Yechen Qin

Road excitation classification for semi-active suspension system based on system response:

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to determine road classification output. The most representative semi-active control strategy, i.e. skyhook control, is used to validate this method, and simulation results with varying conditions including different control parameters and sprung mass are compared. The results show that unsprung mass acceleration is most suitable for road classification, and more robust to varying conditions in comparison to other responses.

The Use of Vehicle Dynamic Response to Estimate Road Profile Input in Time Domain

A new method for road profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.Copyright

A new modeling algorithm based on ANFIS and GMDH

System modeling is one of the most important tasks of dynamic analysis and prediction systems, and imprecise model may lead to high bias. The presence of noise in sample data can make it more difficult to obtain precise system models. A new modeling algorithm called ANFIS-GMDH is presented in this paper, which builds upon the traditional ANFIS structure and utilizes the self-organizing mechanisms of GMDH. The aim of ANFIS-GMDH is to improve upon the traditional ANFIS method and prevent overfitting of noisy data. The well-studied Box-Jenkins gas furnace data is utilized to validate the algorithm, with results showing that the proposed algorithm performs better than traditional ANFIS, GMDH and subtractive clustering for both noisy and noiseless data, without any significant increase in execution time.

Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer

A novel road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the road profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the road profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.