A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

Abstract

A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

“…Some researchers have tried to employ these adaptive networks in sliding mode controllers. In [31], a radial basis function neural network based sliding mode control (RBFN-SMC) scheme is considered for deburring tasks by using 6-DOF industry robotic systems. The adaptive tuning algorithms of RBFN-SMC are derived from the Koski function algorithm and are shown to have the ability to learn uncertain control actions.…”

Adaptive Sliding Mode Control of Functional Electrical Stimulation (FES) for Tracking Knee Joint Movement

“…Because of the network is hard to decided, it will constrain the training effects of neural networks if the parameter value is not appropriate [11]. In this paper, an adaptive RBF neural network based sliding mode control method is designed, then use genetic algorithm to optimize the network parameters, the nonlinear RBF is used to approximate the model uncertainties and disturbances, which has fast computation time in real-time implementation.…”

New Adaptive Neural Network for Uncertain Nonlinear System with Disturbance

“…Four trends will appear, the first trend consists in introducing a direct adaptive fuzzy control combined with sliding mode control [5] and [6] based on Lyapunov theory. The second trend is carried out in controller design based on universal approximation [7], [8] and [9], the indirect adaptive fuzzy control (IAFC) combined with sliding mode control (SMC) has attracted much attention. The combining the two achieving more superior performances such as overcoming some limitations of the traditional SMC.…”

“…The lumped disturbance signal is estimated based on a RBF neural network combined with a feed-forward correction term. In addition, the parameters of controllers in [5], [9] and [15] are chosen such as they are coefficients of hurwitz polynomial that mean these controllers ensure only the stable closed loop system. On the other hand, these papers do not focus on the performance of closed loop system such as: the settling time, overshoot and error the output and reference signal.…”