The issues of chassis dynamics-based navigation sensor fault and state estimation in land vehicles are specialized in this study. Owing to the essential attributes of robust theory-based observers, an H-infinity adaptive observer is proposed to implement the fault reconstructions of faulty sensors, offering a reference to vehicles for further favorable control decision-making. This observer fuses a linear matrix inequality convex optimization strategy, with the dynamics of land vehicles established mathematically, the consequent problems associated with augmented descriptor system state-space model, Lyapunov stability and linear matrix inequality convex optimization are discussed in detail. The numerical simulations on vehicular systems that suffered with single-existing deadlocking, gain scheduling, and constant deflection sensor fault are conducted. The results indicate, the fault channel outputs fairly reflect the variations of real faults under severe step-type fault input circumstances, so that the applicability of the fault observer against sensor failures is guaranteed. The proposed sensor fault construction idea is further extended to a loosely coupled inertial measurement units/global positioning system (GPS) illustration with GPS unavailable in its north velocity channel. After reconstructing the priori system state for “State one-step prediction” of Kalman filter, the compensated navigation parameters by state estimator exhibit consistent with the references as expected, the vehicle chassis dynamics-based sensor fault construction method, therefore, may be recognized as an effective measure to a class of integrated navigation systems experiencing some unknown sensor failures.
Implementation of Kalman filter algorithm on reduced models using Linear Matrix Inequality method and its application to measurement water level
