Improvement of PID Controllers by Recurrent Fuzzy Neural Networks for Delta Robot

The objective of this study is to control rotational angles of a 3-degree freedom robot for tracking to the reference trajectories. That is a parallel robot, named Delta, with complex movements in shaping, processing with high efficiency, high load capacity, widely used in industry. The PID controller has been successfully developed for the Delta robot. However, when changing the robot’s parameters such as load, input coupling and friction, the PID controller is difficult to archive control criteria. This article proposes and tests a solution to improve the PID controller by combining it with a recurrent fuzzy neural network (RFNN) controller, so-called RFNN-PID controller. In the proposed solution, the PID controller plays the main role of controlling the Delta robot and the RFNN controller takes charge of a supplemental role to gain with the changes of control conditions. The RFNN-PID and PID controllers will be tested in the same conditions in MATLAB/Simulink. Simulations illustrate that the proposed controller is better than the traditional one, obtaining a response time of about 3.9 ± 0.1 (s) without steady-state error.