Interval Extended Kalman Filter—Application to Underwater Localization and Control

The extended Kalman filter has been shown to be a precise method for nonlinear state estimation and is the facto standard in navigation systems. However, if the initial estimated state is far from the true one, the filter may diverge, mainly due to an inconsistent linearization. Moreover, interval filters guarantee a robust and reliable, yet unprecise and discontinuous localization. This paper proposes to choose a point estimated by an interval method, as a linearization point of the extended Kalman filter. We will show that this combination allows us to get a higher level of integrity of the extended Kalman filter.

A Two-Step Global Maximum Error Controller-Based TPWL MOR with POD Basis Vectors and Its Applications to MEMS

In our previous study, we have proposed a linearization point (LP) selection method based on a global maximum error controller for the trajectory piecewise-linear (TPWL) method. It has been demonstrated that this method has many advantages over other existing methods. In this paper, a more efficient version of this method is presented, which introduces a preliminary LP selection procedure and constructs projection matrix by the proper orthogonal decomposition (POD) method. Compared with the original method, the improved method takes much less time for extracting a reduced-order model (ROM) of similar quality and gets some other benefits (such as being easier to implement, having lower memory requirement, and enhanced flexibility). The effectiveness of the new method is fully demonstrated by a diode transmission line RLC circuit. And then, the method is applied to three more complicated microelectromechanical systems (MEMS) devices, which are a micromachined switch, an electrostatic micropump diaphragm, and a thermomechanical in-plane microactuator.

Robust Kalman Filter Thrust Estimation in a Turbofan Engine

This article will present that a robust Kalman filter design has a favorable property, when applied on thrust estimation on a low bypass turbofan gas turbine engine, compared to the regular Kalman filter design. This property is a larger operation range in parameters around the linearization point. On the other hand, the robust Kalman filter has marginally lower accuracy at the linearization point. This paper will present a method for describing the uncertainties in the engine model for use in the design of a robust Kalman filter. Both a regular Kalman filter and a robust Kalman filters are evaluated through simulations around a linearization point by using simulations of a nonlinear military turbofan engine.

Systematic Controller Design Methodology for Variable-Speed Wind Turbines

Variable-speed, horizontal axis wind turbines use blade-pitch control to meet specified objectives for three regions of operation. This paper provides a guide for controller design for the constant power production regime. A simple, rigid, non-linear turbine model was used to systematically perform trade-off studies between two performance metrics. Minimization of both the deviation of the rotor speed from the desired speed and the motion of the actuator is desired. The robust nature of the proportional-integral-derivative controller is illustrated, and optimal operating conditions are determined. Because numerous simulation runs may be completed in a short time, the relationship between the two opposing metrics is easily visualized. Traditional controller design generally consists of linearizing a model about an operating point. This step was taken for two different operating points, and the systematic design approach was used. The surfaces generated by the systematic design approach using the two linear models are similar to those generated using the non-linear model. The gain values selected using either linear model-based design are similar to those selected using the non-linear model-based design. The linearization point selection does, however, affect the turbine performance. Inclusion of complex dynamics in the simulation may exacerbate the small differences evident in this study. Thus, knowledge of the design variation due to linearization point selection is important.