Proportional-derivative unknown input observer design using descriptor system approach for non-minimum phase systems

Abstract

This paper considers the problem of estimating the state of an MIMO linear system with unknown inputs in the state and output. Through a series of linear transformations in the state and output equations, the original system can be transformed into a descriptor system form. The proposed proportional derivative observer can accurately estimate the system state and avoid the peaking phenomenon. Moreover, the approach developed in this paper does not require the derivatives of the output and can be applied to the system with unstable zeros (with respect to the relation between the output and the unknown input). Finally, our algorithm can prove the valid feasibility and the property of disturbance attenuation through demonstrating a simulation-base example.

An Efficient Robust Servo Design for Non-Minimum Phase Discrete-Time Systems with Unknown Matched/Mismatched Input Disturbances

“…Several developed approaches to simultaneously estimate the system state and unknown input can be referred to [1][2][3][4] and in the literature therein. Nevertheless, most UIO design methodologies presented in the early literatures require that the transfer function from the unknown input to the system output is minimum-phase and of relative degree one.…”

“…Nevertheless, most UIO design methodologies presented in the early literatures require that the transfer function from the unknown input to the system output is minimum-phase and of relative degree one. Recently, some design methodologies, which exempted the assumptions that the transfer function must be the minimum-phase (with respect to the relation between the unknown input to the system output, but not the relation between the control input to the system output) and unit-relative-degree constraints have been reported in [1,3,4] under some rank conditions. More precisely, in order for the design methodology to be feasible in [1,3], the dimensions of the unknown inputs/disturbances must be no greater than the output dimensions.…”

State estimation and simultaneous unknown input and measurement noise reconstruction based on adaptive H ∞ observer

“…Hence, unknown input observer designs can be found their applications in many fields, including fault-tolerant control design, fault diagnosis and isolation, and chaos synchronization-based secure communication [6,7]. Recently, many results on unknown input observer designs can be found in literatures [8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Novel unknown input observer for fault estimation of gas turbine dynamic systems

“…the use of optimization methods [11,14], the unknown input observer (UIO) [15], the sliding mode observer [16], the high-gain estimator [13,17] and the geometric design approaches [18]. Particular, unknown input observer (UIO) is an interesting method for robust fault diagnosis, which has received much attention during the last three decades [19,20]. In the meanwhile, another important tool for robust design is the linear matrix inequality (LMI) technique.…”