In this paper, a nonlinear motorcycle model is considered in order to estimate both the lean and steering dynamics. The model is transformed into a Takagi-Sugeno (T-S) form using the well-known sector nonlinearity approach. The first contribution of this work is the exactness of the obtained T-S model compared to the considered nonlinear model, where the weighting functions of the T-S model depend on unmeasured state variables. A novel approach to construct a nonlinear unknown input fast observer is proposed. The objective is the simultaneous reconstruction of the state variables and the rider's torque. The observer's convergence is studied using Lyapunov theory guaranteeing boundedness of the state and unknown input estimation errors which is expressed by the Input to State Practical Stability (ISpS). Stability conditions are then expressed in terms of Linear Matrix Inequalities (LMI). Finally, simulation results are provided to confirm the suitability of the proposed nonlinear observer.
Abstract-This paper deals with lateral dynamics estimation of powered two-wheeled (PTW) vehicles. A linear parametervarying (LPV) model of the motorcycle is presented and then transformed into a Takagi-Sugeno (TS) form in order to design the observer under the variation of the longitudinal velocity. This paper presents a new way to estimate every motorcycle dynamic states including unknown input (UI) by keeping a simple observer structure with a Luenberger observer in TS form. The observer convergence study is based on the Lyapunov theory associated with LMI tools and L2-gain to guaranty boundedness of the state estimation error. The effectiveness of the proposed solution is illustrated for a nominal and an uncertain simulation cases.
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