Machine learning and its impact on control systems: A review. (2021)

• Record Type: Journal Article
• Authors: Dev, Prabhat; Jain, Siddharth; Kumar Arora, Pawan; Kumar, Harish
• Is Part Of: Materials today. Volume 47:Part 13(2021)
• Page Start: 3744
• Page End: 3749
• Publication Date: 2021
• Subjects: Control system -- PID -- Neural network -- RNN -- LSTM -- System identification; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.02.281
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Control systems play a vital role in our day to day lives, from mobile phones to autopilot systems that precisely navigate airplanes, they can be found anywhere. The primary role of any control system is to achieve a set point defined by the user and provide stability to the system while doing so. There are various approaches that are used for the design of control systems such as PID (Proportional, Integral and Derivative control) algorithms, Fuzzy logic controller, Neural Network controllers etc. Machine learning (ML) is a key tool in analysing time series data and can be used to predict the future states of any dynamic system, however, sufficient historic data is required. This key feature of ML based predictive algorithms can be employed in modelling a wide range of non-linear dynamic systems for ensuring system stability as well as seeking continuous improvement in system response. This paper gives a brief introduction of conventional methods used in the design of control systems with focus on Neural Network based control strategies which employ Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) networks for system identification and predictive control of dynamic processes.