Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation

Abstract

Abstract-This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power (Ricardo Wave), and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical results show very encouraging results with small estimation errors even in the presence of sensor noise.Index Terms-Engine torque estimation, mean value engine model, unknown input observer, time-varying parameter estimation.

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

“…Controller parameters have been estimated to minimize the tracking errors in robot manipulation (Na et al [25] and Wang et al [26]). Adaptive methods like unknown input observer and time-varying parameter estimation have also been proposed and applied in complex systems such as vehicle engine system (Na et al [27]). …”

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

“…Parameters identifications are also be populated for frequently usage in various engineering such as hovercraft system, robot system, vehicle engines, and Wiener system . However, parameters identifications only be suited to some class of known structured model systems in other works . Although some novel adaptive parameter estimation control schemes were constructed for the unknown robotic system in the work of Na et al, the assumption of persistent excitation condition is necessary to be known beforehand.…”

Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc‐Wen Model

“…The servo motor system has been widely utilized in industry and military (vehicle systems [1][2][3], robotic systems [4][5][6][7], radar systems [8][9][10], and nonlinear turn table systems [11][12][13][14][15]). But hysteresis nonlinearity exists in these motor driving servo systems.…”