Relay based identification of processes with time delay

General analytical expressions are derived from the analysis of half cycle of the relay response curve for identification of a class of processes with time delay. Using these expressions the parameters of open loop stable, unstable and integrating transfer function models may be estimated making simple measurements on the half cycle of the oscillation. For the first time, the time delay parameter of the process model is calculated from the second derivative of the half cycle data. Models with up to four parameters can be identified by means of a single symmetrical relay experiment. The robustness of the technique is assessed against two forms of introduced error: measurement errors and load disturbances. The limit cycle data in the presence of measurement noise and load disturbance are obtained using the Fourier series based curve fitting with the options of non-linear least squares method and the resetting of the relay height, respectively. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for identification of processes with time delay.     

Design and Construction of a Helical Resonator for Delivering Radio Frequency to an Ion Trap

Supplying high voltage radio frequency (RF) is a critical part of ion trapping system due to impedance mismatching between RF source and the ion trap. A helical resonator has been constructed in order to deliver narrow bandwidth and high voltage RF to the ion trap for stable confinement of ions. The performances of the helical resonator have been studied for different capacitive load of the ion trap. Both the resonant frequency and quality factor of the resonator show strong dependence on external capacitive loads.     

Adaptive threshold selection for impulsive noise detection in images using coefficient of variance

This paper proposes an adaptive threshold selection strategy to detect impulsive noise in images. The proposed method utilizes a simple neural network with statistical characteristics of noisy images. The method is adaptive in the sense that the threshold obtained is adaptable to different type of images and noise conditions. The network tuned for one image works for other images as well at different noise conditions. Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of noise suppression.     

Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique

The paper introduces a novel method of adaptive robust identification of complex nonlinear dynamic plants including Box Jenkin, Mackey Glass and Sunspot series under the presence of strong outliers in the training samples. The identification model consists of a low complexity single layer functional link artificial neural network (FLANN) in the feed forward path and another on the feedback path. The connecting weights are iteratively adjusted by a population based particle swarm optimization technique so that a robust cost function (RCF) of the model-error is minimized. To demonstrate robust identification performance up to 50% random samples of the plant output is contaminated with strong outliers and are employed for training the model using PSO tool. Identification of wide varieties of benchmark complex static and dynamic plants is carried out through simulation study and the performance obtained are compared with those obtained from using standard squared error norm as CF. It is in general observed that, the Wilcoxon norm provides best identification performance compared to squared error and other RCFs based models.     

Integral criteria for optimal tuning of PI/PID controllers for integrating processes

Tuning formulas for PI/PID controllers for integrating processes are presented in this paper. The controller parameters are obtained by minimizing various integral performance index. Bacterial Foraging strategy, a new entrant to the family of evolutionary algorithms is used for minimization to avoid the local minima in the optimization procedure. A setpoint filter is used to reduce the large overshoot, and a significant improvement in control performance is obtained when compared to recently reported methods. Simulation results for an assumed perturbation in the plant delay are also given to illustrate the robustness of the proposed controller design method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Blind symbol timing offset estimation for offset‐QPSK modulated signals

In this paper, a blind symbol timing offset (STO) estimation method is proposed for offset quadrature phase‐shift keying (OQPSK) modulated signals, which also works for other linearly modulated signals (LMS) such as binary‐PSK, QPSK, π/4‐QPSK, and minimum‐shift keying. There are various methods available for blind STO estimation of LMS; however, none work in the case of OQPSK modulated signals. The popular cyclic correlation method fails to estimate STO for OQPSK signals, as the offset present between the in‐phase (I) and quadrature (Q) components causes the cyclic peak to disappear at the symbol rate frequency. In the proposed method, a set of close and approximate offsets is used to compensate the offset between the I and Q components of the received OQPSK signal. The STO in the time domain is represented as a phase in the cyclic frequency domain. The STO is therefore calculated by obtaining the phase of the cyclic peak at the symbol rate frequency. The method is validated through extensive theoretical study, simulation, and testbed implementation. The proposed estimation method exhibits robust performance in the presence of unknown carrier phase offset and frequency offset.     

Dynamics on higher-order networks: a review

Network science has evolved into an indispensable platform for studying complex systems. But recent research has identified limits of classical networks, where links connect pairs of nodes, to comprehensively describe group interactions. Higher-order networks, where a link can connect more than two nodes, have therefore emerged as a new frontier in network science. Since group interactions are common in social, biological and technological systems, higher-order networks have recently led to important new discoveries across many fields of research. Here, we review these works, focusing in particular on the novel aspects of the dynamics that emerges on higher-order networks. We cover a variety of dynamical processes that have thus far been studied, including different synchronization phenomena, contagion processes, the evolution of cooperation and consensus formation. We also outline open challenges and promising directions for future research.     

Limit cycle-based exact estimation of FOPDT process parameters under input/output disturbances: a state-space approach

A set of novel explicit expressions for the identification of stable, unstable and integrating first-order plus dead time process dynamics is presented. In the absence of sensor noise/static load disturbances, an autonomous relay control system with an asymmetrical relay induces a smooth limit cycle at the process output. Dynamic model parameters are estimated using the set of proposed expressions which depend on the parameters of limit cycle output and its derivatives. However, in practice, the process output is generally corrupted by the measurement noise, thereby rendering an erroneous identification of process dynamics. Furthermore, static load disturbance during an identification test also induces an asymmetrical limit cycle output resulting in inaccurate measurements. Hence, a fast Fourier transform technique and biased relay feedback methods are implemented to obviate the problem of asymmetries and chattering in the limit cycle output yielding the original limit cycle and its subsequent derivatives. The proposed method has been validated, considering five typical examples from literature. An extensive comparison study with existing approaches based on Nyquist plots demonstrate the efficacy of the method presented.     

Flicker-Noise Improvement in 100-nm $L_{g} hbox{Si}_{0.50}hbox{Ge}_{0.50}$ Strained Quantum-Well Transistors Using Ultrathin Si Cap Layer

This letter presents a record low flicker-noise spectral density in biaxial compressively strained p-channel 100-nm L_gSi_0.50Ge_0.50 quantum-well FETs (QWFETs) with ultrathin Si (~2 nm) barrier layer and 1-nm EOT hafnium silicate gate dielectric. The normalized power spectral density of I_d fluctuations (S_Id/I_d ²) in Si_0.50Ge_0.50 QWFETs exhibits significant improvement by ten times over surface channel unstrained Si pMOSFETs at high V_g due to strong confinement of holes within the high-mobility QW and strong quantization in the ultrathin Si barrier layer enabled by low-thermal-budget device processing. The noise behavior in strained QW devices is found to evolve from being correlated mobility fluctuation dominated across most of V_g range to being Hooge mobility fluctuation dominated at very high V_g.