Using artificial neural network for forward kinematic problem of under-constrained cable robots

Abstract

Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot's kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot's geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot.

Self-Learning of Delta Robot Using Inverse Kinematics and Artificial Neural Networks

“…Some methods are numerical algorithm, geometrical method, genetic algorithm (GA), reduced method, polynomial method, fuzzy algorithm, fuzzy PID [4], Feed Forward Neural Network, Fuzzy Neural Network, Particle Swarm Optimization (PSO), etc. [5].…”

“…NN can be used to control validations of the IK model. IK model is created based on the robot's geometry and dynamic to solve the forward kinematics problem [4] [5].…”