An Improved Cuckoo Search Algorithm for Dynamic Compensation of High-Speed Railway Sensors

The traction and braking systems of high-speed railways impose stringent requirements on the dynamic performance and measurement consistency of sensors. Dynamic response errors (e.g., overshoot and oscillation) and data discreteness issues in conventional sensors severely impact the control accuracy and safety of the system. This paper proposes an intelligent compensation method based on an Improved Cuckoo Search (ICS) algorithm, aimed at enhancing the dynamic performance of sensors. The method involves constructing a dynamic compensation filter model and utilizing the ICS algorithm to efficiently optimize the filter parameters. The improvement strategies include local perturbation of the best solution, a linearly increasing discovery probability, and an adaptive step-size adjustment mechanism based on fitness changes, which balances global exploration and local exploitation to avoid premature convergence to local optima. Simulation results demonstrate that, compared to the standard Cuckoo Search algorithm, the compensator designed by the proposed ICS algorithm can more effectively suppress the dynamic errors of the sensor. It reduces the Root Mean Square Error (RMSE) of the step response by approximately 25 % and significantly improves the discreteness of the output signal, validating the effectiveness and application potential of the method in enhancing the dynamic accuracy of highperformance sensors.