Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms

In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.     

Computationally lean algorithm of novel optimal FIR adaptive filter for vortex signal extraction

In a vortex flowmeter vortices are generated by a bluff body, inserted in the path of flow, which has a piezoelectric sensor embedded in it. This piezosensor develops a signal having a fundamental frequency that is proportional to flow. The flow measurement relies completely on extraction of true vortex signal and estimation of the correct frequency. A novel adaptive FIR filter has been designed and implemented using low power computational resource (8.25 mW), which gives better results than an existing contemporary system when tested on an industrial flow rig. Further more a comparative study of autocorrelation, EMD Scales filter and proposed algorithm is carried on the good and bad vortex signals. From this comparative study it is seen that proposed algorithm is effective for bad vortex signals and low flowrates where vortex signals are weak.     

Dual mode adaptive fractional order PI controller with feedforward controller based on variable parameter model for quadruple tank process

The Quadruple Tank Process (QTP) is a well-known benchmark of a nonlinear coupled complex MIMO process having both minimum and nonminimum phase characteristics. This paper presents a novel self tuning type Dual Mode Adaptive Fractional Order PI controller along with an Adaptive Feedforward controller for the QTP. The controllers are designed based on a novel Variable Parameter Transfer Function model. The effectiveness of the proposed model and controllers is tested through numerical simulation and experimentation. Results reveal that the proposed controllers work successfully to track the reference signals in all ranges of output. A brief comparison with some of the earlier reported similar works is presented to show that the proposed control scheme has some advantages and better performances than several other similar works.     

A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.     

A new discrete-time robust adaptive time-delay control for a class of uncertain nonlinear strict-feedback systems using sliding mode

This paper presents a new discrete-time adaptive second-order sliding mode control with time delay estimation (TDE) for a class of uncertain nonlinear time-varying strict-feedback systems. The existing researches on time delay control (TDC) are conventionally established based on a stability criterion that is subject to the infinitesimal time delay assumption. Recently, this criterion was rejected and a new criterion was proposed for the development of a controller for systems with fully known dynamics. In this study, this approach is extended to uncertain systems. Specifically, a new criterion is developed for the stability of the TDE-error within an adaptive robust controller design without the infinitesimal time delay assumption. With the proposed adaptive robust control, there is no need for determination of uncertainties upper-bounds. Simulation results illustrate the efficacy of the proposed controller.     

A network approach for modeling and design of agile supply chains using a flexibility construct

Agility can be viewed as a need to encourage the enterprise-wide integration of flexible and core competent resources so as to offer value-added product and services in a volatile competitive environment. Since flexibility is considered a property that provides change capabilities of different enterprise-wide resources and processes in time and cost dimensions, supply chain flexibility can be considered a composite state to enterprise-wide resources to meet agility needs. Enterprise modeling frameworks depicting these composite flexibility states are difficult to model because of the complex and tacit interrelationship among system parameters and also because agility thrives on many business objectives. In view of this, the modeling framework presented in this paper is based on analytical network process (ANP) since this methodology can accommodate the complex and tacit interrelationship among factors affecting enterprise agility. The modeling framework forms a three-level network with the goal of attaining agility from the perspective of market, product, and customer as the actors. The goal depends on substrategies that address the characteristics of the three actors. Each of these substrategies further depends on manufacturing, logistic, sourcing, and information technology (IT) flexibility elements of the enterprise supply chain (SC). The research highlights that, under different environmental conditions, enterprises require synergy among appropriate supply chain flexibilities for practising agility. In the present research, the ANP modeling software tool Super Decisions™ has been used for relative prioritization of the supply chain flexibilities. We demonstrate through sensitivity analysis that dynamic conditions do require adjustments in the enterprise-wide flexibility spectrum.

A new digital background calibration for redundant radix-4 pipelined ADCs by modeling of adaptive filter for linear and nonlinear errors

In this paper a new digital background correction and calibration technique for redundant multi-bit pipeline stages is presented. In this method output voltage of each stage in converter is defined as sum of the ideal product and error signal, which error voltage include of linear non-ideal section or first order error and nonlinearity undesired signal or third order error. Linear error is formed by capacitor mismatch, op-amp offset, comparator offset and finite op-amp gain effects. Nonlinear error is deformed the output voltage depend on the nonlinear results of open loop residue amplifier. Correction begins with separately calculation and cancelation of the nonlinear and linear errors respectively. For calibration of each stage at first step, the nonlinear effects in digital output of backend ADC is eliminated and then by digital modeling of first order analog error the influence of this unfavorable signal is diminished from digital equivalent of input voltage. Therefore for cancelation of non-ideal impairment in each stage a digital filter consist of linear and nonlinear channel in digital domain is designed. The first order and third order coefficients of designed digital function are unknown and should by a pertinent method be estimated simultaneously. Adaptive filter are best choose for this method. Simulation results show that INL/DNL parameters of 14-bit radix-4 pipelined converter are improved from 17LSB/3LSB to 0.45LSB/0.41LSB after calibration. The SNDR/SFDR parameters are increased from 30 dB/36 dB to 83 dB/90 dB.