Step Response Identification Research Articles

Dynamic control of aluminum morphology by wire and arc additive manufacturing based on image feedback

The stability of the forming layer shape is a critical factor that impacts the final quality of sample morphology in wire and arc additive manufacturing (WAAM). This paper investigates the forming process and control methods to identify ways to optimize the process and improve the quality of the final product. The study aims to enhance the quality and precision of samples produced by the WAAM process by establishing a morphology control method based on image feedback. The focus is on real-time image acquisition using a CCD and simultaneous extraction of forming process parameter characteristics. Using a central composite experimental design, a prediction model is developed to estimate key process parameters and feature sizes of deposits, including deposition height and width. To further analyze the dynamic characteristics of feature sizes of deposited layers, step response identification is conducted using three process parameters as the system input: forming speed, arc current, and wire feeding speed. The experiments are designed to determine the weight of each process parameter and achieve a high level of response speed and precision. The findings indicate that the feature size of the deposited layer is most sensitive to changes in forming speed, followed by wire feeding speed, while arc current has the least impact on feature size. After verification, the monitoring of the deposition height and width was found to be in good agreement with the prediction model, with an accuracy of over 90%. The results of this study can be used for size measurement and optimization of large aviation aluminum alloy components using WAAM technology and to improve the quality of products produced using these processes.

Efficient low-order system identification from low-quality step response data with rank-constrained optimization

In the presence of low-quality industrial process data, generic step response identification methods typically show unsatisfactory performance and heavily rely on manual intervention of technical personnel. This erects obvious obstacles for the advancement of intelligent manufacturing in process industries. To address these challenges, we propose a novel rank-constrained optimization approach to low-order system identification from step response data, which yields much more accurate and robust estimates than existing modeling methods. By exploiting the inherent low-rank structure of the Hankel matrix of ideal step response, parameters of a low-order process can be accurately recovered by solving a rank-constrained program, which effectively bypasses the two-step procedure in some state-of-the-art algorithms involving significant error accumulation. The alternating direction method of multipliers is adopted to effectively solve the nonconvex error minimization problem and circumvent poor local optima. Case studies on both numerical examples and industrial datasets demonstrate that, the proposed method not only gives much better modeling accuracy, but also secures reliable and robust estimates even for raw low-quality industrial data. This is particularly helpful for automated execution of the identification routine without human intervention, with success percentage over 99% that is remarkably higher than the state-of-the-art.

Heating-up control with delay-free output prediction for industrial jacketed reactors based on step response identification

To improve the heating-up control performance for industrial jacketed reactors typically involved with long input delay, a predictor-based two-degree-of-freedom (2DOF) control scheme is proposed for the heating-up process operation, based on model identification from a practical step response test. By performing a simple step test (i.e., turning on the electrical heater with certain power (e.g., 50% or 100% of the heating power) to observe the heating-up temperature response), a transfer function identification method is developed to establish an integrating type process model with time delay for describing fundamental dynamics of the heating-up response. Based on the identified model, a predictor-based 2DOF control structure is established, where the delay-free output prediction is obtained by constructing two filters. The 2DOF controllers are analytically derived by proposing the desired transfer functions for the set-point tracking and load disturbance rejection, respectively. There is a single adjustable parameter in each controller (or filter), which may be monotonically tuned to obtain the desired control (or prediction) performance. Illustrative examples from the existing references and a practical application to a 4-liter jacketed reactor for pharmaceutical crystallization are given to demonstrate the effectiveness and advantage of the proposed identification and control methods in comparison with the existing methods.

Gradient-based step response identification of overdamped processes with time delay

In this paper, a step response identification method is proposed for overdamped industrial processes with time delay from sampled data by developing a gradient searching approach to minimize the output prediction error. Based on establishing a least-squares fitting of the time domain expression of a low-order process model, i.e. first-order-plus-dead-time (FOPDT) or second-order-plus-dead-time (SOPDT), with respect to a step change, the rational model parameters together with the delay parameter can be simultaneously estimated, while the computation effort can be significantly reduced compared to the existing step identification methods based on using the time integral approach for model fitting. Both cases of repetitive poles and distinct poles are considered for the identification of an SOPDT model, along with a guideline for a suitable choice of the model structure for practical applications. The convergence and accuracy of the proposed algorithms are analysed with a strict proof. Four illustrative examples from recent references are used to show the effectiveness of the proposed method.

Step Response Identification of a Quadcopter UAV Using Frequency-sampling Filters

This paper presents an approach to identify a quadcopter's closed-loop step response for evaluation of control performance. The first step in the proposed approach is data acquisition using a relay feedback experiment and the second step is step response estimation using frequency sampling filters. The proposed approach provides engineers with an intuitive way to more preciously evaluate the designed controllers and to obtain better controller tuning parameters. To validate this approach, experiments are conducted on a quadcopter test-bed and comparisons are made between the estimated step response and the step response from testing.

Step Response Identification for a Batch Reaction Vegetable Oil Transesterification

This paper discusses parameter estimations of step response from biodiesel transesterification process. The estimates signal generated from impedance measurement from sunflower oil transesterification process. 15 kHz AC signal has used to characterize the capacitance value during the process. The recorded signal then processed using Matlab system identification toolbox. The parametric identification method is selected to develop model structure. The model structure has modelled based on three different structures, ARMAX, BJ and ARX. The algorithm generated from identification toolbox as well as validation test. The validation test including correlation and cross correlation test. ARMAX structure appeared to be best between the others structure.

Proportional stabilization and closed-loop identification of an unstable fractional order process

This paper deals with proportional stabilization and closed-loop step response identification of the fractional order counterparts of the unstable first order plus dead time (FOPDT) processes. At first, the necessary and sufficient condition for stabilizability of such processes by proportional controllers is found. Then, by assuming that a process of this kind has been stabilized by a proportional controller and the step response data of the closed-loop system is available, an algorithm is proposed for estimating the order and the parameters of an unstable fractional order model by using the mentioned data.

High-Resolution Micro-Pirani Pressure Sensor With Transient Response Processing and Time-Constant Evaluation

A micro-Pirani pressure sensor, which consists of a pressure-dependent thermoresistance gauge, is traditionally exploited using a steady-state resistance measurement. Any signal variation occurs over an offset voltage due to the imperfection of the device, which affects the sensor's sensitivity. This paper presents, for the first time, an experimental investigation of a micro-Pirani gauge based on its dynamic behavior when heated by a current step. Such processing magnifies the pressure dependence of the gauge's signal by eliminating the constant offset influences on the measurement. Furthermore, a first-order low-pass filter step response identification of the experimental transient signal strongly reduces the noise influence on the measurement. Furthermore, such identification enables a direct calculation of the time constant. This paper investigates the pressure measurement based on the time-constant evaluation, which provides a range of measurement and a maximal sensitivity significantly shifted toward the atmospheric pressure compared to that of a traditionally processed Pirani gauge. The heating step, the recording of the transient response signal, and its digital postprocessing can be easily achieved by a small-sized controller. The proposed system provides a substantial performance enhancement of the micro-Pirani pressure sensor.

Step Response Identification under Inherent-Type Load Disturbance with Application to Injection Molding

Motivated by autotuning of injection velocity in an industrial injection molding machine, a step response identification method is proposed for practical application subject to inherent-type load disturbance. Both models of the process and the inherent-type load disturbance can be simultaneously derived from a step test. Identification algorithms are detailed for obtaining the widely used low-order models of first-order plus dead time (FOPDT) and second-order plus dead time (SOPDT). Based on the identified process and disturbance models, a two-degree-of-freedom (2DOF) internal model control (IMC) structure plus feed-forward control is proposed for improving load disturbance rejection. Analytical controller design formulas and tuning guidelines are developed accordingly. A practical application to the velocity control of an injection molding machine is shown to illustrate the effectiveness and merits of the proposed identification method and control strategy.

A frequency domain step response identification method for continuous-time processes with time delay

Step response test is widely practiced for model identification in process industry. A frequency domain step response identification method is proposed for obtaining a continuous-time process model with time delay. By introducing a damping factor to the step response for realization of Laplace transform, a frequency response estimation algorithm is first proposed, in which only single integral is needed for computation, compared to recently developed identification methods based on multiple integral in time domain. Based on the estimated frequency response, two model fitting algorithms are developed analytically for obtaining a time delay model of first-, second-, or higher order with repetitive poles. Another two algorithms based on fitting multiple frequency response points thus estimated are proposed for obtaining a time delay model of any order, the latter of which may also be used to improve fitting accuracy over a specified frequency range interested to control design. Meanwhile, practical strategies to consolidate identification robustness against measurement noise are given based on consistent estimation analysis, together with a guideline for model structure selection to realize optimal fitting for identification of a high order process. Illustrative examples from recent references are used to demonstrate the effectiveness and merits of the proposed identification algorithms.

Closed-loop step response identification of integrating and unstable processes

Motivated by the fact that integrating and unstable processes are usually operated in a closed-loop manner for safety and economic reasons, this paper proposes a systematic closed-loop identification method based on step response test to facilitate closed-loop system operation and on-line optimization. To avoid jeopardizing the closed-loop system stability of such a process, guidelines are given for proper implementation of a closed-loop step test for model identification. By introducing a damping factor to the closed-loop step response for realization of the Laplace transform in frequency domain, a frequency response estimation algorithm is developed in terms of the closed-loop control structure used for identification. Accordingly, three model identification algorithms are derived analytically in frequency domain to obtain the widely used low-order process models of first-order-plus-dead-time (FOPDT) and second-order-plus-dead-time (SOPDT). To enhance fitting accuracy for a higher order process, in particular for a specified frequency range interested to control design and on-line tuning, a weighted least-squares fitting algorithm is also given based on the estimated process frequency response points. Illustrative examples from the recent literature are used to demonstrate the effectiveness and merits of the proposed identification algorithms.

Reaction time and EMG measurement applied to human control modeling

The final goal of this research is to analyze the human visual and force sensory feedback integration related to a manipulation task and build a control model of a human operator. In this primary work the reaction time and the EMG was measured to determine the time lag constant of each subject. The onset time, EMG-RT and the EMD delay was measured and compared to the time lag estimated from step response identification. Although a small discrepancy was found between the directly measured time lag and the estimated one, the system identification method showed a prominent and methodological approach to model the human control system.

Design and Implementation of Electronic System and Computer Control of an Indexing Table and Quality Control Unit

Capability and meanwhile simplicity have stupendously expanded the area of application of linear controllers in the industrial activities, particularly the control of robotic arms, and other devices. The ultimate goal to accomplish this project is to show this capability in the frame of movement control of the arm of a quality control unit (side device), which forms a part of an automatic cell together with an indexing table. The fact that the existence of the gearbox, dramatically decreases the effects of non-linear dynamic of the arm to linear dynamic of the DC motor, justifies the idea of designation and implementation a linear controller like Lead-Lag to control the said arm. Of course, we need to apply some other equipment like DC motor torque control system, sensors, and related interface circuits. In this article, it will be observed that how the above goal will be accomplished by linear identification of the system with the method of step response identification and then digital implementation of the said controller in computer and application of the arm motor torque control.

Frequency-sampling filters: An improved model structure for step-response identification

This paper presents a new methodology for step-response identification using the frequency-sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite-impulse-response (FIR) model are (i) the number of parameters to be estimated is independent of the choice of sampling interval, and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (ii) the general conditioning of the correlation matrix is better with the FSF model because of the reduction in the number of parameters that need to be estimated and to the narrow-bandpass nature of the frequency sampling filters.

Robust Tuning of PID Regulators Based on Step-Response Identification

Robust control design requires an accurate knowledge of the uncertainty bound around the nominal model. While classical identification theory qualifies the uncertainty in identified models through confidence regions of the estimated parameters, recent works have studied frequency-domain error bounds for models identified via least-squares like techniques. In this paper a quantification of errors in models identified through stepresponse methods is proposed. A model error bound is derived based upon an elaboration of the step response of the process. This bound is then used to formulate simple rules for a robust tuning of PID controllers. A simulation example is also presented, where a comparison of the closed-loop performance obtained with the proposed tuning rules and with other common tuning rules is shown.

Frequency Sampling Filters: An Improved Model Structure for Step Response Identification

This paper presents a new methodology for step response identification using the frequency sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite impulse response (FIR) model are: (1) the number of parameters to be estimated is independent of the choice of sampling interval and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (2) the general conditioning of the correlation matrix is better with the FSF model due to the reduction in the number of parameters that need to be estimated and to the narrow bandpass nature of the frequency sampling filters.