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.

Robust H-infinity filtering on uncertain systems under sampled measurements

This paper is concerned with the problem of robust H-infinity filtering on uncertain systems under sampled measurements, both continuous disturbance and discrete disturbance are considered in the systems. The parameter uncertainty is assumed to be time-varying norm-bounded. The aim is to design an asymptotically stable filter, using the locally sampled measurements, which ensures both the robust asymptotic stability and a prescribed level of H-infinity performance for the filtering error dynamics for all admissible uncertainties. The derivation process is simplified by introducing auxiliary systems and the sufficient condition for the existence of such a filter is proposed. During the study, the main results were expressed as LMIs by employing various matrix techniques. Using LMI toolbox of Matlab software, it is very convenient to obtain the appropriate filter. Finally, a numerical example shows that the method is effective and feasible.

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.

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.

Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

Inductor Current Sampled Feedback Control of Chaos in Current-Mode Boost Converter

A chaos control strategy for chaotic current-mode boost converter is presented by using inductor current sampled feedback control technique.The quantitative analysis of control mechanism is performed by establishing a discrete alterative map of the controlled system.The stability criterion,feedback gain,and corresponding critical duty ratio are obtained from the eigenvalue of the map.The simulation results verify the t heoretical analysis results of the control strategy.

H_∞ CONTROL FOR SAMPLED-DATA SYSTEMS

This paper presented a tutorial exposition of H ∞ sampled data control, emphasizing the H ∞ discretization which converts the H ∞ sampled data system to an equivalent finite dimensional H ∞ discrete time system. The discretization process involves two steps. First, the original problem is changed into an equivalent infinite dimensional discrete time problem by lifting techniques. Then, further simplification is taken to reduce the problem to an equivalent finite dimensional discrete problem which can be solved by the existing techniques such as state space approach or two riccati method.

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

The problem of robust controller design with covariance constraint for uncertain sampled data feedback control systems was considered in this paper. The goal of this problem is to design controllers such that the closed loop system meets the prespecified covariance constraint. This problem can be reduced to a controller design problem for an equivalent uncertain discrete time system. Sufficient conditions were given for the existence of the desired controllers. The analytical expression of the set of desired controllers was also presented. An illustrative example was given to show the applicability of the proposed design procedure.

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

This paper was concerned with the problem of robust sampled data state estimation for uncertain continuous time systems. A sampled data estimation covariance is given by taking intersample behaviour into account. The primary purpose of this paper is to design robust discrete time Kalman filters such that the sampled data estimation covariance is not more than a prespecified value, and therefore the error variances achieve the desired constraints. It is shown that the addressed problem can be converted into a similar problem for a fictitious discrete time system. The existence conditions and the explicit expression of desired filters were both derived. Finally, a simple example was presented to demonstrate the effectiveness of the proposed design procedure.

DEADBEAT COVARIANCE CONTROLLER DESIGN FOR SAMPLED-DATA SYSTEMS

The problem of deadbeat covariance controller design for sampled data feedback systems is considered. The purpose of considering this problem is to design linear discrete controllers such that the state covariance of the closed loop system achieves its steady state value which is equal to a prespecified positive definite matrix during finite beats. This problem is reduced to the similar one for equivalent discrete time systems by taking intersample behaviour into account. Both the existence conditions and the explicit expression of the desired controllers are given.

Application of sampled grating to control the lasing wavelength in complex-coupled DFB laser

This paper reports that the complex-coupled distributed feedback laser with the sampled grating has been designed and fabricated.The ＋1st order reflection of the sampled grating is utilized for laser single mode operation,which is 1.5387 μm in the experiment.The typical threshold current of the device is 30 mA,and the optical output power is about 10 mW at the injection current of 100 mA.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

Simultaneous Strain and Temperature Measurement Using Single High-duty-cycle Sampled Fiber Bragg Grating

A novel and simple fiber grating sensor based on high-duty-cyclesample fiber bragg grating is proposed and demonstratedexperimentally. This type of sensor can measure strain andtemperature simultaneously with merits of low cost, high sensitivityand immunity to electro- magnetic interference. The sensor has anaccuracy of 20 με and 0.8 deg.C over a strain range of 500～1 500 με and a temperature range of 5～36 deg.C under experimentalconditions.

Dynamic OADM Using Tunable Sampled Bragg Gratings Based on Vernier Effect

A novel dynamic optical add-drop multiplexer(OADM) scheme using tunable sampled fiber Bragg gratings(SFBG) by Vernier effect is firstly proposed. In this scheme, broad wavelength tuning range is achieved by only slightly adjusting the SFBG, the wavelength tuning of OADM will be faster than traditional dynamic OADM.

Analysis of Sampled Fiber Bragg Grating Based on the Photonic Crystal Theory

One of the most basic characteristics of photonic crystal is frequency band gap. When defects are introduced into the periodic photonic crystal, a number of defect modes appear in the stop band. In this paper, we exploit transfer matrix method based on photonic crystal theory, and assume the sampled fiber Bragg grating as one-dimensional dual photonic crystal with a large size defect. Characteristics of the sampled fiber Bragg grating are analyzed. Experimental results show that the sampled fiber Bragg grating has many reflective peaks. Its reflectivity, center wavelength, reflective peak intervals and band width all change with the grating parameters, including grating length, duty ratio of the material with high dielectric constant, and index modulation depth and period. Results agree with the conventional couple mode theory which can be used when analyzing other characteristics of the sampled fiber Bragg grating or applying it into practice.

Simulation and Fabrication of Sampled Fiber Bragg Grating

The calculation principle of sampled FBG, which is called transfer matrix method, is analyzed and the simulation software is designed. The sampled FBG is simulated with the software we designed and is fabricated by ourselves.The experiment result fits well with the simulated one.It is concluded that to improve the refractive modulation index is an effective way to fabricate a sampled FBG with high-reflectivity.

Process modeling and optimizing control based on sparse nonuniformly sampled data

In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.

Multiwavelength Actively Mode-locked Fiber Laser with a Double-ring Configuration and Integrated Cascaded Sampled Fiber Bragg Gratings

A modified multiwavelength actively mode-locked fiber ring laser is proposed and experimentally demonstrated. In this kind of laser, stable multiwavelengths lasing is achieved by integrating cascaded sampled fiber Bragg gratings(SFBGs) into the laser cavity. To implement actively mode-locking technique, a double-ring cavity configuration is used to assure that the cavity lengths for all wavelengths lasing are identical. Thus, simultaneous mode locking of all wavelengths has been successfully achieved by using the same mode-locking signal.

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.