Establishing formal state space models via quantization forquantum control systems

Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.

Application of the state space model to the system of wave energy conversion Analytical method for wave forces on singleoscillating rectangular buoy

A new analytical method is proposed to analyze the force acting on a rectangular oscillating buoy due to linear waves.In the method a new analytical expression for the diffraction velocity potential is obtained first by use of theeigenfunction expansion method and then the wave excitation force is calculated by use of the known incident wavepotential and the diffraction potential. Compared with the classical analytical method, it can be seen that the presentmethod is simpler for a two-dimensional problem due to the comparable effort needed for the computation ofdiffraction potential and for that of radiated potential. To verify the correctness of the method, a classical example inthe reference is recomputed and the obtained results are in good accordance with those by use of other methods,which shows that the present method is correct.

Design Criterion for Fixture Layout Based on the State Space Model in M ultistation Assembly Processes

The fixture layout is crucial to assure the product quality in a multistation assembly process （MAP）. A well-designed fixture layout will make the final product＇s variability be insensitive to the fixture variation inputs. As the basis of the fixture layout design, the design criterion plays an important role in the effectiveness of a solution and the optimization efficiency. In this paper, an effective and efficient design criterion is proposed for the fixture layout with a fixed reference point （FRP） in an MAP. First of all, a state space model for the individual port＇s variation propagation and accumulation is developed, which is the mathematical foundation of the proposed criterion. Then, based on this model, a novel design criterion used to evaluate the performance of the fixture layout is proposed for the fixture layout with an FRP. Finally, a method extracted from the proposed design criterion is developed for quick fixture layout design. A four-station assembly process is used to validate the effectiveness and efficiency of the proposed models and methods.

Quantum-dot Semiconductor Optical Amplifiers in State Space Model

A state space model(SSM) is derived for quantum-dot semiconductor optical amplifiers(QD-SOAs).Rate equations of QD-SOA are formulated in the form of state update equations,where average occupation probabilities along QD-SOA cavity are considered as state variables of the system.Simulations show that SSM calculates QD-SOA′s static and dynamic characteristics with high accuracy.

Deviation Diagnosis and Analysis of Hull Flat Block Assembly Based on a State Space Model

Dimensional control is one of the most important challenges in the shipbuilding industry. In order to predict assembly dimensional variation in hull flat block construction, a variation stream model based on state space was presented in this paper which can be further applied to accuracy control in shipbuilding. Part accumulative error, locating error, and welding deformation were taken into consideration in this model, and variation propagation mechanisms and the accumulative rule in the assembly process were analyzed. Then, a model was developed to describe the variation propagation throughout the assembly process. Finally, an example of fiat block construction from an actual shipyard was given. The result shows that this method is effective and useful.

State Space Modeling and Implementation of a New Transformer Based Multilevel Inverter Topology with Reduced Switch Count

This paper presents a new transformer based multilevel inverter, with a novel pulse width modulation scheme to achieve seven-level inverter output voltage. The proposed inverter switching pattern consists of three fundamental frequency sinusoidal reference signals with an offset value, and one high frequency triangular carrier signal. This switching scheme has been implemented using an 8-bit Xilinx SPARTAN-3E field programmable gate array based controller. In addition, the state space model of the proposed inverter is developed. The significant features of the proposed topology are: reduction of the power switch count and the gate drive power supply unit, the provision of a galvanic isolation between load and sources by a centre tap transformer. An exhaustive comparison has been made of the existing multilevel inverter topologies and the proposed topology. The performances of the proposed topology with resistive, resistive-inductive loads are simulated in a MATLAB environment and validated experimentally on a laboratory prototype.

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

Reliability Prediction Method Based on State Space Model for Rolling Element Bearing

Reliability analysis based on equipment's performance degradation characteristics is one of the significant research areas in reliability research. Nowadays, many researches are carried on multi-sample analysis. But it is limited for a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model(SSM) is proposed in this research. Feature energy of the monitored signals is extracted with the wavelet packet analysis and the associated frequency band energy with online monitored data. Then,degradation feature is improved by moving average filtering processing taken as input pair model parameter of SSM to be estimated. In the end, state space predicting model of degradation index is established. The probability density distribution of the degradation index is predicted, and the degree of reliability is calculated. A real testing example of bearing is used to demonstrate the rationality and effectiveness of this method. It is a useful method for single sample reliability prediction.

A new online modelling method for aircraft engine state space model

To overcome the drawbacks of current modelling method for aircraft engine state space model,a new method is introduced.The form of state space model is derived by using Talyor series to expand the nonlinear model that is implicit equations and involves many iterations.A partial derivative calculation method for iterations is developed to handle the influence of iterations on parameters.The derivative calculation and the aerothermodynamics calculations are combined in the component level model with fixed number Newton-Raphson(N-R)iterations.Mathematical derivation and simulations show the convergence ability of proposed method.Simulations show that comparing with the linear parameter varying model and centered difference based state space model,much higher accuracy of proposed online modelling method is achieved.The accuracy of the state space model built by proposed method can be maintained when the step amplitudes of inputs are within 2%,and the responses of the state space model can match those of the component level model when each input steps larger amplitudes.In addition,an online verification was carried out to show the capability of modelling at any operating point and that state space model can predict future outputs accurately.Thus,the effectiveness of the proposed method is demonstrated.

Monte Carlo Likelihood Estimation of Mixed-Effects State Space Models with Application to HIV Dynamics

The statistical inference for generalized mixed-effects state space models （MESSM） are investigated when the random effects are unknown. Two filtering algorithms are designed both of which are based on mixture Kalman filter. These algorithms are particularly useful when the longitudinal ts are sparse. The authors also propose a globally convergent algorithm for parameter estimation of MESSM which can be used to locate the initial value of parameters for local while more efficient algorithms. Simulation examples are carried out which validate the efficacy of the proposed approaches. A data set from the clinical trial is investigated and a smaller mean square error is achieved compared to the existing results in literatures.

Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action

Since time-course microarrav data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

A Study on the Factors Influencing the Income Gap between Urban and Rural Areas Based on State-space Model

The increasingly widening income gap between urban and rural areas is affected by many factors. Using the stepwise regression analysis,we find that urbanization level,socio-economic development,education level,financial development scale and financial development efficiency have the greatest impact on the income gap between urban and rural areas. By cointegration test,it is found that there is a long-term equilibrium relationship between these five variables and the income gap between urban and rural areas. We build the state-space model to research the dynamic impact of these factors on the income gap between urban and rural areas. The results show that by improving the level of urbanization,we can effectively narrow the income gap between urban and rural areas,while socio-economic development,the improvement of education level,expansion of financial development scale and financial development efficiency all significantly expand the income gap between urban and rural areas.

A Generalized State Space Average Model for Parallel DC-to-DC Converters

The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.

Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.

Stiffness Characteristics of a Basic Nonlinear Air Spring Model

This study predicts the characteristics of a compressible polytropic air spring model. A second-order nonlinear autonomous air spring model is presented. The proposed model is based on the assumption that polytropic processes occur. Isothermal and isentropic compression and expansion of the air within the spring chambers are the two scenarios that are taken into consideration. In these situations, the air inside the spring chambers compresses and expands, resulting in nonlinear spring restoring forces. The MATLAB/Simulink software environment is used to build a numerical simulation model for the dynamic behavior of the air spring. To quantify the values of the stiffnesses of the proposed models, a numerical solution is run over time for various values of the design parameters. The isentropic process case has a higher dynamic air spring stiffness than the isothermal process case, according to the results. The size of the air spring chamber and the area of the air spring piston influence the air spring stiffness in both situations. It is demonstrated that the stiffness of the air spring increases linearly with increasing piston area and decreases nonlinearly with increasing air chamber length. As long as the ratio of the vibration’s amplitude to the air spring’s chamber length is small, there is good agreement in both scenarios between the linearized model and the full nonlinear model. This implies that linear modeling is a reasonable approximation of the complete nonlinear model in this particular scenario.

Model-Following Designs Using Direct State Derivative Measurement Feedback in Novel Reciprocal State Space Form

The paper introduces effective and straightforward algorithms of both explicit and implicit model-following designs with state derivative measurement feedback in novel reciprocal state space form (RSS) to handle state derivative related performance output and state related performance output design cases. Applying proposed algorithms, no integrators are required. Consequently, implementation is simple and low-cost. Simulation has also been carried out to verify the proposed algorithms. Since acceleration can only be modeled as state derivative in state space form and micro-accelerometer which is the state derivative sensor is getting more and more attentions in many microelectromechanical and nanoelectromechanical systems (MEMS/NEMS) applications, the proposed algorithms are suitable for MEMS/NEMS systems installed with micro-accelerometers.

State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.