Abstract: The measurement value of the icing sensor designed with three-terminal piezoelectric ceramics is greatly affected by temperature. In order to improve the measurement accuracy of the sensor, an adaptive simulated annealing particle swarm optimization algorithm is proposed to compensate for temperature. This algorithm integrates the simulated annealing algorithm and the particle swarm algorithm, uses the nonlinear hyperbolic tangent function to control the change of the inertia weight coefficient, and uses the linear change strategy to control the values of the social learning factor and the self-learning factor, so that the optimization focus varies at different stages. The inertia weight coefficient , the social learning factor and the self-learning factor are adaptively changed, which solves the problem that the algorithm is easy to fall into the local optimal solution. The experimental results show that after the temperature compensation of this algorithm, the measurement error of the freezing state is reduced to about 3% ,  and the measurement accuracy is improved to 0.1mm, which indicates that the compensation algorithm can effectively reduce the influence of -5 ℃~-55 ℃ on the measurement results of the sensor and improve the measurement accuracy.

Key words: Piezoelectric ceramic, ice sensor, temperature compensation, simulated annealing algorithm; particle group optimization algorithm