Implementation of Neural Network-based PID Controller for Speed Control of an IC Engine

Praneeth, V. N. Siva; Kumar, V. Bharath; Sampath, Dasa; Kumar, Y. V. Pavan; Pradeep, D. John; Reddy, Ch. Pradeep; Kannan, Ramani

doi:10.1007/978-981-16-7664-2_33

Control and Measurement Applications for Smart Grid

2022

DOI: 10.1007/978-981-16-7664-2_33

|View full text |Cite

Implementation of Neural Network-based PID Controller for Speed Control of an IC Engine

V. N. Siva Praneeth

V. Bharath Kumar

Dasa Sampath

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A robust proportional filtered integral controller based on backpropagation neural network

Benharkou,

Gherbi,

Sedraoui

et al. 2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers.

show abstract

A robust proportional filtered integral controller based on backpropagation neural network

Benharkou,

Gherbi,

Sedraoui

et al. 2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques

et al. 2023

View full text Add to dashboard Cite

Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Implementation of Neural Network-based PID Controller for Speed Control of an IC Engine

Cited by 2 publications

References 17 publications

A robust proportional filtered integral controller based on backpropagation neural network

A robust proportional filtered integral controller based on backpropagation neural network

Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques

Contact Info

Product

Resources

About