In this project, I provide a novel method for integrating reservoir computing, a type of recurrent neural network, with Cyber Physical Systems (CPS). I look at the difficulty presented by the rising complexity of CPS, which makes it difficult for conventional control techniques to meet necessary flexibility and performance standards. I use reservoir computing to provide CPS with dynamic and reliable control techniques as a remedy.

I use the well-known reservoir computing model Echo State Networks (ESNs) to forecast the time-series data included in CPS. A control mechanism that can adjust in real-time to changes in system behavior is built on this prediction capability.

The fusion of computing, networking, and physical pro- cesses has resulted in an unprecedented increase in the com- plexity of Cyber-Physical Systems (CPS). Traditional control systems, which were created for situations that were less inter- connected and dynamic, encountering substantial difficulties as a result of this complexity. More flexible, resilient, and effective control mechanisms are thus the need of the hour in order to keep up with the changing nature of CPS. The potential of reservoir computing techniques, including Echo State Networks (ESN), Neural Circuit Policies (NCP), Liquid State Machines (LSM), Liquid Time Constants (LTC), and Closed-form Continuous-time (CfCt) networks, is exam- ined in this research. Each of these approaches has distinctive qualities that make it appropriate for handling the high- dimensional, non-linear, and time-dependent complexity of CPS. Through simulations in various CPS scenarios, I demon- strate that the proposed ESN-based control strategy signifi- cantly outperforms traditional methods in terms of adaptability and prediction accuracy. This study offers promising evidence for the viability of reservoir computing in CPS, opening up new avenues for future research in adaptive control strategies. Furthermore, the findings underscore the potential for enhanc- ing system reliability and performance in rapidly evolving CPS environments. Initially, I sought to use DeepMind’s MuJoCo, a physics engine built for simulating large physical systems, to provide an appropriate setting for my CPS. I did, however, have significant challenges while trying to build a reliable data pipeline between the CPS and the MuJoCo simulation. This experience brought home the inherent difficulties of managing sophisticated CPS with conventional methods, particularly when it comes to handling and analyzing the data these systems produce.