This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear iden...This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62025306CAS Project for Young Scientists in Basic Research under Grant No.YSBR-008。
文摘This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.
