Road Geometry and Steering Reconstruction for Powered Two Wheeled Vehicles

Abstract

This paper deals with the estimation of both motorcycle lateral dynamics and road geometry reconstruction. A linear parameter varying (LPV) unknown input observer is designed to estimate the whole motorcycle dynamic states including road banking angles and the rider's steering torque taken into account the variation of the forward velocity. The road bank angle and the lateral slip angle are relevant parameters for improving rider's safety and handling, thus, it is interesting to estimate the road geometry. The observer convergence study is based on Lyapunov theory and the established convergence conditions are expressed in linear matrix inequalities (LMIs) formalism. The main idea consists in getting a set of conditions to design an observer transformed into a polytopic form, which estimates a part of the motorcycle dynamics states independently of some inputs (rider torque) and/or other states (zeros dynamics: roll angle) taken into account the variation of the longitudinal velocity. Simulation results of experimental test are provided to confirm the efficiency of the proposed design method.

Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics

“…An LPV unknown inputs observer with Takagi-Sugeno representation formalized in the LMI framework was proposed in [18,19] for the estimation of both vehicle lateral dynamics and the driver's steering torque. A simultaneous estimation of lateral dynamics and road attributes, including curvature, slope, and superelevation, was addressed in [20][21][22][23][24]. Steering and torque actuators' faults detection was studied in [25], where the nonlinear vehicle dynamics were reformulated as an N-TS fuzzy form with both measured and unmeasured nonlinear outcomes in order to design a fault detector based on a nonlinear observer.…”