Parametric estimation of nonlinear 3D of system using genetic algorithm in time domain

This paper mainly concentrates on the procedural technique to identify the system parameters of multi degrees of freedom nonlinear system using Genetic Algorithm (GA) in time domain. Conventional optimization techniques are mainly calculus based, and often fails in search for global optimum. In this paper estimation has been done using continuous Genetic Algorithm (GA). In parametric estimation the difference between experimental acceleration and acceleration estimated by GA is minimized by updating the parameters. A three degrees of freedom (3-DOF) system with two pairs of non-linear spring and damper (Van der Pol–Duffing oscillator) is considered. Both hardening and softening type of nonlinear springs are considered and corresponding parameters are estimated. The system is excited by known harmonic forces at each mass to ensure enough excitation. Different combinations of hardening and softening nonlinear springs, nonlinear dampers, and single and two nonlinear springs are solved in the present study. The percentage of error in estimation of parameters is less than 5%. Perturbation analysis has been done to study the sensitivity of parameters on output.