A computationally intelligent neural network-based nonlinear autoregressive exogenous balancing approach for real-time processing in industrial applications using big data

摘要

Deep learning based neural networks and their variants have gained popularity due to their inherent flexibility to handle unforeseen especially when a chaotic time series big data are required to be dealt with. There are innumerable applications that are beneficiary of vast interest in computational intelligent approaches that include but not limited to robotics, healthcare, transport, industrial, decision making, and gaming. This paper attempts to investigate the effectiveness of using a neural nonlinear autoregressive with exogenous inputs (NARX) controller in an emerging application field of balancing systems like inverted pendulum (IP) using big data. This paper's aim has been to control an IP cart system by designing a neural NARX controller, and the focus is primarily on real-time processing in industrial applications grounded on big data ecosystems. In the proposed work, an IP system is mathematically modeled and first controlled utilizing a combination of classical proportional-integral-derivative (PID) controllers for cart and pendulum. Second, a chaotic time series input-output data are obtained and are used to train two NARX controllers for cart and pendulum, respectively. Both the controllers are designed as single-input single-output systems with one layer each at input and output with suitable number of hidden layers and neurons. Performance comparison of NARX system behavior with PID controller indicates that the NARX controllers successfully adapt to two different kinds of unknown inputs and effectively stabilize the plant. Simulation results confirm that NARX controllers follow the training parameters and exhibit superior performance and overall system stability than PID control. Experimental results demonstrate the effectiveness of the approach.