A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System

This paper presents a radial basis function (RBF) neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC) and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network

An adaptive radical basis function (RBF) neural network PID control scheme for X-Y position table is proposed by the paper. Firstly, X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. PID neural network controller base on augmented variable method is designed. PID controller is used as assistant direction error controller, neural network parameters base on stochastic gradient algorithm can be adjust adaptive on line. The simulation results show that the presented controller has important engineering value

Radial basis function neural network control for parallel spatial robot

The derivation of motion equations of constrained spatial multibody system is an important problem of dynamics and control of parallel robots. The paper firstly presents an overview of the calculating the torque of the driving stages of the parallel robots using Kronecker product. The main content of this paper is to derive the inverse dynamics controllers based on the radial basis function (RBF) neural network control law for parallel robot manipulators. Finally,  numerical simulation of the inverse dynamics controller for a 3-RRR delta robot manipulator is presented as an illustrative example