Model Reference-Based Neural Controller for Transmission Line Inspection Robot

Abstract

The regular inspection of the power transmission lines is essential for the uninterrupted transmission of electrical energy to demand points. This quickly requires actions with economically, efficiently, and safely. Therefore, the transmission line inspection robots are inevitable solution as an alternative to existing line inspection methods. This study present design and control of a transmission line inspection robot (I-Robot). Since the I-Robot exhibits nonlinear behavior and has multiple inputs and multiple outputs, a model reference-based neural controller is determined to achieve nonlinear control. The robot design process consists of four stages which are kinematic modelling, dynamic modelling, actuator modelling and controller design. To meet inspection requirements, the conceptual design of the I-Robot is performed, and the kinematic model are calculated in terms of the transformation matrices. According to the design requirements and system constraints, the dynamic model of the I-Robot is created. To provide desired motions and trajectory tracking, the actuator models are determined. Then, the I-Robot is prototyped. According to the dynamics of joint, robot and constraints, the system identification is performed to create reference model. During the system identification, the logged data are used the train the reference model. Finally, the desired trajectory for the driving cycles is created by manual excitation of the I-Robot. During the manual excitation, the logged data are used to train the neural network (NN)-based controller. Eventually, the I-Robot is assessed under the test scenarios in term of the trajectory tracking performance as regression value and mean squared errors. According to the experiments, the neuron numbers and the training algorithm of the NN controller are determined. It was observed that the controller is quickly optimized with the adapting algorithm designed for the NN reference model. As a result, the performance of the model reference-based neural controller was determined as 99%.