Neural network force control for industrial robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-MI industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments an carried out for the dual-robot system handling an object

An Intelligent Position-Tracking Controller for Constrained Robotic Manipulators Using Advanced Neural Networks

Nowadays, robots have become a key labor force in industrial manufacturing, exploring missions as well as high-tech service activities. Possessing intelligent robots for such the work is an understandable reason. Adoptions of neural networks for excellent control accuracies of robotic control systems that are restricted in physical constraints are practical challenges. This chapter presents an intelligent control method for position tracking control problems of robotic manipulators with output constraints. The constrained control objectives are transformed to be free variables. A simple yet effective driving control rule is then designed to force the new control objective to a vicinity around zeros. To suppress unexpected systematic dynamics for outstanding control performances, a new neural network is employed with a fast-learning law. A nonlinear disturbance observer is then used to estimate the neural estimation error to result in an asymptotic control outcome. Robustness of the closed loop system is guaranteed by the Lyapunov theory. Effectiveness and feasibility of the advanced control method are validated by comparative simulation

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models