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

Adaptive Neural Network Robust Control for Space Robot with Uncertainty

The trajectory tracking problems of a class of space robot manipulators with parameters and non-parameters uncertainty are considered. An adaptive robust control algorithm based on neural network is proposed by the paper. Neutral network is used to adaptive learn and compensate the unknown system for parameters uncertainties, the weight adaptive laws are designed by the paper, System stability base on Lyapunov theory is analysised to ensure the convergence of the algorithm. Non-parameters uncertainties are estimated and compensated by robust controller. It is proven that the designed controller can guarantee the asymptotic convergence of tracking error. The controller could guarantee good robust and the stability of closed-loop system. The simulation results show that the presented method is effective

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

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

Control of Flexible Manipulator Robots Based on Dynamic Confined Space of Velocities: Dynamic Programming Approach

Linear Parameter Varying models-based Model Predictive Control (LPV-MPC) has stood out in manipulator robots because it presents well-rejection to dynamic uncertainties in flexible joints. However, it has become too weak when the MPC's optimization problem does not include kinematic constraints-based conditions. This paper uses dynamic confined space of velocities (DCSV) to include these conditions as a recursive polytopic constraint, guaranteeing optimal dependency on a simplex scheduling parameter. To this end, the local frame's velocities and torque/force preload of joints (related to violation of kinematic constraints) are associated with different time scale dynamics such that DCSV correlates them as a polytope. So, a classical LPV-MPC will be updated using a dynamic programming approach according to the DCSV-based polytope. As a result, one lemma about DCSV-based recursive polytope and a five-step procedure for two decoupled close-loop schemes with different time scales compose the LPV-MPC proposed method. Numerical validation shows that even for relevant flexibility situations, trajectory tracking performance is improved by tuning finite horizons and optimization problem constraints regarding DCSV's behavior

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems