System Identification Algorithm Research Articles

A rapid system identification method for measuring explosion heat by the constant temperature method

In order to reduce the risk of measurement failure caused by system failure in the classical constant temperature method for measuring explosion heat, a measurement method was developed for identifying the explosion heat value based on the water temperature rise curve of an inner barrel before failure. Firstly, the heat transfer mechanism of the measurement process was analyzed, and the heat transfer model of the calorimeter was established, and the water temperature rise curve of the inner barrel in each measurement stage was obtained. Then, based on the system identification theory, the identification algorithm of intermediate parameters was proposed, and based on the idea of isolating easily oscillating parameters, a fast system identification algorithm was given to correct the temperature rise and explosion heat. And it is proved that the identification value of explosion heat approximately converges to the classical value. Finally, the test data of 8 explosive samples with explosion heat values ranging from 4 to 9 kJ/g were used to test the algorithm, and the test criterion for judging the convergence time was put forward. The simulation results show that the identification algorithm can effectively isolate the influence of the oscillation parameters and has a strong prediction ability on the temperature change of the water in the inner barrel, and that the identification value of explosion heat can quickly and stably converge to the upper limit of relative error of 3.5% within 40 min (1/3 of the main end stage), and that the test criterion can accurately judge the convergence time of the identified value of explosion heat. This method can also be extended to the adiabatic method for calculating explosion heat.

Evaluation of ML Algorithms for System Dynamics Identification of Aircraft Pressure Control System

In this work we investigate the behaviour of aircraft air conditioning and internal pressure control system. This system is critical for aircraft operations, because detected faults lead to flight cancellation and maintenance, while in-flight faults can substantially worsen crew or passenger flight conditions. Our goal is to investigate the quality of fault detection and identification as well as computational complexity of novel data-driven algorithms for system identification. The main algorithm studied is Sparse Identification of Nonlinear Dynamics, which identifies nonlinear dynamical systems from data promoting sparsity in the solution. Developing a SINDy-based prototype of the health monitoring system is carried out as follows. We are using simulation data from an industrial ground facility, which is capable of high altitude flight simulation. We generate time series data of system's normal operation and different types of faults, such as wear of the most sensitive elements of the system (air gate, temperature and pressure gauges). Then, using the obtained data we build a Koopman operator approximation for each known mode of operation. During the testing phase prediction error is used as anomaly indicator. It is shown that online model update and its comparison with reference model can help in unknown fault isolation.

An Adaptive Model Predictive Control Scheme for Energy-Efficient Control of Building HVAC Systems

Abstract An autonomous adaptive model predictive control (MPC) architecture is presented for control of heating, ventilation, and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A yearlong simulation with a realistic plant shows that both of the features of the proposed architecture—periodic model and disturbance update and convexification of the planning problem—are essential to get performance improvement over a commonly used baseline controller. Without these features, long-term energy savings from MPC can be small while with them, the savings from MPC become substantial.

Aortic pressure waveform reconstruction using a multi-channel Newton blind system identification algorithm

BackgroundCentral aortic pressure (CAP) as the major load on the left heart is of great importance in the diagnosis of cardiovascular disease. Studies have pointed out that CAP has a higher predictive value for cardiovascular disease than peripheral artery pressure (PAP) measured by means of traditional sphygmomanometry. However, direct measurement of the CAP waveform is invasive and expensive, so there remains a need for a reliable and well validated non-invasive approach. MethodsIn this study, a multi-channel Newton (MCN) blind system identification algorithm was employed to noninvasively reconstruct the CAP waveform from two PAP waveforms. In simulation experiments, CAP waveforms were recorded in a previous study, on 25 patients and the PAP waveforms (radial and femoral artery pressure) were generated by FIR models. To analyse the noise-tolerance of the MCN method, variable amounts of noise were added to the peripheral signals, to give a range of signal-to-noise ratios. In animal experiments, central aortic, brachial and femoral pressure waveforms were simultaneously recorded from 2 Sprague-Dawley rats. The performance of the proposed MCN algorithm was compared with the previously reported cross-relation and canonical correlation analysis methods. ResultsThe results showed that the root mean square error of the measured and reconstructed CAP waveforms and less noise-sensitive using the MCN algorithm was smaller than those of the cross-relation and canonical correlation analysis approaches. ConclusionThe MCN method can be exploited to reconstruct the CAP waveform. Reliable estimation of the CAP waveform from non-invasive measurements may aid in early diagnosis of cardiovascular disease.

Data-dependent orthogonal polynomials on generalized circles: A unified approach applied to [formula omitted]-domain identification

The performance of algorithms in system identification and control, depends on their implementation in finite-precision arithmetic. The aim of this paper is to develop a unified approach for numerically reliable system identification that combines the numerical advantages of data-dependent orthogonal polynomials and the discrete-time δ-domain parametrization. In this paper, earlier results for discrete orthogonal polynomials on the real-line and the unit-circle are generalized to obtain an approach for the construction of orthogonal polynomials on generalized circles in the complex plane. This enables the formulation of a unified framework for the numerically reliable identification of systems expressed in the δ-domain, as well as in the traditional Laplace and Z-domains. An example is presented which shows the significant numerical advantages of the δ-domain approach for the identification of fast-sampled systems.

System identification methods for dynamic models of brain activity

ObjectiveThere is a broad need to better understand the dynamics of neural activity in both space and time. Rigorous modeling methods are needed to improve the analysis of brainwave dynamics. Two system identification algorithms, Output Only Modal Analysis (OMA) and Dynamic Mode Decomposition (DMD), are modified for use on neural dynamics and compared. An example application is included. MethodsThe system identification methods are applied to estimate state space models for neural dynamics. The modeling technique results in a reduced order modal decomposition of the behavior of the brain. The resultant eigenmodes can be non-orthogonal and complex, capturing the emergent space time dynamics. We apply the modeling method to the Database for Emotion Analysis using Physiological Signals (DEAP) and the EEG Motor Movement/Imagery Dataset (EEGMMI) in a biosecurity application. ResultsIt is shown that there are common modes shared among all subjects, regardless of stimuli. Further, the modal decompositions may be used to distinguish subjects from one another in a subject identification biosecurity task. The accuracy of the OMA eigenmode model is 100%, while the accuracy of the DMD eigenmode model is 96%. ConclusionOutput only system identification techniques are an appropriate rigorous modeling method for EEG data. The structured modeling procedure offers new opportunities for cognitive modeling and affective computing.

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor.

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.

Design of Microelectronics Microsystem Neural Learning Controller Based on Subspace State Recognition

Aiming at the large delay and large inertia in cone storage and irrigation, this paper adopts machine learning and predictive control, proposes a closed-loop N4SID (numerical subspace state system identification) algorithm, which is adjusted by IMC based on Smith predictor PI controller and parameter tuning are given, and the steps designed in cascade control are given. The experimental results verify the effectiveness of the proposed method.Compared with closed-loop SVA and open-loop methods, the method proposed in this paper has better verification results and pole assignment. According to this model, the PI controller is adjusted using IMC based on Smith Smith predictor. Compared with the traditional ZN adjustment method, this method shows good results in a conical system. In addition, the PI parameters can be adjusted to obtain robust control using the H∞-based mixed sensitivity algorithm. The contribution of this paper is the use of closed subspace system identification to identify the MIMO state space. The developed model validates the IMC tuned PI controller based on the Smith predictor in the factory.

Prediction of local resonance band gaps in 2D elastic metamaterials via Bloch mode identification

This paper investigates methods of band gap formation in two-dimensional locally resonant elastic metamaterials and proposes a technique for computing such gaps as algebraic functions of integrated Bloch modes and metamaterial parameters. Mode shapes associated with the deformation of unit cells consisting of a hard matrix, soft filler, and hard resonator are investigated near the lower and upper bounds of the first local resonance band gap. Similarities reveal that Bloch modes at these bounds have the same non-dimensional geometries and locations within the irreducible Brillouin Zone irrespective of material properties, matrix and resonator thicknesses, and unit cell size. A theoretical model for the unit cell incorporating Mindlin plate theory is employed to compute the frequency of these modes in transverse vibration using conservation of energy. A least squares system identification algorithm is then described to generate surface equations for non-dimensional Bloch mode shapes computed by finite element analysis, employing a computation-saving Kronecker factorization and yielding continuous surface equations for the non-dimensional Bloch modes when rectangular cell components are utilized. Nine sample metamaterials are analyzed with this approach generating reliable predictions of the first band gap bounds. The presented framework offers insights into band gap formation, opens avenues in inverse unit cell design, and provides versatile algebraic relationships between band gap frequencies and unit cell parameters in such resonant metamaterials.

Vibration-based inverse graphical technique for thickness estimation of bulk acoustic wave (BAW) resonators: application for corrosion monitoring of sacrificial anodes

Resonant acoustic sensors have been used in a variety of applications that leverage the large sensitivity of mechanical resonance frequency to shift in geometric or mechanical properties of the sensor induced by the measurand. In applications such as structural health monitoring, accurate damage quantification requires development of precise models for resonator-measurand interactions or advanced system identification or optimization algorithms. In this paper, we utilize a graphical technique originally proposed for damage assessment in beams, to determine the extent of corrosion of sacrificial zinc anode discs instrumented with piezoelectric transducers. We develop analytical models for expressing natural resonance frequencies of the radial and transverse vibration modes of a disc in terms of material and geometric properties of its constituent elements. The underlying parameters (in this case, the thicknesses of the zinc and zinc oxide films) are extracted from measured resonance frequencies by finding roots of the characteristic determinant of these modes through graphical technique. Even though accelerated corrosion induced by impressed current results in non-uniform corrosion of the anode, the graphical technique with uniform-corrosion model shows excellent agreement with experimental results and is suitable for in-situ monitoring of sacrificial anodes used in cathodic protection systems. This technique requires no calibration or model of corrosion dynamics, and is computationally inexpensive unlike optimization techniques.

Impedance Parameters Estimation of An RLCM Ladder Network Using Subspace and Similarity Transformation Approach

ABSTRACT Numerous physical systems such as transformers, sensors, and biomaterials are analysed using their equivalent ladder network model. Impedance parameters of a ladder network consist of combinations of resistances, inductances, and capacitances. Accurately determining any device impedance parameters directly from the measured time-domain input-output data is challenging. The use of non-linear optimization algorithms does not always work due to the problem being ill-conditioned. Also, the obtained parameters may not be physically realizable. In this work, the problem is approached from a black-box system identification point of view, followed by a physical parameter extraction step. The necessary current and voltage equations obtained from first principles are utilized to build a physical model in the state-space domain. With the use of a subspace system identification algorithm, the black-box parameter matrices are determined from input-output data, which do not correspond to the physical parameter matrices. However, both sets of parameters can be related through a similarity transformation matrix, leading to the extraction of the physical parameters. The novelty of the approach lies in solving the non-linear parameter estimation problem linearly without initialization. The presence of mutual inductances between various transformer winding sections makes its ladder network the most complex one. Therefore, in this paper, the ladder network model of a transformer winding is considered. As an example of the methodology, a six-section transformer ladder network is simulated and the parameter estimation results are used to validate the proposed algorithm. The same approach can be applied to estimate any other device impedance parameters.

Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives.

Markovian Adaptive Filtering Algorithm for Block-Sparse System Identification

In this brief, a novel adaptive filtering algorithm for block-sparse system identification called, Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed. We use a Gaussian Markov (GM) model to generate the unknown block-sparse system. In the proposed algorithm, a maximum a posteriori (MAP) estimation procedure is used to estimate the adaptive filter coefficients which characterized by a Gaussian Mixture Markov (GMM) model. Moreover, the convergence in the mean of the proposed algorithm is provided in this brief. Simulation results show that the computational complexity of the proposed algorithm is less than some recent algorithms, while the performance is comparable to them.

Непараметрическая идентификация элементов системы терморегулирования

The thermal control system “Heater-Fan-Room” is represented by three different-type interconnected simpler subsystems. In this paper, a “black-box” whose structure is not specified is used as a mathematical model of the system and subsystems due to complexity of physical processes proceeding in these subsystems. For stationary linear systems, the connection between an input and an output of the “black-box” is defined by the Volterra integral equation of the first kind with an undetermined difference kernel also known as impulse response in the automatic control theory. In such a case, it is necessary to evaluate an unknown impulse response to use the “black-box” model and formulate all subsystems and the system as a whole. This condition complicates significantly the solution search of non-parametric identification problems in the system because an output of one subsystem is an input of another subsystem, so active identification schemes are unappropriated. Formally, an impulse response evaluation is a solution of the integral equation of the first kind for its kernel by registered noise-contaminated discrete input and output values. This problem is ill-posed because of the possible solution instability (impulse response evaluation in this case) relative to measurement noises in initial data. To find a unique stable solution regularizing algorithms are used, but the specificity of the impulse response identification experiment in the “Heater-Fan-Room” system do not allow applying computational methods of these algorithms (a system of linear equations or discrete Fourier transformation). In this paper, the authors propose two specific identification algorithms for complex technical systems. In these algorithms, impulse responses are evaluated using first derivatives of identified system signals that are stably calculated by smoothing cubic splines with an original smoothing parameter algorithm. The results of the complex “Heater-Fan-Room” system modeling and identification prove the efficiency of the algorithms proposed. Acknowledgments: The reported study was funded by RFBR, project number 20-38-90041.

Comparison of Crossover in Genetic Algorithm for Discrete-Time System Identification

System identification is a process where a mathematical model is derived in order to explain dynamical behaviour of a system. One of its step is model structure selection and it is crucial that, in this step, an adequate model i.e. a model with a good balance between parsimony and accuracy of the model is selected in approximating the system. Genetic algorithm (GA), a method known for optimisation, is used for selecting a model structure. GA is known to be able to reduce much computational burden. This paper investigates the effect of different types of crossover, namely, single-point, double-point, multiple-point and uniform crossover, within GA in producing an optimum model structure for system identification. This was carried out using a computational software on a number of simulated data. As a conclusion, using Akaike Information Criterion as objective function, single point crossover produces the model with the best balance in most of the tests.

Gradient algorithm for Hamiltonian identification of open quantum systems

In this paper, we present a gradient algorithm for identifying unknown parameters in an open quantum system from the measurements of time traces of local observables. The open system dynamics is described by a general Markovian master equation based on which the Hamiltonian identification problem can be formulated as minimizing the distance between the real time traces of the observables and those predicted by the master equation. The unknown parameters can then be learned with a gradient descent algorithm from the measurement data. We verify the effectiveness of our algorithm in a circuit QED system described by a Jaynes-Cumming model whose Hamiltonian identification has been rarely considered. We also show that our gradient algorithm can learn the spectrum of a non-Markovian environment based on an augmented system model.

Numerically stable algorithm for identification of linear dynamical systems by extended instrumental variables

Instrumental variables are widely used to identify linear dynamical systems. The advantages of instrumental variables include low computational complexity, as well as the possibility of identification for different models of color noise. Often the method of instrumental variables leads to ill-conditioned problems, which significantly limits the application of this method. The paper proposes a solution to the problem of extended instrumental variables based on augmented normal equations. Test examples showed a high accuracy of the proposed approach.

Particle Swarm Optimization based modelling of a surface ship using free running experimental approach

System modelling of the surface ship, which could be regarded as a multi-variable parameter estimation problem, has attracted tremendous interests from researchers since it is of key importance to development of ships’ control system. The aim of this study is to utilize the Particle Swarm Optimization (PSO) algorithm to get the optimized solution of hydrodynamic coefficients in the presence of ship’s mathematical model using the space search principle. The motion equations of the ship in four degrees of freedom are formulated to clarify the scheme of the model. After expressing the theory of the PSO method, the system identification of the ship’s motions is conducted to estimate the hydrodynamic coefficients. By using the identified parameters, the mathematical motion model of the free-running ship, namely ‘P and O Nedlloyd Hoorn’, is developed. The comparison between the experiment measurements and simulation trails and attitudes have indicated that the developed mathematical is sufficient and effective to response the ship’s motions. Therefore, it is demonstrated that the PSO based system identification algorithm is capable of conducting system identification and system modelling for the surface ship through free running approach.

A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. In this paper, two modifications were employed on the conventional Multi-Verse Optimizer. Our first modification was an average design parameter updating mechanism to solve the local optima issue. The second modification was the hybridization of Multi-Verse Optimizer with Sine Cosine Algorithm that will balance the exploration and exploitation processes and thus improve the poor searching capability. The proposed hybrid method was used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. A continuous-time linear subsystem was considered in this study, while there were a few methods that utilize such models. Furthermore, various nonlinear subsystems such as the quadratic and hyperbolic functions had been used in those experiments. The efficiency of the novel technique is illustrated using a numerical example and two real-world applications, which are a twin rotor system and a flexible manipulator system. The numerical and experimental results analysis were observed with respect to the convergence curve of the fitness function, the parameter deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon's rank test. The results showed that the proposed method was efficient in identifying both the Hammerstein model subsystems in terms of the quadratic output estimation error and parameter deviation index. The proposed hybrid method also achieved better performance in modeling of the twin-rotor system as well as the flexible manipulator system and provided better solutions compared to other optimization methods including Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer and Sine Cosine Algorithm.

Design and Development of a High-Speed UAS for beyond Visual Line-of-Sight Operations

This paper provides details on best practices for vehicle design for BVLOS missions, with a focus on robust control links and real-time health monitoring.The development work presented herein focuses on a specific mission to set the speed and distance world record for a fully autonomous vehicle, requiring development of analytic tools for estimating vehicle range and endurance, as well as models for predicting command and control link integrity while operating BVLOS. Range and endurance estimates are developed using a novel real-time system identification algorithm onboard the vehicle for aerodynamic parameter estimation, and propulsion system characterization. Flight-testing methods and results are presented demonstrating the capabilities of these algorithms, with results from a unique test-case involving an unintended fault during the record flight. In addition to the vehicle performance assessment, Friis-based models are introduced to provide a pseudo real-time communications link range assessment. The predictive communications range models were refined using specific antenna and system characterization data measured in a compact range facility resulting in a detailed link budget useful for addressing BVLOS command and control requirements. The combination of aircraft and radio-frequency performance prediction models with supporting flight-testing techniques applied to small unmanned aerial systems provides critical insight into the design of future vehicles, and validation of existing systems tasked with performing BVLOS missions.