Sage Husa Research Articles

Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

The short-term power load forecast is deeply studied by integrating fuzzy BP neural networks and composite adaptive filtering in this paper. Due to the difficulty on accurate modeling of complex factors in smart grid, the fuzzy technology is introduced to deal with the uncertain factors. Meanwhile, data-driven method, such as neural networks, is used as the basic frame of the power load forecast by combing the fuzzy technology. Thereby, to design a highly effective training scheme becomes the main problem on the weights of the BP-NNs. To realize better weights training, the combined filtering technology is fully adopted by fusing adaptive filtering methods based on observable degree (OD) analysis, Sage–Husa adaptive technology, and variational Bayesian method. Thereby, a novel power load forecast algorithm is proposed based on the fuzzy BP-NNs and the combined adaptive cubature Kalman filter. Experiment based on practical power load data is presented to validate the effectiveness of the proposed short-term power load algorithm.

Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage–Husa Adaptive Filter

Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage–Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.

Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Autonomous navigation for agricultural machinery has broad and promising development prospects. Kalman filter technology, which can improve positioning accuracy, is widely used in navigation systems in different fields. However, there has not been much research performed into navigation for sprinkler irrigation machines (SIMs). In this paper, firstly, a self-developed SIM is introduced. Secondly, the kinematics model is established on the platform of the self-developed SIM, and the updated Sage–Husa adaptive Kalman filter, which is an accurate and real-time self-adaptive filtering algorithm, is applied in the navigation of the SIM with the aim of improving the positioning accuracy. Finally, experiment verifications were carried out, and the results show that the self-developed SIM has good navigation performance. Besides this, the influence of abnormal observations on the positioning accuracy of the system can be restrained by using the updated Sage–Husa adaptive Kalman filter. After using the updated Sage–Husa adaptive Kalman filter for the SIM, the maximum deviation between the SIM and the predetermined path is 0.18 m, and the average deviation is 0.08 m; these deviations are within a reasonable range. This proves that the updated Sage–Husa adaptive Kalman filter is applicable for the navigation of sprinkler irrigation machines.

Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).

An Integrated Adaptive Kalman Filter for High-Speed UAVs

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots.

The key to successful positioning of autonomous mobile robots in complicated indoor environments lies in the strong anti-interference of the positioning system and accurate measurements from sensors. Inertial navigation systems (INS) are widely used for indoor mobile robots because they are not susceptible to external interferences and work properly, but the positioning errors may be accumulated over time. Thus ultra wideband (UWB) is usually adopted to compensate the accumulated errors due to its high ranging precision. Unfortunately, UWB is easily affected by the multipath effects and non-line-of-sight (NLOS) factor in complex indoor environments, which may degrade the positioning performance. To solve above problems, this paper proposes an effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots, in which our modeling approach is simple to implement and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed. Due to the favorable property (i.e., self-adaptive adjustment) of SHFAF, the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly. Moreover, outliers can be detected and corrected by the proposed sliding window estimation with fading coefficients. This facilitates the positioning performance improvement for indoor mobile robots. The benefits of what we propose are illustrated by not only simulations but more importantly experimental results.

A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles

Estimation of the state-of- charge (SOC) of power batteries has always been the focus of electric vehicle users’ criticism. Accurate SOC is beneficial for extending the mileage of electric vehicles and the life of the battery pack. The key to improving SOC accuracy is to establish its accurate model and combine it with an appropriate estimation algorithm. Based on characterization experiments related to SOC, this paper describes a second-order charge–discharge resistor–capacitor model that can accurately simulate external characteristics of the battery and identify them online. An improved adaptive unscented Kalman filter algorithm based on Sage–Husa is introduced to estimate SOC. The reliability of the algorithm is verified by building a MATLAB/Simulink simulation model. The results show that the improved algorithm displays increased robustness and can quickly converge to the true value; the steady-state error is also within a small range.

Investigation of Adaptive Robust Kalman Filtering Algorithms for GPS/DR Navigation System Filters

The conventional Kalman filter (KF) algorithm is suitable if the characteristic noise covariance for states as well as measurements is readily known but in most cases these are unknown. Similarly robustness is required instead of smoothing if states are changing abruptly. Such an adaptive as well as robust Kalman filter is vital for many real time applications, like target tracking and navigating aerial vehicles. A number of adaptive as well as robust Kalman filtering methods are available in the literature. In order to investigate the performance of some of these methods, we have selected three different Kalman filters, namely Sage Husa KF, Modified Adaptive Robust KF and Adaptively Robust KF, which are easily simulate able as well as implementable for real time applications. These methods are simulated for land based vehicle and the results are compared with conventional Kalman filter. Results show that the Modified Adaptive Robust KF is best amongst the selected methods and can be used for Navigation applications.

State observation–based control algorithm for dynamic vibration absorbing systems featuring magnetorheological elastomers: Principle and analysis

Based on state observation, a rapid, stable, and effective control algorithm for magnetorheological elastomer (MRE)–based dynamic vibration absorbers (DVAs) applied to automobile powertrain mount systems is proposed and investigated in this article. The state-space model for powertrain mount systems with MRE-based DVAs is established using the rank criterion method for observable systems. According to the principle of system reconfiguration, a full state observation model using an adaptive Kalman filter with Sage–Husa noise estimator is developed. With the state vectors estimated by the Kalman filter, the phase difference between the displacement of the dynamic mass of the MRE-based DVA relative to the powertrain and the absolute displacement of the powertrain is updated continuously based on Simpson’s rule. By adjusting the applied current to the MRE-based DVA with fuzzy logic control corresponding to the cosine value of the phase difference, the natural frequency of the MRE-based DVA could track the excitation frequency of the powertrain well, which results in vibration attenuation of the powertrain mount system. With consideration of excitation noise, time delays, and parametric uncertainties, the simulation experiments of vibration attenuation performance of the MRE-based DVA for the powertrain mount systems when under time-varying excitation are carried out to verify the effectiveness and the stability of the proposed algorithm with fuzzy steps. The simulation results show that when using the proposed algorithm with fuzzy steps, the MRE-based DVA could attenuate the powertrain vibration rapidly and effectively, and the vibration attenuation performance will not be influenced by noise, time delays, and parametric uncertainties.

Range tracking method based on adaptive “current” statistical model with velocity prediction

This study considers the problem of range tracking of a maneuvering target by an airborne digital array radar system. In the classic “current” statistical (CS) model, the maneuvering frequency α is not adaptive, and an acceleration limit has to be set. First, a new range tracking method called “adaptive CS model with velocity prediction” (ACSMVP) is proposed. This method utilizes the velocity prediction algorithm of cross product automatic frequency control with loop filter to measure the radial acceleration between the radar and the target. With the acceleration been obtained, the maneuvering frequency is derived, which is calculated in every tracking period. Second, an improved algorithm called “Sage–Husa-based ACSMVP” (SH-ACSMVP) is also proposed. In this algorithm, an online measuring noise covariance matrix is achieved by a simplified Sage–Husa estimator. Third, a Cramér-Rao lower bound of pulse-Doppler pulse train signal for range estimation is derived to assess the range tracking performance of the proposed algorithms. Simulations highlight the superior performance of our proposed algorithms over existing techniques.

Weighted adaptive filtering algorithm for carrier tracking of deep space signal

Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system. For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio (SNR) is unknown. If the frequency-locked loop (FLL) or the phase-locked loop (PLL) with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused. Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence. Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio. The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process. In addition, the upper diagonal (UD) factorization and innovation adaptive control are used to reduce model estimation errors, suppress filter divergence and improve filtering accuracy. The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.

Application of Magnetic Filter Technology in Improving the Precision of a Micro Attitude Reference System

A micro attitude reference system is vulnerable to overload interference. To solve this problem, a type of Kalman filter was designed on the basis of a geomagnetic technique. Allan variance analysis was used to improve the Sage–Husa adaptive filter algorithm. Attitude error and gyro drifts were used as state variables for adaptive filtering updates, and a three-component magnetic vector was used for adaptive filtering measurement updates. Experimental results show that the pitch accuracy of the attitude reference system with magnetic filter technology working in 2 g vibrations improves by 4.7 times, the roll accuracy improves by 1.5 times, and the heading accuracy improves by 5.4 times. The proposed method can effectively reduce overload interference and ensure angular accuracy.

Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS

In this paper, the adaptive simplified spherical simplex unscented Kalman filter was proposed to calculate angular velocity in gyro-free strapdown inertial navigation system. Firstly, a general angular velocity calculation modeling method with time-varying process noise was proposed, which was not limited to a certain kind of accelerometer configuration. Then aiming at the issues of large amount of calculation of unscented Kalman filter and the time variation of the process noise, and based on the characteristics of additive noise and linear state equation, the adaptive simplified spherical simplex unscented Kalman filter was proposed to estimate the angular velocity. The sampling points were decreased in this method through adopting the spherical simplex sampling strategy and not augmenting the state, thus improving the calculation efficiency. Meanwhile, Sage–Husa suboptimal maximum a posteriori noise estimator was brought in to estimate the process noise in real time in order to settle the problem of filter divergence induced by the time variation. Lastly, the proposed algorithm was simulated and also contrasted with the integration method, the evolution method and the conventional adaptive UKF algorithm. The simulation results indicated that the adaptive simplified spherical simplex unscented Kalman filter algorithm has higher precision than the integration method and evolution method and has higher efficiency than the AUKF, which could effectively improve the calculation precision and meanwhile guarantee the calculation efficiency.