Abstract
Vehicle controllability analysis on real roads can be obtained only if valid road profile and tyre road friction model are known. This work determines the time-vary road profiles, called inputs, in a nonlinear system using two input estimation methods. Both algorithms use the extended Kalman filter (EKF) with two different recursive estimators to determine inputs and states. Based on the two regression equations, a recursive least-squares estimator is used with a tuneable fading factor called a conventional input estimation (CIE) and an adaptive weighting fading factor called an adaptive weighting input estimation (AWIE). Numerical simulations of a nonlinear system, quarter car model, demonstrate the accuracy of the proposed methods. Simulation results show that proposed methods accurately estimate road profiles, tyre forces, and states, and the AWIE approach has superior robust estimation capability to the CIE method in the nonlinear system. The simulation results are the same with the single degree of freedom.
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