Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

Anti-symmetric sampled grating quantum cascade laser for mode selection

For mode selection in a quantum cascade laser(QCL),we demonstrate an anti-symmetric sampled grating(ASG).The wavelength of the-1-th mode of this laser has been blue-shifted more than 75 nm(~10 cm^(-1))compared with that of an ordinary sampled grating laser with an emission wavelength of approximately 8.6μm,when the periodicities within both the base grating and the sample grating are kept constant.Under this condition,an improvement in the continuous tuning capability of the QCL array is ensured.The ASG structure is fabricated in holographic exposure and optical photolithography,thereby enhancing its flexibility,repeatability,and cost-effectiveness.The wavelength modulation capability of the two channels of the grating is insensitive to the variations in channel size,assuming that the overall waveguide width remains constant.The output wavelength can be tailored freely within a certain range by adjusting the width of the ridge and the material of the cladding layer.

Parameter estimation for dual-rate sampled Hammerstein systems with dead-zone nonlinearity

The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.

H_∞ CONTROL FOR SAMPLED-DATA SYSTEMS

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Consensus for second-order multi-agent systems with position sampled data

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated.The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example.

ROBUST FILTERS WITH SAMPLED-DATA ESTIMATION COVARANCE CONSTRAINT FOR UNCERTAIN CONTINUOUS-TIME SYSTEMS

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DEADBEAT COVARIANCE CONTROLLER DESIGN FOR SAMPLED-DATA SYSTEMS

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ROBUST CONTROL WITH COVARIANCE CONSTRAINT FOR UNCERTAIN SAMPLED-DATA FEEDBACK CONTROL SYSTEMS

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Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

Exponential Stabilization and Estimation for Sampled Observer Design of Surface Mounted Permanent Magnet Synchronous Motor

A nonlinear state observer design with sampled and delayed output measurements for variable speed and external load torque estimations of SPMSM drive system has been addressed, successfully. Sampled output state predictor is re-initialized at each sampling instant and remains continuous between two sampling instants. Throughout this study, a positive constant to satisfy an upper limit of the sampling period between sampling instants and allowable timing delay in terms of observer parameters has been prepared such that the exponential stable of the closed-loop system is guaranteed, based on Lyapunov stability tools. In order to validate the theoretical results introduced by main fundamental theorem to prove the observer convergence, the proposed sampled-data observer is demonstrated through a sample study application to variable speed SPMSM drive system.

Two Pass Port Scan Detection Technique Based on Connection Pattern and Status on Sampled Data

Anomaly detection is now very important in the network because the increasing use of the internet and security of a network or user is a main concern of any network administrator. As the use of the internet increases, so the chances of having a threat or attack in the network are also increasing day by day and traffic in the network is also increasing. It is very difficult to analyse all the traffic data in network for finding the anomaly in the network and sampling provides a way to analyse the anomalies in network with less traffic data. In this paper, we propose a port scan detection approach called CPST uses connection status and pattern of the connections to detect a particular source is scanner or benign host. We also show that this approach works efficiently under different sampling methods.

A Method to Reconstruct Nth-Order Periodically Nonuniform Sampled Signals

It is well known that nonuniform sampling is usually needed in special signals processing. In this paper, a general method to reconstruct Nth-order periodically nonuniform sampled signals is presented which is also developed to digital domain, and the designs of the digital filters and the synthesis system are given. This paper extends the studies of Kohlenberg, whose work concentrate on the periodically nonuniform sampling of second order, as well as the studies of A.J.Coulson, J.L.Brown, whose work deal with the problems of two-band signals’ Nth-order sampling and reconstruction.

Digital Spectral Representation of Nonuniformly Sampled Periodic Signals

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Robust Pole Assignment of Digital Control System with Output Multirate Sampled

Multirate digital control system is a periodically time-variant (PTV) system in its essence. It bas many" super capability", such as obtaining arbitrarily-large gain- margin, simultaneous stabilization, strong stabilization, decentralized control, etc. Utilizing freedom aroused from the multirate sampling of system output, this paper assigns poles of the closedloop system robustly, and so improves the resistance of the system to perturbation.

Heavy-Tailed Distributions Generated by Randomly Sampled Gaussian, Exponential and Power-Law Functions

A simple stochastic mechanism that produces exact and approximate power-law distributions is presented. The model considers radially symmetric Gaussian, exponential and power-law functions inn= 1, 2, 3 dimensions. Randomly sampling these functions with a radially uniform sampling scheme produces heavy-tailed distributions. For two-dimensional Gaussians and one-dimensional exponential functions, exact power-laws with exponent –1 are obtained. In other cases, densities with an approximate power-law behaviour close to the origin arise. These densities are analyzed using Padé approximants in order to show the approximate power-law behaviour. If the sampled function itself follows a power-law with exponent –α, random sampling leads to densities that also follow an exact power-law, with exponent -n/a – 1. The presented mechanism shows that power-laws can arise in generic situations different from previously considered specialized systems such as multi-particle systems close to phase transitions, dynamical systems at bifurcation points or systems displaying self-organized criticality. Thus, the presented mechanism may serve as an alternative hypothesis in system identification problems.

FAST ALGORITHM FOR NON-UNIFORMLY SAMPLED SIGNAL SPECTRUM RECONSTRUCTION

In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).

Distributed Least Squares Algorithm of Continuous-Time Stochastic Regression Model Based on Sampled Data

In this paper,the authors consider the distributed adaptive identification problem over sensor networks using sampled data,where the dynamics of each sensor is described by a stochastic differential equation.By minimizing a local objective function at sampling time instants,the authors propose an online distributed least squares algorithm based on sampled data.A cooperative non-persistent excitation condition is introduced,under which the convergence results of the proposed algorithm are established by properly choosing the sampling time interval.The upper bound on the accumulative regret of the adaptive predictor can also be provided.Finally,the authors demonstrate the cooperative effect of multiple sensors in the estimation of unknown parameters by computer simulations.

Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems

The main aim of this work is to design a non-fragile sampled data control(NFSDC) scheme for the asymptotic synchronization criteria for interconnected coupled circuit systems(multi-agent systems, MASs). NFSDC is used to conduct synchronization analysis of the considered MASs in the presence of time-varying delays. By constructing suitable Lyapunov functions, sufficient conditions are derived in terms of linear matrix inequalities(LMIs) to ensure synchronization between the MAS leader and follower systems. Finally, two numerical examples are given to show the effectiveness of the proposed control scheme and less conservation of the proposed Lyapunov functions.

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Considering a variety of sampled value(SV)attacks on busbar differential protection(BDP)which poses challenges to conventional learning algorithms,an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper.The healthy SV data of BDP are defined as self-data composed of spheres of the same size,whereas the SV attack data,i.e.,the nonself data,are preserved in the nonself space covered by spherical detectors of different sizes.To avoid the confusion between busbar faults and SV attacks,a self-shape optimization algorithm is introduced,and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives.Based on the difficulty of boundary coverage in traditional negative selection algorithms,a self-data-driven detector generation algorithm is proposed to enhance the detector coverage.A testbed of differential protection for a 110 kV double busbar system is then established.Typical SV attacks of BDP such as amplitude and current phase tampering,fault replays,and the disconnection of the secondary circuits of current transformers are considered,and the delays of differential relay operation caused by detection algorithms are investigated.

Spectrum Usage Anomaly Detection from Sub-Sampled Data Stream via Deep Neural Network

Anomaly detection is an essential part of any practical system in order to remedy any malfunction and accident early to create a secure and robust system.Malicious users and malfunctioning cognitive radio(CR)devices may cause severe interference to legitimate users.However,there are no effective methods to detect spontaneous and irregular anomaly behaviors in sub-sampling data stream from wideband compressive spectrum sensing as an important function of a CR device.In this article,to detect anomaly utilization of spectrum from sub-sampled data stream,a multiple layer perceptron/feed-forward neural network(FFNN)based solution is proposed.The proposed solution would learn the pattern of legitimate and anomalous usages autonomously without expert's knowledge.The proposed neural network(NN)framework has also shown benefits such as more than 80%faster detection speed and lower detection error rate.