Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Abstract

This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.