Multirate Nonlinear Systems Research Articles

Distributed fusion filtering for multi-rate nonlinear systems with random sensor failures under event-triggering round-robin-like scheme

The distributed fusion filtering problem is addressed for the multi-rate nonlinear systems with random sensor failures (RSFs) over sensor networks, where a prediction compensation approach is proposed to transform the system unlike the lifting technique. The RSFs are portrayed by using stochastic variables with known statistical properties that satisfy certain probability distribution. In order to prevent data conflicts and reduce unnecessary data transmission, the event-triggering round-robin-like scheme (ETRRLS) is introduced to schedule the data transmission among sensor nodes. The main objectives of this paper are to design a local distributed filtering scheme based on the information of itself and ETRRLS scheduled neighboring nodes, and obtain an upper bound on the local filtering error (LFE) covariance which is minimized based on the filter gains design. Afterward, the local filters are fused by using the sequential covariance intersection fusion criterion. Moreover, we provide a sufficient condition, which can ensure the boundedness of the trace of LFE covariance. Finally, a simulation example is presented to illustrate the effectiveness and superiority of the newly proposed distributed fusion estimation algorithm.

Distributed Recursive State Estimation for a Class of Multi-Rate Nonlinear Systems Over Wireless Sensor Networks Under FlexRay Protocols

Distributed Recursive State Estimation for a Class of Multi-Rate Nonlinear Systems Over Wireless Sensor Networks Under FlexRay Protocols

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Performance evaluation of neural network topologies for online state estimation and fault detection in pressurized water reactor

The nuclear reactor is a multi-rate nonlinear system in which the state variables progress with widely varying dynamics. It has state variables such as reactivity and delayed neutron precursor densities that cannot be measured directly via sensors. Reactivity signifies the criticality of the reactor core. Delayed neutron precursors are the source for delayed neutrons which plays a vital role in the change of neutron densities. Besides, the other states which are measured are also corrupted by measurement noise and are susceptible to sensor faults. Thus, estimation of these state variables becomes critical. As traditional estimators like EKF, UKF, and Particle filers require a close model of the process, Data-driven Neural networks architectures like Feed-Forward Neural networks, Dynamic NARX Neural networks, and Recurrent Neural networks are designed to estimate the reactor core states. The performance of the selected neural estimator is also compared with the Unscented Kalman estimator.

Distributed recursive filtering for multi-rate nonlinear systems under the Round-Robin scheduling

Distributed recursive filtering for multi-rate nonlinear systems under the Round-Robin scheduling

Frequency-domain estimates of the sampling interval in multirate nonlinear systems by time-delay approach

ABSTRACTA class of nonlinear hybrid systems consisting of a continuous plant and a multirate sampled-data controller is considered. Applying the time-delay approach, the system is transformed to the system with a time-varying sawtooth delay. Then, the value of the sampling intervals ensuring stability of the closed-loop system is estimated based on the frequency-domain circle-like criterion for systems with time-varying delay. Such a criterion for multirate MIMO systems is established based on the previous result for SISO systems proposed by M.Churilova in 1995. Efficiency and advantages of the method are illustrated by numerical examples. The key advantage of the proposed approach is that it is applicable to multirate systems nonaffine in control. It allows one to use a nonlinear feedback for stabilisation. Particularly, the case of nonsmooth or discontinuous nonlinear systems can be treated.

Estimation of reactivity and delayed neutron precursors’ concentrations using a multiscale extended Kalman filter

A wavelet based multiscale extended Kalman filtering technique for estimation of reactivity and delayed neutron precursors’ concentrations is presented in this paper. Reactivity which indicates the criticality status of the reactor core can only be measured in indirect way. Similarly delayed neutron precursors’ concentrations, the source of the delayed neutrons which play important role in reactor control cannot be measured directly. Nuclear reactor is an example of multirate nonlinear system in which different state variables evolve with widely varying dynamics. The state estimation algorithm presented here is based on and preserves merits of Extended Kalman Filtering (EKF) technique. In addition, use of wavelet filters enables multiscale decomposition of the state variables that in turn, effectively captures the multirate nature of the system. Estimation has been carried out using reactor power as the only input. In order to justify effectiveness of the proposed method, simulation results are shown for completely known power variation dataset and experimental power variation datasets collected from one of the Indian research reactors.

Filtering for multirate nonlinear sampled-data systems using T-S fuzzy models

This paper investigates the l2-l~ filtering problem for multirate nonlinear sampled-data systems. By employing the Takagi-Sugeno (T-S) fuzzy model to represent the nonlinear system, the original multirate nonlinear system can be expressed as a set of linear multirate subsystems. Then the lifting technique is applied to reformulate the multirate system as a single-rate system. Finally, the fuzzy multirate filter with guaranteed energy-to-peak (l2-l~) performance is designed by solving a set of linear matrix inequalities (LMIs). Simulation examples are provided to illustrate the effectiveness of the proposed method.

On multi-rate fusion for non-linear sampled-data systems: Application to a 6D tracking system

Egomotion estimation, e.g. for robot navigation or augmented reality applications, requires the fusion of non-linear sampled-data system with different sensors. An example is to fuse the complimentary characteristics of visual and inertial sensors. Existing approaches either use Kalman filters in conventionally sampled systems or use Particle filters to accommodate the uncertainty of motion models. This paper introduces an approach that models multi-rate non-linear systems to exploit the characteristics of both sensors, assuming synchronicity and periodicity of measurements. The final contribution of this paper is an in-depth analysis and performance comparison of the Extended Kalman filter, the Unscented Kalman filter and three particle filters (Bootstrap, Extended and Unscented). While there is large debate over the pros and cons of these two approaches, this work shows the following results for fusing visual and inertial data in 6 DOF (position and orientation) in a tracking application: the Bootstrap Particle filter gives higher estimation error than Extended and Unscented Particle filters, which give very similar results than Extended and Unscented Kalman filters, but with considerable higher computational burden.