A comparison of two approaches to utilizing XML in parametric databases for temporal data

The parametric data model captures an object in terms of a single tuple. This feature eliminates unnecessary self-join operations to combine tuples scattered in a temporal relation. Despite this advantage, this model is relatively difficult to implement on top of relational databases because the sizes of attributes are unfixed. Since data boundaries are not problematic in XML, XML can be an elegant solution to implement parametric databases for temporal data. There are two approaches to implementing parametric databases using XML: (1) a native XML database with XQuery engine, and (2) an XML storage with a temporal query language. To determine which approach is appropriate in parametric databases, we consider four questions: the effectiveness of XML in modeling temporal data, the applicability of XML query languages, the user-friendliness of the query languages, and system performances of two approaches. By evaluating the four questions, we show that the latter approach is more appropriate to utilizing XML in parametric databases.     

A performance comparison between two design techniques for non-linear output feedback control

For a system possessing a non-linear output feedback normal form, an observer backstepping design is compared to a high gain observer design with respect to non-singular performance cost functional. If the initial error between the initial condition of the state and the initial condition of the observer is large, the high gain observer design is shown to have better performance than the observer backstepping design. An output feedback system with parametric uncertainty is then considered. It is shown that if an a priori estimate for the bound of the uncertain parameter is conservative, then an adaptive observer backstepping design has better performance than the adaptive high gain observer design.     

Multiple model self-tuning control for a class of nonlinear systems

This study develops a novel nonlinear multiple model self-tuning control method for a class of nonlinear discrete-time systems. An increment system model and a modified robust adaptive law are proposed to expand the application range, thus eliminating the assumption that either the nonlinear term of the nonlinear system or its differential term is global-bounded. The nonlinear self-tuning control method can address the situation wherein the nonlinear system is not subject to a globally uniformly asymptotically stable zero dynamics by incorporating the pole-placement scheme. A novel, nonlinear control structure based on this scheme is presented to improve control precision. Stability and convergence can be confirmed when the proposed multiple model self-tuning control method is applied. Furthermore, simulation results demonstrate the effectiveness of the proposed method.     

Robustness of non-linear stochastic optimal control for quasi-Hamiltonian systems with parametric uncertainty

The robustness of non-linear stochastic optimal control for quasi-Hamiltonian systems with uncertain parameters is studied. Based on the independence of uncertain parameters and stochastic excitations, the non-linear stochastic optimal control for the nominal quasi-Hamiltonian system with average-value parameters is first obtained by using the stochastic averaging method and stochastic dynamical programming principle. Then, the means and standard deviations of root-mean-square responses, control effectiveness and control efficiency for the uncertain quasi-Hamiltonian system are calculated by using the stochastic averaging method and the probabilistic analysis. By introducing the sensitivity of the variation coefficients of controlled root-mean-square responses, control effectiveness and control efficiency to those of uncertain parameters, the robustness of the non-linear stochastic optimal control is evaluated. Two examples are given to illustrate the proposed control procedure and its robustness.     

Adaptive control for a class of large scale non-linear systems

The design of stabilizing controllers for multi-input multi-output (MIMO) non-linear plants with unknown non-linearities is a challenging problem. The inability to identify the non-linearities on-line or off-line accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant non-linearities. However, the design of a centralized controller for large scale non-linear systems is often complex due to the high dimensionality and difficulty to implement in real time. In this paper a decentralized-like non-linear adaptive control algorithm is designed and analysed for a class of large scale non-linear systems with unknown non-linearities. The controller guarantees closed loop semi-global stability and convergence of the tracking error to a small residual set whose size can be specified a priori provided the neglected in the control design non-linear interconnections are small relative to the modelled non-linear parts. A procedure for choosing the various design parameters to guarantee that the tracking error bound will converge to within the specified a priori bound is presented. Even though the proposed controller is not purely decentralized it does reduce computations and makes the control design easier than a corresponding centralized approach.     

Adaptive non-linear compensation control based on neural networks for non-linear systems with time delay

This article investigates a new adaptive non-linear compensation controller for a class of time-delay non-linear systems with partly known dynamics. First, a non-linear neural-network(NN)-based identification model that includes a prior knowledge about the plant dynamics is discussed by using the approximation capabilities of NNs. Then, the adaptive non-linear compensation controller is developed to produce the desired tracking performance. The proposed controller based on the NN can reduce the effect of modelling uncertainties and provide the time-delay compensation, while stability of the closed-loop system is guaranteed. The effectiveness of the proposed scheme is demonstrated through the application to the control of a continuous stirred tank reactor.     

Approximate model matching for non-linear control systems

Approximate model matching refers to the problem of controlling a non-linear system so as to achieve a response resembling that of a desirable model. The paper presents a family of recursive output feedback controllers that can achieve approximate model matching in all cases where it is possible. The design of these controllers depends on the solution of a set of algebraic inequalities.     

Pairwise testing for software product lines: comparison of two approaches

Software Product Lines (SPL) are difficult to validate due to combinatorics induced by variability, which in turn leads to combinatorial explosion of the number of derivable products. Exhaustive testing in such a large products space is hardly feasible. Hence, one possible option is to test SPLs by generating test configurations that cover all possible t feature interactions (t-wise). It dramatically reduces the number of test products while ensuring reasonable SPL coverage. In this paper, we report our experience on applying t-wise techniques for SPL with two independent toolsets developed by the authors. One focuses on generality and splits the generation problem according to strategies. The other emphasizes providing efficient generation. To evaluate the respective merits of the approaches, measures such as the number of generated test configurations and the similarity between them are provided. By applying these measures, we were able to derive useful insights for pairwise and t-wise testing of product lines.     

Adaptive neural network tracking control for a class of non-linear systems

This article extends the application of the adaptive neural network control to a new class of uncertain MIMO non-linear systems, which are composed of interconnected subsystems where each interconnected subsystem is in the non-affine pure-feedback form. Because both the variables which are used as virtual controllers and the actual controllers appear non-linearly in unknown functions of the MIMO systems, thus, this class of systems is difficult to control. The radial basis function neural networks are utilised to approximate the desired virtual controllers and the desired actual controllers which are obtained by using implicit function theorem. The salient property of the proposed approach is that the number of the adjustable parameters is less than the numerous alternative approaches existing in the literature. It is proven that, under appropriate assumptions, all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to a small neighbourhood of the origin by appropriately choosing design parameters. The feasibility of the developed approach is verified by two simulation examples.     

Adaptive neural control for a class of nonlinearly parametric time-delay systems

In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.     

Adaptive model predictive control for a class of constrained linear systems based on the comparison model

This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.     

Adaptive stabilization of non-linear oscillators using direct adaptive control

Direct adaptive controllers developed for linear systems are applied to non-linear oscillators. A wide range of nonlinearities are considered, including stiffness non-linearities, input non-linearities, limit cycle oscillations and friction. Numerical results suggest that by increasing the speed of adaptation, these direct adaptive controllers are highly effective when applied to non-linear plants.     

A comparison of two parametric surface patch methods

The Bézier and Ferguson methods of representing surfaces are compared. Although there are very clear differences in their mode of use they are closely similar in their mathematical basis.     

A comparison of two Bayesian approaches for uncertainty quantification

Statistical calibration of model parameters conditioned on observations is performed in a Bayesian framework by evaluating the joint posterior probability density function (pdf) of the parameters. The posterior pdf is very often inferred by sampling the parameters with Markov Chain Monte Carlo (MCMC) algorithms. Recently, an alternative technique to calculate the so-called Maximal Conditional Posterior Distribution (MCPD) appeared. This technique infers the individual probability distribution of a given parameter under the condition that the other parameters of the model are optimal. Whereas the MCMC approach samples probable draws of the parameters, the MCPD samples the most probable draws when one of the parameters is set at various prescribed values. In this study, the results of a user-friendly MCMC sampler called DREAM_(ZS) and those of the MCPD sampler are compared. The differences between the two approaches are highlighted before running a comparison inferring two analytical distributions with collinearity and multimodality. Then, the performances of both samplers are compared on an artificial multistep outflow experiment from which the soil hydraulic parameters are inferred. The results show that parameter and predictive uncertainties can be accurately assessed with both the MCMC and MCPD approaches.     

Adaptive finite-time control of nonlinear systems with parametric uncertainty

In this note, global finite time stabilization is investigated for a class of nonlinear systems in p normal form with parametric uncertainties. To achieve finite-time stabilization, a constructive control design approach is proposed by following backstepping methodology, and an adaptive finite-time control law is obtained in the form of continuous time-invariant feedback.     

基于非线性奇异子系统模型的同步发电机分散非线性控制

针对基于非线性奇异子系统模型的同步发电机,本文利用反推控制方法,研究了其非线性分散控制问题.同步发电机本质上是电力大系统中的一个非线性奇异子系统,与系统其余部分存存相互约束和影响.首先利用微分同胚变换和状态反馈,实现了同步发电机模型的等价转化.然后在等价系统的楚础上,在充分考虑系统其余部分对于同步发电机影响的前提下,利用反推控制方法设计了同步发电机组的非线性镇定控制器,使得闭环系统是渐近稳定的.最后的仿真结果农明了本文所提控制方法的有效性.     

Adaptive control for the conventional mode of operation of MEMS gyroscopes

This paper presents adaptive add-on control algorithms for the conventional mode of operation of MEMS z-axis gyroscopes. This scheme is realized by adding an outer loop to a conventional force-balancing scheme that includes a parameter estimation algorithm. The parameter adaptation algorithm estimates the angular rate, identifies and compensates the quadrature error, and may permit on-line automatic mode tuning. The convergence and resolution analysis show that the proposed adaptive add-on control scheme prevents the angular rate estimate from being contaminated by the quadrature error, while keeping ideal resolution performance of a conventional force-balancing scheme.     

Adaptive state-feedback stabilization for high-order stochastic non-linear systems with uncertain control coefficients

This paper investigates the adaptive state-feedback stabilization problem for a class of high-order stochastic non-linear systems with unknown lower and supper bounds for uncertain control coefficients. Under some weaker and reasonable assumptions, a smooth adaptive state-feedback controller is designed, which guarantees that the closed-loop system has an almost surely unique solution on [0,∞, the equilibrium of interest is globally stable in probability and the states can be regulated to the origin almost surely. A simulation example is given to show the systematic design and effectiveness of the controller.     

Adaptive feedforward and feedback control of non-linear time-varying uncertain systems

An adaptive regulation scheme is proposed for a class of non-linear time-varying systems with parametric uncertainties. The proposed approach is based upon a combination of the adaptive backstepping design method and a feedforward control scheme to design a non-linear adaptive feedforward and feedback controller, such that robust output tracking can be achieved even in the presence of structured uncertainties, as well as time-varying, measurable disturbances. Although the systematic design procedure does not a priori satisfy the feedback linearizable system with triangular structures, however, the constructed condition must be satisfied to ensure that the control scheme has a stable inversion. Under the feasibility condition, the states of the resulting closed-loop system would be guaranteed boundedness and converge to a bounded set. Finally, the proposed methodology is illustrated by a chemical reactor example.