Small scale helicopter system identification based on Modified Particle Swarm Optimization

This paper proposes an effective Modified Particle Swarm Optimization (MPSO) identification method which is suitable for small scale helicopter system identification. By remodifying the Basic Particle Swarm Optimization (BPSO) algorithm, both the global search capability and the convergence rate can be guaranteed, meanwhile the operation of the established identification process is surveyed. With the help of parameter estimation and the reduced search space, the determination process of the primary identification parameters can largely speed up during the search progress and improve recognition accuracy. In order to test the proposed identification method and the validity of the model developed by the identification system, an object experimental helicopter, which is simplified as a linear flight dynamic model, is used as a test bed to carry out identification tests. By using test data that is not involved in the identification process, the performance of the proposed method is verified, and the validity of the results is also testified by direct comparing between the identified model and the actual flight test data. The final results demonstrate that the proposed MPSO method can be used both effectively and practicality in the system identification fields.