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This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate.

An adaptive electronic throttle control strategy is proposed with the aim to provide robust and precise positioning of the throttle plate. The control strategy consists of a PID controller and a nonlinear compensator of friction and limp-home effects. The adaptation mechanism includes auto-tuning and self-tuning algorithms. The auto-tuner provides automatic adjustment of the key control strategy parameters without any prior knowledge of the process parameters. The self-tuning algorithms are based on the permanent, on-line estimation of the DC motor armature resistance, battery voltage, and limp-home position. The control strategy and the adaptation algorithms are verified experimentally.

This paper presents a detailed experimental analysis of potentials for road condition estimation based on the tire static curve gradient in the low-slip region, and the damping of the 40 Hz tire vibration mode. An experimental vehicle equipped with four standard ABS sensors (44 pulses/rev), two special high-resolution wheel speed sensors (512 pulses/rev), and a halfshaft torque sensor was utilized for collecting the experimental data. The test data were recorded on a test track characterized by different road conditions such as dry concrete, wet and dry ice, and wet and dry snow.

The paper presents a detailed design procedure of an SI engine load torque estimator based on an adaptive Kalman filter. The adaptation mechanism is based on a simple and robust load torque change detection algorithm. The presented simulation and experimental results point out that the estimator is characterized by a fast response and good noise suppression. The proposed estimator is used to establish a fast load torque compensation path in an idle speed control system. The presented simulation and experimental results show that this yields a significant improvement of idle speed control performance