Parameter estimation of integrating and time delay processes using single relay feedback test

Autotuning using relay feedback is widely used to identify low order integrating plus dead time (IPDT) systems as the method is simple and is operated in closed-loop without interrupting the production process. Oscillatory responses from the process due to ideal relay input are collected to calculate ultimate properties of the system that in turn are used to model the responses as functions of system model parameters. These theoretical models of relay response are validated. After adjusting the phase shift, input and output responses are used to find land mark points that are used to formulate algorithms for parameter estimation of the process model. The method is even applicable to distorted relay responses due to load disturbance or measurement noise. Closed-loop simulations are carried out using model based control strategy and performances are calculated.     

ADC characterization by using the histogram test stimulated by Gaussian noise: Theory and experimental results

This paper presents results relating to the measurement of differential and integral nonlinearity of ADCs using the histogram method with white Gaussian noise as the stimulus signal. We specify the optimum noise power of the generator, in the sense of number of samples minimisation, as a function of the converter range and resolution. An expression of tolerance interval as a function of the number of samples acquired given a certain confidence level is presented both for the determination of transition levels (INL) and quantization intervals (DNL). Experimental and simulation results concerning the characterization of a 12-bit PC acquisition board are shown.     

Identification of a class of Wiener and Hammerstein-type nonlinear processes with monotonic static gains

In this paper a non-iterative approach to identifying Wiener and Hammerstein models, including model structure and parameters, is proposed. A single symmetrical relay test is conducted to determine the structure and then the parameters of the block-oriented nonlinear model possessing a static nonlinearity and a linear process in cascade. The static nonlinearity block is represented by a memoryless and monotonic function and the linear process by a second order transfer function model. A relay with hysteresis induces the limit cycle output signal and one cycle data of the output signal is used to identify the block-oriented nonlinear model. The proposed identification method is simple and gives better performance than previous methods for processes with static nonlinearity.     

Identification and Control of Bioreactor using Recurrent Networks

Abstract

A nonlinear model predictive control (NMPC) strategy based on recurrent neural networks (RNN) is proposed for a single‐input single‐output system (SISO) to control the uncertain nonlinear process. The automatic configuration and modeling of the networks is carried out using a recurrent Elman network using back propagation through time (BPTT) with MATLAB. Identification of the process is performed with a RNN based nonlinear autoregressive with exogenous input (NARX) model and the incorporation of the developed model in the formulation of NMPC is presented. Further, the results of the NMPC is compared with a well tuned IMC based PI controller, which shows a better performance based on the rise time and settling time of the proposed NMPC scheme for the control of an unstable bioreactor.     

Identification of yield stress and plastic hardening parameters from a spherical indentation test

Assuming plastic hardening of metals are specified by the stress–strain curve in the form , the material parameters σ₀, k and m are identified from spherical indentation tests by measuring compliance moduli in loading and unloading of the load–penetration curve. The curve P(h_p) is analytically described by a two term expression, each with different exponents. Here, ε_p and h_p denote the plastic strain and permanent penetration. The proposed identification method is illustrated by specific examples including numerical and physical identification tests.     

Calibration of laser Doppler vibrometer and laser interferometers in high-frequency regions using electro-optical modulator

We proposed a new primary calibration method of a laser Doppler vibrometer (LDV) using the optical modulated excitation of an electro-optical modulator and validated this approach by comparing mechanical modulated excitation with homodyne laser interferometer at the National Metrology Institute of Japan (NMIJ). The velocity sensitivity of the LDV was evaluated with the uncertainty budget at a high-frequency range up to 1 MHz, which is indicated as the upper limit of 50 kHz in ISO (International Organization for Standardization) 16063–41. As the results, we confirmed that velocity sensitivities between optical and mechanical modulated excitations are flat within their uncertainty from 100 Hz up to 1 MHz. Moreover, in order to evaluate the accurate phase shift of the accelerometer up to 20 kHz using optical modulated excitation, we also revealed the reliability of two time shifts between homodyne and heterodyne laser interferometers.     

Accelerated life testing of nano ceramic capacitors and capacitor test boards using non-parametric method

Engineers are searching for a reliable method to determine the time-to-failure (TTF) data of the electronic systems as cheaper as possible. Reliability plays a vital role in electronic devices marketing & sales, product quality, etc. Accelerated Life Test (ALT) and Highly Accelerated Life Testing (HALT) are the latest methodology in the field of life testing of engineering systems. The ALT can be conducted at higher stress level to generate more failure data within short duration of time. The parametric method and non-parametric method are used to convert the accelerated test condition to actual condition. In this paper, the most widely used C0G and X7R nano ceramic capacitor is selected to generate the time-to failure data at accelerated condition and non-parametric method is used to convert the accelerated condition data into actual condition.     

Identification of motion platform using the signal compression method with pre-processor and its application to siding mode control

In case of a single input single output (SISO) system with a nonlinear term, a signal compression method is useful to identify a system because the equivalent impulse response of linear part from the system can be extracted by the method. However even though the signal compression method is useful to estimate uncertain parameters of the system, the method cannot be directly applied to a unique system with hysteresis characteristics because it cannot estimate all of the two different dynamic properties according to its motion direction. This paper proposes a signal compression method with a pre-processor to identify a unique system with two different dynamics according to its motion direction. The pre-processor plays a role of separating expansion and retraction properties from the system with hysteresis characteristics. For evaluating performance of the proposed approach, a simulation to estimate the assumed unknown parameters for an arbitrary known model is carried out. A motion platform with several single-rod cylinders is a representative unique system with two different dynamics, because each single-rod cylinder has expansion and retraction dynamic properties according to its motion direction. The nominal constant parameters of the motion platform are experimentally identified by using the proposed method. As its application, the identified parameters are applied to a design of a sliding mode controller for the simulator.     

Gear fault identification and localization using analytic wavelet transform of vibration signal

The aim of this present work is to identify and localize the defect in gear and measure the angle between two damaged teeth in the time domain of the vibration signal. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. The enveloping technique is revisited for defect identification but is found unsatisfactory in measuring the angle between two faulty teeth. A signal processing scheme is proposed to filter the noise and to measure the angle between two damaged teeth. The proposed technique consists of undecimated wavelet transform (UWT), which is used to denoise the signal. The analytic wavelet transform (AWT) has been implemented on approximation signal followed by a time marginal integration (TMI) of the AWT scalogram. The TMI graph time-axis is mapped onto the angular displacement of the driver gear. The measurement is shown to identify the first and the second defective teeth impact on gear meshing, which is visible as sharp spikes in the TMI graph. An attempt is also made to replace the approximation from UWT with Intrinsic Mode Function (IMF) derived from the Empirical Mode Decomposition (EMD). The present experimental work establishes the proposed method of measuring and localizing multiple gear teeth defect using vibration signal in the time domain.