---

Dynamic model identification and state variables estimation from the corrupted measurement data have been attracted much research efforts during the recent years. In this way, Kalman and H-infinity filters have been increasingly used to estimate the parameters individually. In this paper, a mixed kalman-H_∞ filter is designed in an innovative approach using a multi-objective optimization method. It is desired to simultaneously employ the advantages of both filters to minimize both the root-mean squared errors and the upper bounds limit of estimation errors associated with Kalman and H-infinity filters, respectively. Some Pareto optimum design points are presented for two case studies from which trade-off optimum design points can be simply selected.

---

This paper presents a new approch in the design of output feedback control system based on disturbance observer for dynamic positioning vessels. The proposed control system includes a controller and a structure of a modified notch filter and a nonlinear observer. The filter is used for estimating low-frequency motions and removing the wave-frequency motions by using vessel position mesurement. The low-frequency disturbances and vessel-velocites are estimated in nonliner observer using the low-frequency vessel motion. In this structre, wave filtering and low-frequency motion estimation are independent from the estimation of low-frequency disturbances and vessel velocities. It causes to incease the accuracy of filtering and estimation which results in desirable performance of control system. Also, filtering is independent of the vessel and low frequency disturbances models, and therefore it is not affected by modeling uncertainty. The effect of wave filtering and low-frequency disturbances estimation in DP control system from the point of reducing control signal flactutions were evaluated with numerical simulation. This is important in view of reduction of wear and tear in propaltion system and fuel consumption in a surface vessel. Futhermore, simulation results show that the proposed method has better performances in comparision with conventional method.

---

This paper presents a new scheme based on state estimation to diagnosis an actuator or plant fault in a class of nonlinear systems that represent the nonlinear dynamic model of gas turbine engine. An optimal nonlinear observer is designed for the nonlinear system. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton -Jacobi -Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Using the proposed observer, the system states and the fault signal can be estimated and diagnosed, respectively. The proposed approach is implemented for state estimation and fault detection of a gas turbine model subject to compressor mass flow fault. The simulation results illustrate that the proposed fault detection scheme is a promising tool for the gas turbine diagnostics.

---

The early success in the 1960s of the Kalman filter in aerospace applications led to attempts to apply it to more common industrial applications in the 1970s. However, these attempts quickly made it clear that a serious mismatch existed between the underlying assumptions of Kalman filters and industrial state estimation problems. Accurate system models and statistical nature of the noise processes are not as readily available for industrial problems. In this paper, a novel method of combining two nonlinear unscented Kalman filter and "H" _∞ unscented Kalman filter is presented so that the results are a compromise between in addition of more reliability compared to that of two other filters. One characteristic of this filter is no need to linearize of the nonlinear problems and gives more suitable results than other two filters with non-Gaussian noise. Investigations show, when in a part of estimating the UKF is best and in the other part the UHF, the hybrid filter can give better results with present a compromise estimation. The variance analysis indicated that the filter is robust to statistical noise nature and a proper response can be found by changing its variable. Validation of results is performed by simulation of two nonlinear problems, free falling and inverted pendulum in mechanical engineering.

---

In order to use and control Shape Memory Alloy (SMA) actuators, it is essential to measure its state variables to be used as the feedback in the control loop. The wire temperature is one of critical state variables need to be fed back. However, measuring this variable is difficult and usually contains some noises and delay. Therefore, it is desirable to estimate this variable instead of measuring it. Thermoelectric model is one of the most common models used to estimate the SMA wire temperature. This model calculates the SMA wire temperature based on its input electric current. In this paper, first three unknown parameters of thermoelectric model are estimated using Extended Kalman filter (EKF) and the wire temperature is calculated based on the identified model. The parameter estimation and temperature calculation are performed on a practical SMA actuator. Then, in order to eliminate the effects of environmental disturbances and the thermoelectric model inaccuracies, the temperature is estimated using EKF. In this method, all measurable data such as the input current, the strain and stress of the SMA wire are used in the temperature estimation. The estimator combines the information obtained from both thermoelectric and Brinson models and the measurement data. This method is used for online temperature estimation of the SMA wire on a practical SMA actuator. The results show that the estimated temperature matches the actual wire temperature with high precision. Furthermore, the temperature estimation using EKF is more accurate than the estimates of the thermoelectric model.