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

Abstract

Summary 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.

Big data and predictive analytics: A systematic review of applications

“…We can classify these articles into three sub-classes. The first class has articles discussing business processes (Zhang et al 2022a;Kong et al 2022;Yang and Ge 2022;Shafi et al 2021;Krumeich et al 2014;Mishra 2019). The second class has articles focusing on the Industrial Internet of things (IIoT) (Failed 2017c(Failed , 2018bYu et al 2020;Wang et al 2020;Tryapkin and Shurova 2020;Lin et al 2022;Kodidala et al 2021;Rosati, et al 2023).…”

“…Lastly, the authors shed light on the future directions and possible potential to excel the industrial applications with the use of big data. Shafi, et al (Shafi et al 2021) mathematically modeled an IP system. Then, by the usage of proportional-integral-derivative, controlled utilizing of data.…”

“…These improvements will positively affect resource reliability, utilization, and other related metrics. According to the reviewed and discussed industrial articles, the comparison of their specifications has been depicted in Table 3 which are divided into three categories: business processes (Zhang et al 2022a;Kong et al 2022;Yang and Ge 2022;Shafi et al 2021;Krumeich et al 2014;Mishra 2019);IIoT (Failed 2017c, 2018bYu et al 2020;Wang et al 2020;Tryapkin and Shurova 2020;Lin et al 2022;Kodidala et al 2021); Supply chain (Hazen et al 2014;Gunasekaran, et al 2017;Dubey, et al 2019;Jeble 2018;Nilashi, et al 2023). Table 3 shows the main ideas, evaluation methods, tools, advantages, and disadvantages of industrial articles.…”

PID Neural Controller Design for Nonlinear Inverted Pendulum System

“…Inverted pendulum (IP), system with two degrees of freedom (angular pendulum movement and cart location) and a single control input, orthodox control is difficult to implement. Given how complex the issue is, one must choose a dynamic system to analyze it as a model and develop a law to govern the system [1].…”