Non-linear System Identification and Control for Autonomous Robot Using Artificial Neural Network
- Post by: airjournal
- February 15, 2022
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The problem of a robot not identifying and accomplishing a particular job assigned to it at a shorter speed and time has reduced the production capacity of some industries that solely depend on robots for their daily production. With this development, the industries do not meet up with their customers’ demands thereby making them lose some of their customers. This will equally reduce the financial strength of the industries, as a result of losing some of their customers thereby making them not meet up with paying their staff. If the problem of robot not being able to accomplish a particular job assigned to it at a shorter speed and time is resolved by non-linear system identification and control for an autonomous robot using an artificial neural network, there will be an increase in the production capacity of the industries that have assigned some of their industrial production responsibilities to robots. In this research, a case study of the use of autonomous robots in the production outlay of 7-UP Bottling Company Factory at 9th Mile, Ngwo, was carried out. The need for the use of robots in the factory line production plants was an understudy. It was meant that a robot should be used to replace the work done by men in the packing of produced mineral beverages, otherwise known as soft drinks. Preliminary research showed that robots could be more suitable in that aspect of the production process owing to the enormous strength needed to meet daily production targets. Summary of the research results made relevant revelations. The result obtained when ANN is used is a speed of 13.47m/s while that of using conventional methods like PD or PI is 67.44m/s. With these results, it shows that using ANN accomplished its allotted duty faster at a shorter speed than using a conventional approach like PD or PI.
Keywords: Non-Linear System, Artificial Neural Network, Autonomous Robot, Proportional Integral, Proportional Integral Derivative
Eze, Ukamaka J.; Bakare, Kazeem and Ndubuisi Paul-Darlington I. | Full PDF