To improve the upright balancing performance of the two-wheeled self-balancing car, this paper proposes an attitude estimation algorithm based on fuzzy Kalman filtering. Fuzzy logic is used to correct the inclination angle and angular velocity of the two-wheeled self-balancing car, thereby optimizing the state of the Kalman filter and ultimately improving the balancing performance of the car. This paper combines dual closed-loop PID control with the complementary filtering algorithm, Kalman filtering algorithm, and fuzzy Kalman filtering algorithm to conduct experiments on a physical two-wheeled self-balancing car. The experimental results validate the superiority of the fuzzy Kalman filtering algorithm proposed in this paper for improving the upright balancing performance of the two-wheeled self-balancing car.

This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation.