It is demonstrated the proposed approach achieves to determine unknown parameters with negligible relative errors in spite of noise interference.
📌 3 Key Takeaways
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Objective & Hypothesis:This work deals with parameter identification of a vehicle using population based algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and Genetic Algorithm (GA).
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Key Findings & Data:Solving the optimization problem vehicle mass, moments of inertia and vehicle center of gravity parameters, which are necessary for later applications such as vehicle control and performance analysis, are obtained.
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Conclusion & Impact:It is demonstrated the proposed approach achieves to determine unknown parameters with negligible relative errors in spite of noise interference.
This work deals with parameter identification of a vehicle using population based algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and Genetic Algorithm (GA). Full vehicle model with seven degree of freedom (DoF) is employed, and two objective functions based on reference and computed responses are proposed. Solving the optimization problem vehicle mass, moments of inertia and vehicle center of gravity parameters, which are necessary for later applications such as vehicle control and performance analysis, are obtained. It is demonstrated the proposed approach achieves to determine unknown parameters with negligible relative errors in spite of noise interference.
Gökdağ, H. (2015). Vehicle parameter identification using population based algorithms. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 3 Issue: 2. https://ajindex.com/articles/1398702
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