840 resultados para Control and Optimization


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Thermoforming processes generally employ sheet temperature monitoring as the primary means of process control. In this paper the development of an alternative system that monitors plug force is described. Tests using a prototype device have shown that the force record over a forming cycle creates a unique map of the process operation. Key process features such as the sheet modulus, sheet sag and the timing of the process stages may be readily observed, and the effects of changes in all of the major processing parameters are easily distinguished. Continuous, cycle-to-cycle tests show that the output is consistent and repeatable over a longer time frame, providing the opportunity for development of an on-line process control system. Further testing of the system is proposed.

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Closing feedback loops using an IEEE 802.11b ad hoc wireless communication network incurs many challenges sensitivity to varying channel conditions and lower physical transmission rates tend to limit the bandwidth of the communication channel. Given that the bandwidth usage and control performance are linked, a method of adapting the sampling interval based on an 'a priori', static sampling policy has been proposed and, more significantly, assuring stability in the mean square sense using discrete-time Markov jump linear system theory. Practical issues including current limitations of the 802.11 b protocol, the sampling policy and stability are highlighted. Simulation results on a cart-mounted inverted pendulum show that closed-loop stability can be improved using sample rate adaptation and that the control design criteria can be met in the presence of channel errors and severe channel contention.

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Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented.

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Intense, few-femtosecond pulse technology has enabled studies of the fastest vibrational relaxation processes. The hydrogen group vibrations can be imaged and manipulated using intense infrared pulses. Through numerical simulation, we demonstrate an example of ultrafast coherent control that could be effected with current experimental facilities, and observed using high-resolution time-of-flight spectroscopy. The proposal is a pump-probe-type technique to manipulate the D2+ ion with ultrashort pulse sequences. The simulations presented show that vibrational selection can be achieved through pulse delay. We find that the vibrational system can be purified to a two-level system thus realizing a vibrational qubit. A novel scheme for the selective transfer of population between these two levels, based on a Raman process and conditioned upon the delay time of a second control-pulse is outlined, and may enable quantum encoding with this system.

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This paper presents a new method for calculating the individual generators’ shares in line flows, line losses and loads. The method is described and illustrated on active power flows, but it can be applied in the same way to reactive power flows. Starting from a power flow solution, the line flow matrix is formed. This matrix is used for identifying node types, tracing the power flow from generators downstream to loads, and to determine generators’ participation factors to lines and loads. Neither exhaustive search nor matrix inversion is required. Hence, the method is claimed to be the least computationally demanding amongst all of the similar methods.

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This paper provides an overview of the current field in wireless networks for monitoring and control. Alternative wireless technologies are introduced, together with current typical industrial applications. The focus then shifts to wireless Ethernet and the specialised requirements for wireless networked control systems (WNCS) are discussed. This is followed by a brief look at some current WNCS research, including reduced communication control.

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This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.