摘要：Piezoelectric actuators fundamentally possess hysteresis behavior. Estimation of the hysteresis is usually demanded for enhancing the performance of piezo-actuated systems. This paper presents an observer-based scheme to estimate the hysteresis in piezo—actuated flexible beams. The observer is based on a nonlinearity observer method. The discrete-time kalman-filter algorithm is adopted for determination of the observer gains. The major advantages of the presented scheme include ease of implementation and robustness to uncertainty of hysteresis parameters. Simulation results demonstrate that the observer is able to estimate the hysteresis efficiently and has better robustness compared to the previous scheme existing in the literature. The present scheme was also successfully applied to a real-life system. Moreover, a control application example is included to demonstrate the effectiveness of the scheme.

摘要：Recently there have been researches about new efficient nonlinear filtering techniques in which the nonlinear filters generalize elegantly to nonlinear systems without the burdensome lineafization steps. Thus, truncation errors due to linearization can be compensated. These filters include the unscented kalman filter （UKF）, the central difference filter （CDF） and the divided difference filter （DDF）, and they are also called Sigma Point filters （SPFs） in a unified way. For higher order approximation of the nonlinear function. Ito and Xiong introduced an algorithm called the Gauss Hermite filter, which is revisited in [5]. The Gauss Hermite filter gives better approximation at the expense of higher computation burden, although it＇s less than the particle filter. The Gauss Hermite filter is used as introduced in [5] with additional pruning step by adding threshold for the weights to reduce the quadrature points.

摘要：A fast U D factorization based kalman filter for the 21 state integrated global positioning system and inertial navigation system (GPS/INS) is developed from the point of engineering implementation. The conventional kalman filter is widely used for integration of GPS/INS, however, due to the model and numerical computation errors, the kalman filter may diverge in engineering implementation. In order to solve this problem, an extended kalman filter based on the U D factorization is proposed. Moreover, the high order integrated system suffers from the problem of long computation time, leading to difficulties in real time applications. An algorithmic approach is developed to improve the computational speed. A typical aircraft trajectory is simulated to compare the improvement in the computational speed and the navigation accuracy using the conventional kalman filter and the fast kalman filter based on the U D factorization. The results indicate that the methods proposed in this paper are very effective in overcoming these problems for the high dynamic integrated GPS/INS system.

摘要：Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard kalman filters is *** performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are *** simulation is based on the almanac data of the GPS satellites to compute its feasibility during the simulation time and position on shape 8 track with continuous vehicle maneuvering. The goal is to obtain computationally efficient filter with reasonable accuracy for vehicle in maneuvering situation. The filter proposed is an alternative to the filter proposed in Ref. [1] with low computational burden.

摘要：An interval kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.

摘要：用户基线负荷是工商业用户参与需求响应项目执行效果的重要参考,受到环境、用户用电行为等多种因素的影响。为提高工商业用户基线负荷预测的精度,提出了一种基于时间序列(ARMA)和卡尔曼滤波(kalman filter)组合的需求响应基线负荷预测模型,通过沙普利值(Shapley Value)方法求出单个预测模型对组合模型的边际贡献率,得到最优的预测结果。案例结果表明,kalman filter模型对负荷波动平稳的时间段内预测精度较高,时间序列模型对负荷波动较大的时间段内预测精度较高,而组合预测模型结合了两者的优点,降低了单一模型在预测过程中受时间因素造成偏差较大的影响,提高了整体预测精度,扩大了适用范围。

摘要：This paper is concerned with the practical application control of a pneumatically actuated Stewart-Gough platform with 6-degrees of freedom （6-DOF）. The control approach for motion control of the platform is presented using a modern control technique, namely, linear quadratic Gaussinn （LQG） with reference tracking. The LQG controller is the combination of a kalman filter, i.e., a linear-quadratic estimator （LQE） with a linear-quadratic regulator （LQR）. The robustness of the control scheme is accessed under various load conditions, and the experimental results are shown.

摘要：In this paper, an uplink power control problem is considered for code division multiple access （CDMA） systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio （SIR）. A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a kalman filter problem.

摘要：The fiber strapdown inertial navigation system （FSINS）/dead reckoning （DR）/Beidou double-star integrated navigation scheme is proposed aiming at the need of land fighting-vehicle independence positioning. The measurement information fusion technology is studied by introducing the FSINS/DR/Beidou double-star integrated scheme. Several specific methods for the information fusion are discussed, and a kalman filter is designed for the information fusion. Experimental results show that the design of the integrated scheme can improve the positioning accuracy of the navigation system.

摘要：Since the landmark work of R. E. kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture （GM） prior to a Gaussian/GM posterior （termed ＇Gaussian conjugacy＇ in this paper）, form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality （including target maneuver）, intractable uncertainties （such as unknown inputs and/or non-Gaussian noises） and constraints （including circular quantities）, etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.