Developing a hybrid of tunable proportional integral derivative (tpid) and neural network (nn) controllers for vibration suppression of flexible robot arm manipulators

The main objective of designing industrial robots is to carry out tasks that are oftentimes routine in nature. The designs are achieved by ensuring the manipulators to move the body, arm and wrist through a serious of motions. To ensure these motions, controllers are also designed to enhance stability and flexibility. Conventional proportional integral derivatives (PID) controllers which are most popular are not particularly efficient in providing this control operation. This work presents a hybrid of tunable PID and neural network (NN) controllers for vibration suppression of flexible robot arm manipulators. Mathematical models were developed to design the proposed hybrid. A model of the hybrid system was developed. Thereafter, different control parameters including joints and rotational angles, electrical and mechanical plant components and forearm transposition model were used to design the body, arm and wrist components of the robot manipulator. The hybrid was simulated and results show that the proposed model produced a better controller that the conventional PID or NN controller used alone for a robot arm control.