A Systematic Approach to Estimate Non-Linear System Parameters using Particle Swarm Optimization and Bond Graph Methods

Bahiuddin, Irfan and Siregar, Parsaulian I and Amri Mazlan, Saiful and Nugroho, Rizki S and Imaduddin, Fitrian and Ismail, Andhi A (2021) A Systematic Approach to Estimate Non-Linear System Parameters using Particle Swarm Optimization and Bond Graph Methods. In: International Conference on Instrumentation, Control, and Automation (ICA).

[thumbnail of A_Systematic_Approach_to_Estimate_Non-Linear_System_Parameters_using_Particle_Swarm_Optimization_and_Bond_Graph_Methods.pdf] Text
A_Systematic_Approach_to_Estimate_Non-Linear_System_Parameters_using_Particle_Swarm_Optimization_and_Bond_Graph_Methods.pdf
Restricted to Registered users only

Download (841kB) | Request a copy

Abstract

This paper presents an application of particle swarm optimization (PSO) methods to predict Bond Graph parameters. In a real-world application, the parameters within the bond graph model can be unknown due to various reasons, such as weariness and unknown disturbance. Hence, the parameters can be identified using metaheuristic methods, such as PSO. Therefore, several PSO variants are applied and investigated to predict the unknown parameters for two nonlinear systems. The modeled systems are a single tank case representing a simple incompressible fluid system and a passive actuator in turbocharger representing interconnected multi-domain system behaving highly nonlinear considering the pulsating flow exhaust gas pressure as the device's input. The investigated PSO systems are the classic, linearly decreasing inertia weight, and constriction PSO. The objective function is formulated to estimate the equivalent pipe area in the tank system and two parameters in the passive actuator system. After the optimization process, the predicted response has an agreement with the experimental results. The linearly decrease inertia weight PSO has shown comparable performance with constriction PSO with root mean square error up to 0.7 for a tank system and 2.5 for the passive actuator. The applied PSO shows satisfying results and shows its capability to predict unknown variables for both cases. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 1
Uncontrolled Keywords: Actuators; Forecasting; Graph theory; Linear systems; Mean square error; Nonlinear systems; Tanks (containers); Bond graph; Bond graph method; Dynamics simulation; Inertia weight; Non linear system; Parametric models; Particle swarm optimization method; Passive actuators; Systems parameters; Tank system; Particle swarm optimization (PSO)
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering > Mechanical and Industrial Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 04 Nov 2024 08:06
Last Modified: 04 Nov 2024 08:06
URI: https://ir.lib.ugm.ac.id/id/eprint/10603

Actions (login required)

View Item
View Item