Artificial Neural Network Based Prediction Model of the Sliding Mode Control in Coordinating Two Robot Manipulators

Abstract

Abstract. The design of a decentralized controlling law in the coordinated transportation area of an object by multiple robot manipulators employing implicit communication between them is a specific alternative in synchronization problems. A decentralized controller is presented in this work which is combination of the sliding mode control and artificial neural network which guarantees robustness in the system. Implicit communication among robot manipulators considers the light weight beam angle in this controller. A multi layer feed forward neural network based prediction model is presented not only to improve trajectory tracking of multiple robots but also to solve the chattering phenomena in the sliding mode control. The simulation results show the effectiveness of the proposed controller on two cooperative PUMA 560 robot manipulators.

A Compound Controller of an Aerial Manipulator Based on Maxout Fuzzy Neural Network

“…And m and p are weight coefficients. According to fuzzy learning rules in Table 1, for the error signal e, the corresponding control signals u 1 , u 2 , u 3 , u 4 of the four motors adopt the logic method of the following table [18]. Values 1 to 5 indicate that the control signal changes from weak to strong as shown in Table 1.…”