Parameter and state estimation in vehicle roll dynamics

In active rollover prevention systems, a real-time rollover index, which indicates the likelihood of the vehicle to roll over, is used. This paper focuses on state and parameter estimation for reliable computation of the rollover index. Two key variables that are difficult to measure and play a critical role in the rollover index are found to be the roll angle and the height of the center of gravity of the vehicle. Algorithms are developed for real-time estimation of these variables. The algorithms investigated include a sensor fusion algorithm and a nonlinear dynamic observer. The sensor fusion algorithm requires a low-frequency tilt-angle sensor, whereas the dynamic observer utilizes only a lateral accelerometer and a gyroscope. The stability of the nonlinear observer is shown using Lyapunov's indirect method. The performance of the developed algorithms is investigated using simulations and experimental tests. Experimental data confirm that the developed algorithms perform reliably in a number of different maneuvers that include constant steering, ramp steering, double lane change, and sine with dwell steering tests.