Volume 11, Number 6, 2014
Special Issue on Recent Advances on Complex Systems Control, Modelling and Prediction II (pp.571-620)
The analysis of stability and numerical simulation of Costas loop circuits for the high-frequency signals is a challenging task. The problem lies in the fact that it is necessary to observe very fast time scale of input signals and slow time scale of signal's phases simultaneously. To overcome this difficulty, it is possible to follow the classical ideas of Gardner and Viterbi to construct a mathematical model of Costas loop, in which only slow time change of signal's phases and frequencies is considered. Such an construction, in turn, requires the computation of phase detector characteristic, depending on the waveforms of the considered signals. In this paper, the problems of nonlinear analysis of Costas loops and the approaches to the simulation of the classical Costas loop, the quadrature phase shift keying (QPSK) Costas loop, and the two-phase Costas loop are discussed. The analytical method for the computation of phase detector characteristics of Costas loops is described.
This paper presents the starting project of a web site focussed on unstable systems. It is a web-based database in a bilingual version (English/Czech), which can be used as an information database for models of unstable processes. The web site contains the mathematical models of such systems, including their simulation files together with basic information about the stability of dynamic systems. The paper outlines the motivation for the development of this database, presents its basic structure, and discusses several models from the site. The areas of prospective usage are also suggested together with the possible directions of further development of this project. The contribution ends with a case study using the database for control system analysis and design of the Amira inverted pendulum. The systematic polynomial approach is fruitfully utilised for the task together with some useful tools from the robust control theory.
We introduce the paradigm of chaotic mathematical circuitry which shows some similarity to the paradigm of electronic circuitry, especially in the frame of chaotic attractors for solving practical problems (generating hyperchaos; developing chaos based pseudo random number generator (CPRNG) and chaotic multistream PRNG; secure communication via synchronization). They can also be used in cryptography, generic algorithms in optimization, control, etc.
This paper deals with the design of a nonlinear observer for sensorless induction motor control. Based upon the circle criterion approach, a nonlinear observer is designed to estimate pertinent but unmeasurable state variables of the considered induction machine for sensorless control purpose. The observer gain matrices are computed as a solution of linear matrix inequalities (LMI) that ensure the stability conditions of the state observer error dynamics in the sense of Lyapunov concepts. Measured and estimated state variables can be exploited to perform a state feedback control of the machine system. The simulation results are presented to illustrate the effectiveness of the proposed approach for nonlinear observer design.
This paper studies the problem of tracking control for a class of switched nonlinear systems with time-varying delay. Based on the average dwell-time and piecewise Lyapunov functional methods, a new exponential stability criterion is obtained for the switched nonlinear systems. The designed output feedback H∞ controller can be obtained by solving a set of linear matrix inequalities (LMIs). Moreover, the proposed method does not need that a common Lyapunov function exists for the switched systems, and the switching signal just depends on time. A simulation example is provided to demonstrate the effectiveness of the proposed design scheme.
This paper presents overview of new features so far prepared for new version of spectral analysis tool SPLAT-VO that allows to retrieve a large amount of spectra (and other data) based on its characteristics by detailed querying a virtual observatory's resources. The overview is focused on enhancements of user experience, work with simple application messaging protocol (SAMP) and other interoperability that improves work with global list of spectra, plot window and analysis menu.
In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.
Piezoelectric actuators fundamentally possess hysteresis behavior. Estimation of the hysteresis is usually demanded for enhancing the performance of piezo-actuated systems. This paper presents an observer-based scheme to estimate the hysteresis in piezo—actuated flexible beams. The observer is based on a nonlinearity observer method. The discrete-time Kalman-filter algorithm is adopted for determination of the observer gains. The major advantages of the presented scheme include ease of implementation and robustness to uncertainty of hysteresis parameters. Simulation results demonstrate that the observer is able to estimate the hysteresis efficiently and has better robustness compared to the previous scheme existing in the literature. The present scheme was also successfully applied to a real-life system. Moreover, a control application example is included to demonstrate the effectiveness of the scheme.
Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer (D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals' measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed, and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally, the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.
The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models' base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.
The stabilization problem for a class of linear continuous-time systems with time-varying non differentiable delay is solved while imposing positivity in closed-loop. In particular, the synthesis of state-feedback controllers is studied by giving sufficient conditions in terms of linear matrix inequalities (LMIs). The obtained results are then extended to systems with non positive delay matrix by applying a memory controller. The effectiveness of the proposed method is shown by using numerical examples.
Linear quadratic regulator (LQR) and proportional-integral-derivative (PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions. The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.
In this paper, an analytical model for computing the resonant frequency of the gap-coupled ring microstrip patch antennas is developed. The analytical model is based upon the cavity model along with circuit theory. Using the field expressions and boundary conditions, the transcendental equation for the structure is developed. The analytically computed results are compared with the simulated results. The simulation work is carried out by using computer simulation technology (CST) microwave studio simulator. The comparison between simulated and computed results shows good agreement.
In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms (also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square (LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio (SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.