• 제어로봇시스템학회논문지
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• 제22권6호
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• pp.411-416
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• 2016

This paper presents the design methodology of an unknown input observer for Lipschitz nonlinear systems with unknown inputs in the framework of convex optimization. We use an unknown input observer (UIO) to consider both nonlinearity and disturbance. By deriving a sufficient condition for exponential stability in the linear matrix inequality (LMI) form, existence of a stabilizing observer gain matrix of UIO will be assured by checking whether the quadratic stability margin of the error dynamics is greater than the Lipschitz constant or not. If quadratic stability margin is less than a Lipschitz constant, the coordinate transformation may be used to reduce the Lipschitz constant in the new coordinates. Furthermore, to reduce the maximum singular value of the observer gain matrix elements, an object function to minimize it will be optimally designed by modifying its magnitude so that amplification of sensor measurement noise is minimized via multi-objective optimization algorithm. The performance of UIO is compared to a nonlinear observer (Luenberger-like) with an application to a flexible joint robot system considering a change of load and disturbance. Finally, it is validated via simulations that the estimated angular position and velocity provide true values even in the presence of unknown inputs.

• 한국자동차공학회논문집
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• 제23권5호
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• pp.478-485
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• 2015

Availability of the climbing angle information is crucial for the intelligent vehicle system. However, the climbing angle information can't be measured with the sensor mounted on the vehicle. In this paper, climbing angle estimation system is proposed. First, longitudinal acceleration obtained from gyro-sensor is compared with the actual longitudinal acceleration of the vehicle. If the vehicle is in yawing motion, actual longitudinal acceleration can't be approximated from time derivative of wheel speed, because lateral velocity and yaw rate affect actual longitudinal acceleration. Wheel speed and yaw rate can be obtained from the sensors mounted on the vehicle, but lateral velocity can't be measured from the sensor. Therefore, lateral velocity is estimated using unknown input observer with nonlinear tire model. Simulation results show that the compensated results using lateral velocity and yaw rate show better performance than uncompensated results.