System Identification Methodology Research Articles

Simple Tuning Rules for Feedforward Compensators Applied to Greenhouse Daytime Temperature Control Using Natural Ventilation

In this work, simple tuning rules for feedforward compensators were applied to design a control strategy to regulate the inside air temperature of a greenhouse during daytime by means of a natural ventilation system. The developed control strategy is based on a PI (Proportional-Integral) controller combined with feedforward compensators to improve the performance against measurable external disturbances such as outside air temperature, solar radiation, and wind velocity. Since the greenhouse process dynamics is very complex and physical non-linear models are mathematically complicated, a system identification methodology was proposed to obtain simpler models (high-order polynomial and low-order transfer functions). Thus, an easier procedure was completed to tune the PI controller parameters and to obtain the feedforward compensators expressions by following a series of modern and simple tuning rules. Simulations with real data were executed to compare the control performance of a PI controller with or without the addition of feedforward compensators. Moreover, real tests for the developed control strategy were carried out in an experimental greenhouse. Results demonstrate an enhanced control performance with the presence of the feedforward compensators under different weather conditions.

Open-Loop Linear Model Identification of aMultirotor Vehicle with Active Feedback Control

As multirotor vehicles become integrated into the national airspace for applications such as package delivery and videography, it is important that the inner-loop control system be robust and able to meet ever-demanding performance constraints. To achieve high bandwidth control designs, it is necessary to have accurate and high bandwidth open-loop models. In this Paper, a linear state-space model of the open-loop dynamics of a hex-configuration multirotor vehicle was identified from flight tests conducted with an active feedback controller. The system identification methodology and initial model structure were formed and informed by an analytical model, which incorporated a propeller aerodynamics model derived using blade element theory. The methodology for identification of the open-loop model involved the combined application of manual pilot inputs and automated multisine inputs added to the output of the controller. The effectiveness of the model’s predictive capability was shown with a validation flight test with simultaneous excitation of the four input axes and an independent flight test for validation of the heave model. The final model structure, which incorporated axis-lumped first order dynamics to represent the propulsion system dynamics, was found to be generalizable to various traditional coplanar multirotor configurations.

Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations

In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system using steady-state excitations. To accomplish this, a phase-locked loop controller is implemented to acquire periodic oscillations near resonance and construct a nonlinear-mode model. This model is based on amplitude-dependent modal properties, i.e. does not require nonlinear basis functions. The second methodology exploits uncontrolled experiments with broadband random inputs to build polynomial nonlinear state-space models using advanced system identification tools. The methods are applied to two experimental test rigs, a magnetic cantilever beam and a free-free beam with a lap joint. The respective models obtained by either method for both specimens are then challenged to predict dynamic, near-resonant behavior observed under different sine and sine-sweep excitations. The vibration prediction of the nonlinear-mode and state-space models clearly highlight capabilities and limitations. The nonlinear-mode model, by design, yields a perfect match at resonance peaks and high accuracy in close vicinity. However, it is limited to well-spaced modes and sinusoidal excitation. The state-space model covers a wider dynamic range, including transient excitations. However, the real-life nonlinearities considered in this study can only be approximated by polynomial basis functions. Consequently, the identified state-space models are found to be highly input-dependent, in particular for sinusoidal excitations where they are found to lead to a low predictive capability.

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Abstract This paper presents a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

A stochastic framework for hydraulic performance assessment of complex water distribution networks: Application to connectivity detection problems

This paper is concerned with the hydraulic performance assessment of large scale water distribution networks in presence of uncertainty. In particular, the associate connectivity detection problem is examined in detail. For this purpose, a Bayesian system identification methodology is combined with an efficient hydraulic simulation model. A number of hydraulic model classes are defined as potential connectivity events. Based on information from flow rates in the pipes, the proposed updating technique provides estimates of the most probable connectivity scenarios. Such scenarios correspond to the model classes that maximize their evidences or posterior probabilities. The effectiveness of the proposed identification framework is illustrated by applying the connectivity detection approach to a real water distribution system.

Simulation and time-frequency analysis of the longitudinal train dynamics coupled with a nonlinear friction draft gear

AbstractTrain safety and operational efficiency can be improved by investigating the dynamics of the train under varying conditions. Longitudinal train dynamics (LTD) simulations performed for such purposes, usually by utilising a nonlinear time-domain model. This paper covers two modes of LTD results corresponding to the time domain and frequency domain analysis. Time-domain solutions are essential to evaluate the full response used for parameter optimisation and controller design studies while frequency domain solutions can provide significant but straightforward clues regarding system dynamics. An advanced draft gear model, which works under a four-stage process is constructed considering all structural components, geometric relationships, friction modelling and dynamic characteristics such as hysteresis, stiffening, state transition, locked unloading, softening. Then, this model is parametrically reduced and implemented into an LTD simulation. The simulation in the time domain is conducted assuming a locomotive connected with a nine wagon via “ode3” fixed-step solver. The transfer function among the first wagon acceleration (output) and the locomotive force (input) estimated through system identification methodology. Then, the identification results interpreted by investigating step-response characteristic and best response giving parameter set is selected. Next, the modal and spectral analysis performed to reveal the behaviour of the in-train forces and the effects of vibration. This paper discusses a reliable methodology for the longitudinal dynamic analysis of the multi-bodied train in time and frequency domain and clarifies in-train vibration behaviour under the existence of sophisticated draft gear.

A Novel Method And Implementation Of Digital Algorithm For First Order Non Linear Spherical Tank Level Control Process With An Integral Windup

The primary goal of the process control is to control, monitor and to maintain different kinds of process stations at the desired stable conditions for improving the productivity and product quality. In industries most of the process are non - linear, controlling such type of system is difficult. So, spherical tanks are one among the non-linear systems which can be used to store maximum quantity of product in minimum area. These types of non-linear systems violate the superposition principle and are very difficult to control and monitor. Generally, conventional PID Controller is used for controlling a process in a plant. In Level control process, there exists the sudden change in final control element which leads to maximum overshoot and larger settling time, this stated phenomenon requires tuning of gain values to control the process variable to the set point. In order to overcome the above-mentioned problem, Anti reset windup concept is preferred. The mathematical modelling of a given process is obtained using black box system identification methodology. The accuracy in controlling the process variable is complicated due to its dynamic behaviour and characteristics. So in order to maintain the proper characteristics of non-linear system the mathematical modelling has been obtained by conducting the open loop test. In ancient stage the Ziegler Nicholas method is implemented to tune the PID parameters such as Kp, τi and τd which takes larger settling time, maximum Integral Square error and maximum peak overshoot. After tuning the PID gain values with the help of anti reset windup, the overshoot and settling time will be reduced. In this proposed work, Deadbeat and Dahlin’s Algorithms are used to convert the transfer function of the level process from ‘S’ domain to ‘Z’ domain (digital controller). The transfer function is used in MATLAB (Matrix Laboratory) to analyse the output of level process and the simulations are taken for the plant with above mentioned two different algorithms. For real time interface the LabVIEW platform is used and the results are discussed. This proposed digital controller with anti reset windup provides lower steady state error, minimum peak overshoot and minimum error criteria indices.

Mathematical model derivation of an unmanned circulation control aerial vehicle UC2AV

This paper presents a system identification method to derive accurate mathematical models for an unmanned circulation control aerial vehicle (UC2AV) that account for the effects of circulation control (CC) on the vehicle dynamics. The X-plane flight simulator and the CIFER system identification software are utilized to first derive simulation models to verify and validate the proposed system identification methodology. This is followed by flight tests to derive mathematical models and stability derivatives for the aircraft with CC-on and CC-off. Flight tests indicate a nose down pitching moment effect induced by CC, which in turn alter the UAV trim values and dynamics. Analysis of the two sets of mathematical models reveal that CC changes the longitudinal trim values and improves the lateral maneuverability of the UAV. Verification experiments indicate an acceptable match between the derived models and UAV dynamics by calculating root mean square (RMS) error values and by quantifying the model predictive ability through calculating the Theil inequality coefficient (TIC).

1058-P: The Use of Metabolic Digital Twins to Personalize Mealtime Insulin Dosing in Type 1 Diabetes Clinical Management

Introduction: Type 1 diabetes guidelines recommend that mealtime insulin dosing accounts for fat and protein. However, implementation remains a challenge. Aim: To evaluate an individual’s glycemic response to a high fat, high protein meal (HFHP) and develop a personalized ‘metabolic digital twin’ (MDTwin) capable of predicting an optimal insulin dose and delivery pattern which accounts for meal fat and protein. Method: Participants (n=9) using pump therapy; mean age 13.9 ±3.2 years (5 male), T1D duration 4.8 ±3.8 years, and HbA1c 49 mmol/mol (6.7 ±0.6%) wore Dexcom G5 CGMS and were given an insulin dual wave bolus (insulin to carbohydrate ratio [ICR] x1, ICRx1.2, ICRx1.4 or ICRx1.6) and ate a HFHP (C:30g, F:40g, P:50g) on 4 mornings. Individual MDTwins were fitted by System Identification methodology combined with a statistical quantification using the ICRx1 and ICRx1.6 CGMS traces. MDTwins were validated using the ICRx1.2 and ICRx1.4 traces. Results: The MDTwins identified individual insulin and distribution requirements to achieve optimal postprandial glycemic control (Figure). MDTwins identified marked inter-individual variability in insulin dose and distribution requirements. Conclusion: MDTwins can potentially be used to inform and individualize insulin dosing strategies for meal fat and protein to produce optimal glycemic outcomes. Further research is required. Disclosure T.A. Smith: None. M.M. Seron: None. G.C. Goodwin: None. A.M. Medioli: None. B.R. King: None. C.E. Smart: None. M. Harris: None. D.N. ONeal: None. J. Rafferty: None. P. Howley: None. Funding Diabetes Australia

Development and Modeling of Remotely Operated Scaled Multi-wheeled Combat Vehicle Using System Identification

This paper describes the development and modeling of a remotely operated scaled multi-wheeled combat vehicle (ROMW-CV) using system identification methodology for heading angle tracking. The vehicle was developed at the vehicle dynamics and crash research (VDCR) Lab at the University of Ontario Institute of Technology (UOIT) to analyze the characteristics of the full-size model. For such vehicles, the development of controllers is considered the most crucial issue. In this paper, the ROMWCV is developed first. An experimental test was carried out to record and analyze the vehicle input/output signals in open loop system, which is considered a multi-input-single-output (MISO) system. Subsequently, a fuzzy logic controller (FLC) was developed for heading angle tracking. The experiments showed that it was feasible to represent the dynamic characteristics of the vehicle using the system identification technique. The estimation and validation results demonstrated that the obtained identified model was able to explain 88.44% of the output vari-ation. In addition, the developed FLC showed a good heading angle tracking.

Enhancing static-load-test identification of bridges using dynamic data

In situ measurements have the potential to provide valuable information about the safety and the condition of bridges through implementation of system-identification methodology. A significant amount of research has focused on system identification using either dynamic or static measurements separately. Realizing the complementary relationship between static and dynamic measurements, traditional model updating methods adopt error functions to account for the residual between modeling and measured values for various types of measurements. Behavioral models may be inaccurate due to incomplete representation of modeling and measurement uncertainties. Furthermore, the normalization of error functions may bring additional uncertainty to the identification process. In this paper, an approach based on the model falsification method is proposed to combine both static and dynamic measurements with explicit consideration of both modeling and measurement uncertainties. A measurement selection strategy is also used to help detect abnormal measurements. The approach has been evaluated using a highway flyover bridge in Singapore. Dynamic measurement data include natural frequencies and mode shapes whereas static measurement data include inclinations, deflections and strains. By combining both static and dynamic measurements, this approach leads to falsification of additional model instances and obtains a more precise prediction of parameter values than approaches which interpret static measurements only.

Model Based Diagnosis of Oxygen Sensors

Abstract Automotive industry targets such as complying with emission legislations and increasing fuel economy, require the improvement of air-fuel ratio control systems. Oxygen sensors are a crucial part of these control systems and regulations oblige monitoring of their performance and detecting sensor-related faults. The primary purpose of this paper is to develop a methodology for precise and accurate monitoring and diagnosis of oxygen sensors to meet legislations and performance targets while the required calibration effort is reduced. Input parameters with the highest correlation factors were selected to be utilized in different system identification methodologies to statistically determine the most fitting model. In the end, a NARX model with two hidden layers and eight neurons in each hidden layer with standard deviation and mean threshold values was determined to be the optimum design to detect if the oxygen sensor was functioning or faulty.

Fractional-Derivative Approximation of Relaxation in Complex Systems

In this paper, we present a system identification (SI) procedure that enables building linear time-dependent fractional-order differential equation (FDE) models able to accurately describe time-dependent behavior of complex systems. The parameters in the models are the order of the equation, the coefficients in it, and, when necessary, the initial conditions. The Caputo definition of the fractional derivative, and the Mittag-Leffler function, is used to obtain the corresponding solutions. Since the set of parameters for the model and its initial conditions are nonunique, and there are small but significant differences in the predictions from the possible models thus obtained, the SI operation is carried out via global regression of an error-cost function by a simulated annealing optimization algorithm. The SI approach is assessed by considering previously published experimental data from a shell-and-tube heat exchanger and a recently constructed multiroom building test bed. The results show that the proposed model is reliable within the interpolation domain but cannot be used with confidence for predictions outside this region. However, the proposed system identification methodology is robust and can be used to derive accurate and compact models from experimental data. In addition, given a functional form of a fractional-order differential equation model, as new data become available, the SI technique can be used to expand the region of reliability of the resulting model.

An Engineering Model to Test for Sensory Reweighting: Nonhuman Primates Serve as a Model for Human Postural Control and Vestibular Dysfunction.

Quantitative animal models are critically needed to provide proof of concept for the investigation of rehabilitative balance therapies (e.g., invasive vestibular prostheses) and treatment response prior to, or in conjunction with, human clinical trials. This paper describes a novel approach to modeling the nonhuman primate postural control system. Our observation that rhesus macaques and humans have even remotely similar postural control motivates the further application of the rhesus macaque as a model for studying the effects of vestibular dysfunction, as well as vestibular prosthesis-assisted states, on human postural control. Previously, system identification methodologies and models were only used to describe human posture. However, here we utilized pseudorandom, roll-tilt balance platform stimuli to perturb the posture of a rhesus monkey in normal and mild vestibular (equilibrium) loss states. The relationship between rhesus monkey trunk sway and platform roll-tilt was determined via stimulus-response curves and transfer function results. A feedback controller model was then used to explore sensory reweighting (i.e., changes in sensory reliance), which prevented the animal from falling off of the tilting platform. Conclusions involving sensory reweighting in the nonhuman primate for a normal sensory state and a state of mild vestibular loss led to meaningful insights. This first-phase effort to model the balance control system in nonhuman primates is essential for future investigations toward the effects of invasive rehabilitative (balance) technologies on postural control in primates, and ultimately, humans.

Identification of distributed-parameter systems from sparse measurements

• A system identification methodology for distributed-parameter systems is proposed. • The methodology only requires measurements from sparse sensor locations. • The methodology extends the standard lumped-parameter system identification methods. • The methodology is suitable for modeling and model-based indoor climate control. • Results are presented for simulated as well as real systems. In this paper, a method for the identification of distributed-parameter systems is proposed, based on finite-difference discretization on a grid in space and time. The method is suitable for the case when the partial differential equation describing the system is not known. The sensor locations are given and fixed, but not all grid points contain sensors. Per grid point, a model is constructed by means of lumped-parameter system identification, using measurements at neighboring grid points as inputs. As the resulting model might become overly complex due to the involvement of neighboring measurements along with their time lags, the Lasso method is used to select the most relevant measurements and so to simplify the model. Two examples are reported to illustrate the effectiveness of the method, a simulated two-dimensional heat conduction process and the construction of a greenhouse climate model from real measurements.

A System Identification Methodology to monitor construction activities using structural responses

Abstract This paper presents a methodology to use structural responses to monitor the progress of construction processes. The methodology was implemented on a launching girder used for viaduct construction of a metro rail project. A strain-based wireless sensor network was used for data acquisition. Structural responses from the launching girder were used to identify the state of construction. A conventional System Identification Methodology was tested for this application but was not accurate in inferring the stage of construction. Therefore a modified system identification strategy using derived features and heuristics was used to infer the state of construction. The modified methodology was found to be significantly more accurate than the conventional methodology and is well suited for applications in unstructured construction environments. Results from the case study confirm that the use of structural responses is feasible for measuring the progress of construction activities.

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

This paper proposes an autonomous system identification methodology for fixed wing autonomous aerial vehicles (AAVs) and implementation with a robust control technique. This system identification methodology does not require the user to provide any knowledge of the aerodynamic derivatives of the AAV. Hence, this control methodology is applicable to a wide range of fixed-wing AAVs. Since the system identification process generates errors due to the measurement noise and external disturbances, a robust control methodology, sliding mode control (SMC), is proposed to control the AAV based on the system identification results. To characterize the capability of the control methodology, experiments have been conducted on commercially available fixed-wing aircraft platforms using low-cost sensing equipment. Experimental results demonstrate that the autonomous system identification-based SMC enables the AAV to follow the desired trajectory without prior knowledge of the aerial vehicle or manual tuning of control system parameters.

Nonlinear System Identification Technique for a Base-Excited Structure Based on Modal Space Formulation

Most of the nonlinear system identification techniques described in the existing literature required force and response information at all excitation degrees-of-freedom (DOFs). For cases, where the excitation comes from base motion, those methods cannot be applied as it is not feasible to obtain the measurements of motion at all DOFs from an experiment. The objective of this research is to develop the methodology for the nonlinear system identification of continuous, multimode, and lightly damped systems, where the excitation comes from the moving base. For this purpose, the closed-form expression for the equivalent force also known as the pseudo force from the measured data for the base-excited structure is developed. A hybrid model space is developed to find out the nonlinear restoring force at the nonlinear DOFs. Once the nonlinear restoring force is obtained, the nonlinear parameters are extracted using “multilinear least square regression” in a modal space. A modal space is chosen to express the direct and cross-coupling nonlinearities. Using a cantilever beam as an example, the proposed methodology is demonstrated, where the experimental setup, testing procedure, data acquisition, and data processing are presented. The example shows that the method proposed here is systematic and constructive for nonlinear parameter identification for base-excited structure.

Robust Estimation of Balanced Simplicity-Accuracy Neural Networks-Based Models

Neural networks are powerful tools for black box system identification. However, their main drawback is the large number of parameters usually required to deal with complex systems. Classically, the model's parameters minimize a L2-norm-based criterion. However, when using strongly corrupted data, namely, outliers, the L2-norm-based estimation algorithms become ineffective. In order to deal with outliers and the model's complexity, the main contribution of this paper is to propose a robust system identification methodology providing neuromodels with a convenient balance between simplicity and accuracy. The estimation robustness is ensured by means of the Huberian function. Simplicity and accuracy are achieved by a dedicated neural network design based on a recurrent three-layer architecture and an efficient model order reduction procedure proposed in a previous work (Romero-Ugalde et al., 2013, “Neural Network Design and Model Reduction Approach for Black Box Nonlinear System Identification With Reduced Number of Parameters,” Neurocomputing, 101, pp. 170–180). Validation is done using real data, measured on a piezoelectric actuator, containing strong natural outliers in the output data due to its microdisplacements. Comparisons with others black box system identification methods, including a previous work (Corbier and Carmona, 2015, “Extension of the Tuning Constant in the Huber's Function for Robust Modeling of Piezoelectric Systems,” Int. J. Adapt. Control Signal Process., 29(8), pp. 1008–1023) where a pseudolinear model was used to identify the same piezoelectric system, show the relevance of the proposed robust estimation method leading balanced simplicity-accuracy neuromodels.

Nonlinear system identification of receptive fields from spiking neuron data

Identification of a linear filter in cascade with a spiking neuron has been previously considered [1] under the assumption that the input of the Hodgkin-Huxley spiking neuron can be measured in addition to the input and output of the circuit. An alternative method to identify a linear filter given the input to the circuit and the spike time sequence, has been proposed [2] for Integrate-and-Fire (IAF) neurons. The method was further extended [3] to nonlinear receptive fields that can be described by a Volterra series. Here we propose a new system identification methodology to identify a more general NARMAX representation of the nonlinear receptive field in cascade with an ideal-IAF neuron, based only on measurements of the input stimulus and the spike time sequence of the IAF neuron. By using an orthogonal forward selection algorithm we are able to derive the NARMAX representation of a scaled version of the nonlinear filter directly from the reconstructed input of an ideal IAF neuron. The method is further extended to leaky-IAF neurons which require estimating an additional spiking neuron parameter. We use the NARMAX methodology to identify recursively the nonlinear filter and the spiking neuron parameter in the presence of noise. Statistical and dynamical model validation tests are used to check if the identified nonlinear filter models are an adequate representation of the underlying nonlinear information processing mechanisms. The performance and robustness to noise of the proposed methods is demonstrated through numerical simulation studies. Specifically, we compared the generalized (higher-order) frequency response functions (GFRFs) of the original and the identified nonlinear filter (Figures 1 A-D) and evaluated, for different noise levels, the SNR of the model predicted output on a validation data set (Figure 1 E). Figure 1 A&B - GFRF functions of the original filter, C&D - Difference between the original GFRFs and the scaled versions for the identified filter, E - SNR between the validation signal and the identified filter output.