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This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset Unknown Input Observer (R-UIO) for state estimation of linear systems in the presence of disturbance using Linear Matrix Inequality (LMI) techniques. In R-UIO, the states of the observer are reset to the after-reset value based on an appropriate reset law in order to decrease the L 2 norm and settling time of estimation error. It is shown that the application of the reset theory to the UIOs in the LTI framework can significantly improve the transient response of the observer. Moreover, the devised approach can be applied to both SISO and MIMO systems. Furthermore, the stability and convergence analysis of the devised R-UIO is addressed. Finally, the efficiency of the proposed method is demonstrated by simulation results.

This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset Unknown Input Observer (R-UIO) for state and fault estimation of a class of nonlinear uncertain systems using linear matrix inequality (LMI) techniques. In the devised R-UIO, the states of the observer are reset to the after-reset value based on an optimal H ∞ reset law in order to decrease the L 2 norm and settling time of estimation error. It is shown that the utilization of such an observer can significantly improve the transient response of the observer. Moreover, the devised approach can be applied to both SISO and MIMO systems. Furthermore, the robust stability analysis of the devised R-UIO is addressed. Finally, the capabilities of the proposed method are demonstrated by applying it to a Continuous Stirred-Tank Reactor (CSTR) as a practical model.

The Kalman Filter (KF) is the most popular approach in sensor fusion and navigation applications. Improving the accuracy of estimation may increase the navigation accuracy. KF is the best choice for minimum variance optimal estimation, but it is not the best approach to improve some of the main and classic features such as steady-state error reduction. These features are more important than optimality for applications like accuracy improvement in navigation. Also, these features and their solutions have been addressed in classic control techniques as a widely known subject. Therefore, some similar improvements in the estimation problem are discussed in the literature. These studies try to conserve the optimality in the covariance of the error, but classic and optimal features cannot be achieved simultaneously. Hence, these methods are not as efficient as expected. This paper intends to improve the estimation of position and velocity in navigation problems by integrating classic and modern control techniques. Beside the classic features and advantages, it may be seen that the resulted covariance of error is not minimum anymore, but it is comparable with the optimal methods.

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