System Identification Techniques Research Articles

High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle

In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities.

Implementation of System Identification Techniques and Optimal Control for RC Model Selection by Means of TRNSYS Simulation Results and Experimental Data

Simulating the thermal model of a district requires simultaneously retaining accuracy and simplicity, in order to avoid cumbersome calculations and unrealistic predictions. Within this scope, introducing a simple structure for modeling the energy consumption of a building that can be expanded to the district level becomes essential. In this paper, a hierarchy of thermal models with increasing complexity is developed to identify the simplest structure that can effectively represent the thermal behavior of a building, using a simulated building in TRNSYS and the measurements of a real building as two datasets to estimate the model parameters. Each model is placed in a closed loop system to track the constant indoor temperature by means of the linear quadratic regulator (LQR) technique. To select the best structure, the model outputs are compared to TRNSYS simulations and measurements. The main features of the selected models include the use of only a few parameters to predict the indoor temperature, peak power, total heat demand, and transient behavior of a building. It is shown that the proposed models are able to determine the indoor temperature with less than 1 °C of error, and the total heat demand and peak power are also determined with an error lower than 25%.

System identification using neuro fuzzy approach for IoT application

The Internet of Things (IoT) has become a popular application in recent years. However, it is the wireless communication mode. In such a scenario, the user would have to send information either nonlinear or dynamic data type in the form of a signal or an image, videos depending on the application. The proposed work is focused on this model identification that tends to nonlinear dynamic system identification for IoT applications. An Autoregressive Moving Average (ARMA) model represents model for IoT application. To verify the model supremacy, an ARMA bench mark system is used. The adaptiveness is proved through variation of weights and can be universally used for the next generation. In the first attempt, the Multilayer Perceptron model (MLP) is considered as the ARMA system and observed. Further, to improve its accuracy, the Adaptive Neuro-Fuzzy system (ANFIS) model is designed for system identification. It is shown in the result section that it identifies better than the MLP as well as traditional system identification techniques.

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

Mechanical rotor unbalance monitoring based on system identification and signal processing approaches

Mechanical unbalance is an important source of vibrations that can cause malfunctions in rotodynamic machinery. In industrial applications, unbalance is a critical issue for mass production machines. Previous studies for detecting and monitoring unbalance are based on balancing machines, trial weights, and intrusive actuators, while other studies rely on signal processing techniques, finite element analysis and physical modeling. These methodologies have some critical drawbacks, especially when non-intrusive monitoring is required, such as having to trial weights or determine constructive parameters such as mass and stiffness. The proposed approach is based on detecting and monitoring the unbalance condition in rotatory machines using data extracted from vibration sensors and a rotation sensor fitted to the system supports. The methodology comprises two main steps: identifying the appropriate speed range for unbalance monitoring and the modal parameters of the rotor, and determining and continuously monitoring the unbalance condition. Signal processing and system identification techniques are used to estimate unbalance in the rotatory machine. Experimental results for two rotodynamic systems demonstrate satisfactory performance in identifying and monitoring different unbalance conditions.

On the encoding of natural music in computational models and human brains.

This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music.

Adaptive Control Strategy of a 2-Axes Gimbal Structure Loading Device for a Stair-Climbing Delivery Robot

This paper studies an adaptive control strategy for a 2-axes gimbal structure loading device for a stair-climbing delivery robot. The properties of the loading device change depending on the luggage, and the loaded luggage cannot be specified. It is difficult to design an appropriate controller with fixed PID gains. To optimize the controller regardless of luggage condition, the adaptive control strategy is conducted by system identification and gain scheduling. The system identification estimates the parameters of the dynamic equations. The gain scheduler optimizes the PID controller using the estimated parameters. The system identification technique is based on the least-squares method. The accuracy of system identification is improved by a null-space solution. The gain scheduler consists of surface functions defined by interpolation of the optimized PID gains. The system identification techniques are verified by simulation, comparing a specific system assumed as a real system and the estimated system. The experiments using the motion platform verify the adaptive control strategy. This proposed control strategy adapts the controller to the system of the loading device.

Evaluating OMA System Identification Techniques for Drive-by Health Monitoring on Short Span Bridges

Evaluating OMA System Identification Techniques for Drive-by Health Monitoring on Short Span Bridges

Data-Driven Identification of Dissipative Linear Models for Nonlinear Systems

We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input–output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of the linear model to enforce dissipativity, while closely approximating the dynamical behavior of the nonlinear system. Further, we provide an analytical relationship between the size of the perturbation and the radius in which the dissipativity of the linear model guarantees local dissipativity of the unknown nonlinear system. We demonstrate the application of this identification technique through two examples.

Ogden material calibration via magnetic resonance cartography, parameter sensitivity and variational system identification.

Contemporary material characterization techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterization problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully three-dimensional displacement fields acquired in silicone elastomers with our recently developed magnetic resonance cartography (MR-u) technique on the order of greater than [Formula: see text], we leverage partial differential equation-constrained optimization as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric and discuss the implications for experimental design. This article is part of the theme issue 'The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity'.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration.

BackgroundThe COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people’s physical activity or their responses to micro-interventions for promoting physical activity.PurposeThis paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.MethodsInsufficiently-active young adults (18–29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0–6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.ResultsDaily step counts decreased significantly following the pandemic declaration on weekdays (Cohen’s d = ‐1.40) but not on weekends (d = ‐0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = ‐0.18) or weekends (d = ‐0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.ConclusionsGeneralizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Determining the Dynamic Characteristics of a Multi-Story RC Building Located in Chile: A Comparison of the Results between the Nonparametric Spectral Analysis Method and the Parametric Stochastic Subspace Identification Method

Estimating the dynamic characteristics of instrumented built structures from seismic vibration data collected from built civil structures is essential input information for structural model updating and assessing the health of structures. This study focuses on the earthquake acceleration time histories obtained from several events recorded during its construction phase by accelerometers placed throughout an office building located in Viña del Mar (Chile) to determine its modal features. To this end, the data obtained were analyzed to compare the building’s dynamic properties obtained with two different modal identification techniques. MATLAB programs were developed to implement both methods. The stochastic subspace identification technique for linear systems developed by van Overschee and de Moor was used to study the dynamic properties of the building. In contrast, the nonparametric method employed herein uses correlations and spectral analysis based on the Welch transform in the frequency domain. The investigation demonstrated that both methods identify similar frequencies and that the obtained translational mode shapes exhibit good agreement. Furthermore, the identified frequencies are congruent with the design frequencies.

Proportional integral derivative controller based on ant colony optimization for vibration cancellation of horizontal flexible plate structure

<p>Flexible plate structure provides many benefits as compared to their rigid counterparts including lower energy consumption, effective, lightweight, and quick response. However, the vibration easily affects the flexible plate structure resulting in structural damage. This study introduces the modelling of a flexible plate structure based on a system identification technique known as ant colony optimization (ACO) algorithm for vibration control. Firstly, the input-output vibration data that represent the actual structure of flexible plate was achieved from the experiment. Next, the acquired vibration data was used to develop a dynamic model of the flexible plate structure. The performances of the ACO algorithm were assess based on mean squared error (MSE), pole-zero plot and correlation test in order to get a precise and reliable outcome. The results show that ACO algorithm achieved the minimum MSE which was 6.7613×10<sup>−6</sup>, high stability of pole-zero plot and excellent correlation test. Subsequently, the best model of ACO was chosen to create controller based on an active vibration control technique. It was noticed that the controller managed to obtain a 6.19 dB reduction at the first mode vibration in which the percentage of attenuation of the controller was 10.63% for sinusoidal disturbances and 9.64% for multiple sinusoidal disturbances.</p>

Coordinated Charging and Discharging of Electric Vehicles: A New Class of Switching Attacks

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.

Shoulder Joint Stiffness in a Functional Posture at Various Levels of Muscle Activation.

In this study, we present a method to quantify the mechanics of the shoulder joint in a functional posture, in two degrees of freedom: internal/external rotation and horizontal abduction/adduction. We performed experiments on 15 healthy participants using a custom perturbation robot. Perturbations were applied in internal/external rotation and horizontal abduction/adduction, whilst participants applied varying levels of joint torque. System identification techniques were used to quantify the mechanical properties of the shoulder joint at various levels of muscle contraction, including; stiffness, viscous damping, and inertia parameters, natural frequency, and damping parameter. We compared the shoulder mechanical properties between dominant and non-dominant limbs. The mean stiffness increased 4.8 times in external rotation, and 6.25 times in internal rotation as a result of contraction to 8 Nm. It increased 2.8 times in adduction and 4.6 times in abduction as a result of contraction to 16 Nm. The mean viscous damping increased 3 times in external rotation, 2.8 times in internal rotation as a result of contraction to 8 Nm. It increased 1.6 times in adduction and 2.25 times in abduction as a result of contraction to 16 Nm. Joint stiffness, viscous damping and natural frequency all increased with the level of shoulder contraction torque, whereas the damping parameter remained unchanged. No differences were observed between dominant and non-dominant limbs. We have presented a method to characterize the mechanical properties of the shoulder complex during various activation states, which has application as a diagnostic and assessment tool for shoulder pathology.

Data-driven modeling of the thermal dynamics of a dual-spectral ultraviolet sterilizer and control of the output irradiance

Multi-channel LED light sources possess complicated nonlinear photo-electro-thermal (PET) dynamics. Modeling such PET dynamics is challenging, but important to ensure the performance of these light sources. Traditional first-principle modeling methods that have been reported in the literature yield either single-channel LED dynamic models, or multi-channel static models. On the other hand, system identification (SID) methods have also been applied, and lead to both single-channel nonlinear dynamic models and multi-channel linear time invariant (LTI) models. However, there are still few attempts given to build multiple-input-multiple-output (MIMO) nonlinear dynamic models of multi-channel LED systems. In this work, we try to model the PET dynamics of a dual-spectral ultraviolet (UV) light source, with a structured nonlinear system identification technique, i.e., linear parameter varying (LPV) SID technique. The experimental prototype contains a 280 nm UVC channel and a 365 nm UVA channel, respectively driven by two separate constant current sources, and is hence a MIMO system. Its nonlinear electro-thermal model is first identified from experimental data by the LPV-SID method, which is then integrated with the estimated photo-electro model to form a complete PET dynamic model. The experimental results have verified that the model can accurately predict the temperature variations at two representative points on the LED board. Moreover, the identified model has been applied in designing a feedforward-feedback controller to precisely track the reference radiation levels of the two UV channels, despite the strong nonlinearity caused by both the electro-thermal dynamics and the temperature-dependent lumen efficacy.

A Subspace Identification Technique for Real-Time Stability Assessment of Droop Based Microgrids

Real-time detection of the microgrid stability is crucial for determining and adjusting the power-sharing droop control's gains to maintain a sufficient stability margin. Maintaining minimum relative stability should be ensured at different operating conditions to accommodate sudden system changes. This article develops a novel subspace-based identification technique to assess microgrid stability in real-time without relying on offline analytical small or large-signal models. Unlike conventional system identification techniques that require the introduction of external excitation (signal probing, load switching, or contingency event), the proposed method employs a simple internal routine through small and short-duration perturbations in the active power droop gain of inverter-based distributed generation (IBDG). The use of subspace identification does not require pre-defining the system's order, avoids the exhaustive computations associated with iterative identification methods and can handle a change of microgrid's network configuration. The proposed stability assessment method has been tested on an IBDG microgrid considering different operating conditions in MATLAB/Simulink. The accuracy of the proposed real-time stability assessment tool is determined by comparing the results to the analytically-derived small-signal model as well as the Matrix pencil and Prony methods.

İleri Beslemeli Yapay Sinir Ağının Eğitiminde Meta-Sezgisel Yaklaşımlar

Sistem kimliklendirme ve modelleme için en yaygın kullanılan yapay zekâ tekniklerinden biri yapay sinir ağlarıdır. Yapay sinir ağları ile etkili sonuçlar elde etmek için etkili bir eğitim sürecine ihtiyaç duyulmaktadır. Meta-sezgisel algoritmalar pek çok gerçek dünya probleminin çözümünde başarılı bir şekilde kullanılmaktadır. Özellikle yapay sinir ağı eğitiminde, ağa ait parametrelerin optimizasyonu gerekmektedir. Son zamanlarda, bu amaçla meta-sezgisel algoritmalar kullanılmakta ve başarılı sonuçlar elde edilmektedir. Literatürde pek çok meta-sezgisel algoritma bulunmaktadır. Meta-sezgisel algoritmaların performansları problem türüne göre farklılık göstermektedir. Bu çalışma kapsamında ileri beslemeli yapay sinir ağının eğitiminde, yapay arı koloni algoritması, parçacık sürü algoritması, armoni arama, arı algoritması, çiçek tozlaşma algoritması ve guguk kuşu arama gibi popüler meta-sezgisel algoritmaların performansları değerlendirilmiştir. Uygulamalar için XOR, 2-bit parity ve 3-bit parity problemleri kullanılmıştır. Tüm problemler için elde edilen sonuçlar çözüm kalitesi ve yakınsama hızı açısından değerlendirilmiştir. Genel olarak ilgili problemlerin çözümü için meta-sezgisel algoritma tabanlı ileri yapay sinir ağı eğitiminin başarılı olduğu gözlemlenmiştir. En iyi sonuçlar ise yapay arı koloni algoritması ve guguk kuşu arama ile bulunmuştur.

Identification and Validation of the Cessna Citation X Longitudinal Aerodynamic Coefficients in Stall Conditions using Multi-Layer Perceptrons and Recurrent Neural Networks

The increased number of accidents in general aviation due to loss of aircraft control has necessitated the development of accurate aerodynamic airplane models. These models should indicate the linear variations of aerodynamic coefficients in steady flight and the highly nonlinear variations of the aerodynamic coefficients due to stall and post-stall conditions. This paper presents a detailed methodology to model the lift, drag, and pitching moment aerodynamic coefficients in the stall regime, using Neural Networks (NN). A system identification technique was used to develop aerodynamic coefficients models from flight data. These data were gathered from a level-D Research Aircraft Flight Simulator (RAFS) that was used to execute the stall maneuvers. Multilayer Perceptrons and Recurrent Neural Networks were used to learn from flight data and find correlations between aerodynamic coefficients and flight parameters. This methodology is employed in here to optimize neural network structures and find ideal hyperparameters: training algorithms and activation functions used to learn the data. The developed stall aerodynamic models were successfully validated by comparing the lift, drag, and pitching moment aerodynamic coefficients predicted for given pilot inputs with experimental data obtained from the Cessna Citation X RAFS for the same pilot inputs.

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

This study introduces a new evolutionary approach called binary genetic programming (BGP) to design and assess public transportation systems from a sustainable development perspective. The BGP combines evolutionary system identification techniques with k-fold cross-validation to obtain an accurate model between the land use and transportation parameters from a sustainable urban development point of view. To assess the new model, two public transportation systems including the new tram line of Antalya (Turkey) and the bus rapid transit line of Bhopal (India) were considered. The model was applied to classify the transportation systems into transit-oriented development (TOD) and non-TOD. The solutions generated by the new model were compared with those of classic decision tree (DT) as well as the state-of-the-art random forest (RF) models evolved as the benchmarks in this study. The results showed that the BGP is highly efficient and may provide less than 5% classification error. It is superior to the DT and RF solutions, which typically require higher datasets to avoid overfitting. Furthermore, the explicit formulation of BGP in combination with the multicriteria evaluation method increases human insight on the factors affecting the design of public transportations from a sustainable urban development point of view.