Parameter Identification of LuGre Friction Model in Servo System Based on Improved Particle Swarm Optimization Algorithm

LuGre friction model can describe dynamic characteristics of friction in servo system accurately, but because of its high nonlinearity, it is very difficult to estimate the parameters of the model. In this paper, based on particle swarm optimization algorithm, a two-step off-line identification methodology of the LuGre friction parameters is presented to compensate the dynamic friction. Firstly, four static parameters are identified via Stribeck curve. Secondly, two dynamic parameters are estimated by stick-slip response curve. Particle swarm optimization is used in both steps to minimize the identification errors, which can avoid local convergence problem existing in the many linear identification methods. The main advantage of this method in comparison with classical ones, as the least-squares approach, is that it provides not only estimation of the parameters but also precision with which the estimated values is guaranteed, and at the same time it can avoid the problem of local minimum. At last, the identification results are applied to a ship-borne gun servo system. Experiments verify the effectiveness of the proposed scheme for high-precision motion trajectory tracking.