynamic model of a robot is derived. Next, an observer-based robust contr...
متن کاملAdaptive RBF network control for robot manipulators
TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...
متن کاملAdaptive Control Based On Neural Network
Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...
متن کاملSaturated Neural Adaptive Robust Output Feedback Control of Robot Manipulators:An Experimental Comparative Study
In this study, an observer-based tracking controller is proposed and evaluatedexperimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturationin the presence of model uncertainties and external disturbances. In comparison with the state-of-the-artobserver-based controllers in the literature, this paper introduces a saturated observer-based controllerbas...
متن کامل