Tae-Sung Yoon

Practical pinch detection algorithm for low-cost anti-pinch window control system

A practical pinch torque estimator based on the Hinfin filter is proposed for low-cost anti-pinch window control systems. To obtain the acceptable angular velocity measurements from Hall-effect sensor outputs, the angular velocity calculation algorithm is proposed with the measurement noise reduction logics. Apart from the previous works based on the angular velocity or torque estimates for detecting the pinched condition, the proposed pinch detection algorithm makes use of the torque rate information. To do this, the torque rate is augmented to the system model and the torque rate estimator is derived by applying the steady-state Hinfin filter recursion to the model. The motivation of this approach comes from the idea that the torque rate is less sensitive to the motor parameter uncertainties. Moreover, the statistics of modelling errors and angular velocity measurement noises are actually unknown. Hence, the proposed scheme minimizes the anti-pinch window control systems exposure to the false alarm. To detect the pinched condition, a systematic way to determine the threshold level of the torque rate estimates is also suggested via the deterministic estimation error analysis. Experimental results certify the pinch detection performance of the proposed algorithm and its robustness against the motor parameter uncertainties

Robust pinch estimation and detection algorithm for low-cost anti-pinch window control systems

A practical pinch torque estimator based on the Kalman filter is proposed for low-cost anti-pinch window control systems. To obtain the accurate angular velocity from Hall-effect sensor measurements, the angular velocity calculation algorithm is proposed by including the measurement noise reduction logics. Apart from the previous works based on the angular velocity or torque estimates for detecting the pinched condition, the proposed pinch detection algorithm makes use of the torque rate information. To do this, the torque rate is augmented to the system model and the torque rate estimator is derived by applying the steady-state Kalman filter recursion to the model. The motivation of this approach comes from the idea that the torque rate is less sensitive to the motor parameter uncertainties. Hence, the proposed scheme minimizes the anti-pinch window control systems exposure to the false alarm. To detect the pinched condition, a systematic way to determine the threshold level of the torque rate estimates is also suggested via the deterministic estimation error analysis. Experimental results certify the pinch detection performance of the proposed algorithm and its robustness against the motor parameter uncertainties.

Real-Time Robust Pinch Detection Algorithm for Automobile Applications

A robust pinch detection algorithm which can be implemented in a cheap microprocessor is proposed for safety power window systems. To deal with the problems caused by performance degradation of a Hall sensor or real driving situations, the proposed algorithm makes use of the Hinfin state estimation technique. The motivation of this approach comes from the advantage that the Hinfin filter can minimize or bound the worst-case estimation error energy for all bounded energy disturbances. Herein, the pinch torque rate estimator is derived by applying the steady-state Hinfin filter to the augmented model which includes the motor dynamics and an additional torque rate state. Then, by redesigning the estimator to be appropriate for real-time implementation, the torque rate estimate can be calculated more simply than the previous method (Lee et al., 2005). Experimental results verify that, with a small amount of computation, the proposed pinch detection algorithm provides fast pinch detection performance similar to the existing method. Furthermore, it guarantees robustness against the worst-case measurement noises

Advanced Relative Localization Algorithm Robust to Systematic Odometry Errors

In this paper, a novel localization algorithm robust to the unmodeled systematic odometry errors is proposed for low-cost non-holonomic mobile robots. It is well known that the most pose estimators using odometry measurements cannot avoid the performance degradation due to the dead-reckoning of systematic odometry errors. As a remedy for this problem, we tty to reflect the wheelbase error in the robot motion model as a parametric uncertainty. Applying the Krein space estimation theory for the discrete-time uncertain nonlinear motion model results in the extended robust Kalman filter. This idea comes from the fact that systematic odometry errors might be regarded as the parametric uncertainties satisfying the sum quadratic constrains (SQCs). The advantage of the proposed methodology is that it has the same recursive structure as the conventional extended Kalman filter, which makes our scheme suitable for real-time applications. Moreover, it guarantees the satisfactoty localization performance even in the presence of wheelbase uncertainty which is hard to model or estimate but often arises from real driving environments. The computer simulations will be given to demonstrate the robustness of the suggested localization algorithm.

Recursive robust H/sub /spl infin// filtering within the framework of set-valued estimation

A recursive robust H/sub /spl infin// filtering algorithm is newly proposed for the discrete time uncertain linear system subject to the energy constraint called sum quadratic constraint (SQC). A set valued estimation approach will be used to tackle the given problem. To this end, by combining an SQC on the H/sub /spl infin// norm condition of the error dynamics and an inequality relationship between the uncertainty input and output, we obtain an augmented SQC and then formulate the robust H/sub /spl infin// filtering problem as the one of finding the set of estimates satisfying this constraint. The solutions will be given in terms of ellipsoids whose centers are the minimums of the indefinite quadratic function defined by the augmented SQC. The Krein space estimation theory will be utilized to efficiently deal with the minimization problem of the indefinite quadratic function and it is shown that the robust H/sub /spl infin// filter turns out to be just the special form of Krein space Kalman filter. The proposed robust filter has basically the same recursive structure as the information form of Kalman filter and therefore demands less computational burdens for the implementation. Numerical examples will be given to verify that the proposed filter guarantees the robustness in the presence of parametric uncertainties and its bounding ellipsoidal sets of filtered estimates always contain true states.