@article{scholars17746,
             doi = {10.1007/978-981-16-7664-2{$_3$}{$_3$}},
            note = {cited By 1; Conference of International Conference on Smart Grid Energy Systems and Control, SGESC 2021 ; Conference Date: 19 March 2021 Through 21 March 2021; Conference Code:271959},
          volume = {822},
           title = {Implementation of Neural Network-based PID Controller for Speed Control of an IC Engine},
            year = {2022},
           pages = {409--418},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Lecture Notes in Electrical Engineering},
            isbn = {9789811676635},
          author = {Siva Praneeth, V. N. and Bharath Kumar, V. and Sampath, D. and Pavan Kumar, Y. V. and John Pradeep, D. and Pradeep Reddy, C. and Kannan, R.},
            issn = {18761100},
        abstract = {In the present day, transportation plays an important role in any country{\^a}??s economy and sustenance. Even though electric vehicles have started market intrusion, at present, main commuting vehicles such as cars, ships and planes work on internal combustion engines (ICEs). In line with any complex system, an ICE exhibits poor time domain characteristics when not controlled properly. Generally, PID controller is used to control the ICE to give better time domain characteristics. There are various conventional methods available to tune the PID controller such as OLTR methods and ultimate cycle methods. Generally, these offline controller tuning methods cannot address non-linear disturbances effectively. So, to overcome these drawbacks, there is a need for using artificial intelligence-based tuning methods. Hence, this paper implements an artificial neural network-based PID controller and compares it with a conventional method and track the rate of change of PID parameters with the injection of disturbances. This paper concludes that the response of the ICE system tuned with ANN-PID gives a better output when compared to the key conventional PID tuning methods. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125279976&doi=10.1007\%2f978-981-16-7664-2\%5f33&partnerID=40&md5=bb73b857cc23bf3d8fc84e702cc16ca6},
        keywords = {Controllers; Electric control equipment; Ice; Integrated circuits; Neural networks; Speed control; Three term control systems; Time domain analysis; Timing circuits; Tuning, ANN-PID controller; Conventional methods; I.C. engine; IC engines; Network-based; Neural-networks; PID controllers; Time domain characteristics; Tuning method, Internal combustion engines}
}