Identify System Parameters Research Articles

Implementation of ARM STM32F4 microcontroller for speed control of BLDC motor based on bat algorithm

Brushless direct current (BLDC) motor speed performance is an essential component that must be considered in the electric vehicle drive system. This research aims to obtain the transfer function from the mathematical modeling of the BLDC motor and optimization of the BLDC motor so that a high transient response is received. The method used in this study is to identify system parameters and then model them using the structure of the autoregressive exogenous (ARX) polynomial model. Meanwhile, to get a high transient response performance, optimization is done with a hybrid intelligent controller. The experimental findings indicate that the optimization process utilizing the bat method yielded the most favorable transient response. The resulting values for the proportional gain (Kp), integral gain (Ki), and derivative gain (Kd) were determined to be 31.2685, 7.7375, and -1.1934, respectively. In the current investigation, the minimum frequency value is 6381934.619. Also determined are the transient response characteristics, which include a rise time of 0.289 seconds, a settling time of 10.9 seconds, an overshoot of 3.4%, and a peak time of 1.04 seconds. Furthermore, the closed-loop system demonstrates stable behavior in terms of stability.

A new method for identifying the parameters of the Li-ion battery PNGV model

The article delves into modeling the Li-ion cell and the prerequisites for identifying system parameters. Initially, we construct a model that encapsulates the nonlinearity of our cell, employing a PNGV model encompassing dynamic parameters like state of charge (SOC), open-circuit voltage OCV, Cb, R0, R1 and C1. Subsequent to this, pertinent battery discharge tests are conducted to discern the model parameters.Commencing with establishing the relationship between state of charge (SOC) and the variable open-circuit voltage (OCV) using the least squares method, we progressed. Following determining thise relationship, cell discharge tests were employed to ascertain the values of R0, R1 and C1. Once the various parameters of the PNGV Model are identified, validation of this established battery model is performed via experimental discharge tests. The resulting error indicates a mean absolute error (MAE) of 0.0176V for modeling, alongside a root mean square error (RMSE) of 0.0232V. These outcomes underscore the remarkable accuracy of our cell model.

Моделирование и идентификация элемента Пельтье TEC1-12706 для применения в малообъемных биореакторах искусственного ЖКТ рыб

The bioreactor of a static mini-model of the artificial gastrointestinal tract of fish will provide modeling of processes in the gastrointestinal tract of industrially grown fish (carp, trout, and sturgeon). The study aimed to determine the thermodynamic processes occurring in the bioreactor and the possibility of using the thermoelectric converter TEC1‑12706 in the temperature control system of the bioreactor. The temperature in a bioreactor with a volume of up to 200 ml should vary from 45 to 14℃, the accuracy of maintaining the temperature is 0.1℃. As a result, a mathematical model was obtained with an adjustment according to the identified system parameters, which makes it possible to evaluate thermodynamic processes in the bioreactor, select hardware and create its general mathematical model. The parameters of the Peltier element were identified using a prototype layout measuring the temperature of the cold side of the converter, the hot radiator, the environment, the temperature of the liquid in the reactor, and the current consumed. The operation of the real system took place at an external temperature of 28.31℃; all physical drives were in the temperature equilibrium and under the same initial conditions. A comparison of temperature changes in the real system and the mathematical model obtained as a result of identifying the parameters of the Peltier element showed a non-perfect match of values, but the nature of the temperature change is identical. The following conclusions have been drawn: in the mathematical model, it is necessary to take into account additional drives and flows describing non-ideal conditions of experimental data, for example, thermal reflection of the working surface of the table and partial reflection of air flows. One third of the maximum converter power is sufficient to reduce the temperature of the filled bioreactor by 2 to 3℃. Thus, the thermoelectric converter TEC1‑12706 can be used in an in vitro modeling system of the gastrointestinal tract of fish.

Motion estimation and system identification of a moored buoy via physics-informed neural network

This paper explores the use of physics-informed neural networks (PINNs) to estimate motion and identify system parameters of a moored buoy under three different sea states. PINNs are a deep learning architecture that incorporates physical information to provide an interpretable and physically-meaningful neural network model, making it well-suited for modeling offshore moored structures with high nonlinearity. The moored buoy is modeled as a nonlinear ordinary differential equation (ODE), and the general formulation of PINN for the system of ODEs is established. Two new metrics for motion estimation and system identification are proposed to evaluate the accuracy and efficiency of the implementation of PINN. The results demonstrate that PINN can accurately estimate motion and identify system parameters by choosing appropriate hyperparameters (HPs). This paper also investigates the effects of the number of layers, nodes, and learning rate on motion estimation and system identification to provide a benchmark for selecting optimal HPs. This study finds that hyperparameter optimization can reduce the relative error of identified parameters by up to two thousand times compared to no optimization. Motion estimation prefers large neural networks for all sea states, while at least three layers of neural networks are needed for accurate parameter identification. The paper also provides a look-up table to investigate further implementing PINN on moored floating offshore structures. Proper selection of HPs is crucial as it can incur up to three orders of magnitude PINN loss and exceedingly-high identification error. Overall, this study highlights the applicability of PINN in modeling complex offshore structures and provides insights into selecting optimal HPs for accurate and efficient estimation of motion and system parameters.

Multi-beam piezoelectric systems by means of dynamically equivalent stiffness concept

Energy harvesting devices allow to obtain forms of energy present in nature and to convert them into electrical energy. One way of generating energy from mechanical vibrations is by using beams of piezoelectric materials. This paper proposes an alternative methodology for characterizing the dynamic behavior of a vibrating composite system composed of a cantilever steel base beam (primary system) and a piezoelectric beam attached to it. The approach involves representing the piezoelectric beam using an equivalent dynamic stiffness at its base. This simplifies the mathematical representation of the compound system and enables the system dynamics to be described solely in terms of the generalized coordinates of the primary system, which is advantageous in optimization environments where a reduced number of equations can facilitate analysis. To determine the equivalent dynamic stiffness, different mathematical models of one and multiple degrees of freedom are presented, including the description of the polyamide base of the piezoelectric sheet. An inverse problem is used to identify system parameters, and the energy generation over a wide range of frequencies is analyzed. Experimental frequency response functions of the voltage–acceleration type are obtained to validate numerical findings, demonstrating that the proposed methodology is a cost-effective alternative for parameter identification or optimal design in energy generation.

Data-Driven Identification of Nonlinear Power System Dynamics Using Output-Only Measurements

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.

Refrigeration via purification through repeated measurements

We design a measurement-based quantum refrigerator with an arbitrary number of qubits situated in a one-dimensional array that interact through variable-range XY interactions. The method proposed is based on repeated evolution followed by a measurement on the single accessible qubit, which has the potential to reduce the temperature in the rest of the subsystems, thereby demonstrating cooling in the device. The performance of the refrigerator is quantified by the fidelity of each local subsystem with the ground state of the local Hamiltonian and the corresponding probability of success. We identify system parameters, which include the interaction strength, range of interactions, initial temperature of each qubit, and the position of the measured qubit, so that the fidelities of all the unmeasured qubits approach unity with a nonvanishing probability. We observe that although strong interactions during evolution are required to achieve cooling, the long-range interactions typically deteriorate the performance of the refrigerator, which indicates that interactions are not ubiquitous. We report the scalability and the saturation property of the success probability with respect to the system size, which turns out to be independent of the involved system parameters and the number of repeated measurements. Furthermore, we show that the number of subsystems which can be cooled changes depending on the odd or even number of sites in the refrigerator. We argue that the distribution of entanglement between unmeasured qubits can give a possible explanation of the dependence of cooling process on the measured and unmeasured sites.

Physical layer security for Internet of Things via reconfigurable intelligent surface

Security countermeasures are considered one of the major concerns in the emerging Internet of Things (IoT) paradigm to guarantee an efficient network operation. To investigate the security problem in unauthorized access to sensitive IoT information and services, this paper, particularly, studies physical-layer security of a typical system model constituted by a reconfigurable intelligent surface (RIS) assisted access point (AP), legitimate user, and an eavesdropper. In this model, a RIS is deployed to increase the system secrecy and communication performances to deal with eavesdropping attacks. To measure the security performances, we firstly expose analytical results for the secrecy outage probability. We provide insights with asymptotic analysis to identify system parameters that significantly affect the secrecy performance of our proposed RIS-aided IoT systems. In particular, the findings show that by controlling the number of metasurface elements of the RIS and the average signal-to-noise ratios (SNR), system performances can be improved. We verify such a RIS-aided IoT system within many practical situations by conducting Monte-Carlo simulations in the Matlab simulation platform. We compared our analytical results with the Monte-Carlo simulations to evaluate the accuracy of the proposed closed-form expressions of secure performance metrics. Simulation results demonstrated that the secrecy performance in the IoT systems can be significantly improved by increasing the number of metasurfaces in the RIS and reducing channel quality approaching to eavesdroppers.

Layered Synchronization and Identification of Uncertain Delayed Hierarchical Networks

This paper investigates the layered synchronization and identification of uncertain delayed hierarchical networks, featured by highlights in three aspects: first, the presentation of a multilayered delayed network model, which can be regarded as a 3D stereochemical structure in compliance with the ever-developing trend of hierarchical network modeling structure; second, the design of a multi-identification scheme combined with uncertain delayed hierarchical networks, which matches the unknown and hierarchical properties of the proposed networks; and third, the construction of a layered control method of Lyapunov function, which synthesizes the factors of the unknown, time delay and hierarchy. Based on these points, the laws of layered synchronization between drive hierarchical networks (DHNs) and response hierarchical networks (RHNs) are derived, the dual identification criteria for monitoring topological structures and identifying system parameters, respectively, are provided, and the hierarchical multiformity of the proposed method is validated through abundant numerical simulations in the end.

Sustainability Analysis of Interconnected Food Production Systems via Theory of Barriers

Controlled environment agriculture (CEA) is used for efficient food production. Efficiency can be increased further by interconnecting different CEA systems (e.g. plants and insect larvae or fish and larvae), using products and by-products of one system in the other. These interconnected systems define an overall system that can be described by models of interacting species. It is necessary to identify system parameters (e.g. initial species concentration, harvest rate, feed quality, etc.) such that the resources are not exhausted. For such systems with interacting species, modelled by the Lotka-Volterra equations, a set-based approach based on the recent results of the theory of barriers to exactly determine the so-called admissible set (also known as viability kernel) and the maximal robust positively invariant set is presented. Using an example of a larvae-fish based production system, steps to obtain special trajectories which are the boundaries of the admissible set are shown. This admissible set is used to prevent the under and over population of the species in the CEA. Furthermore, conditions of the system parameters are stated, such that the existence of these trajectories can be guaranteed.

Model-Based Digital Image Correlation for Noncontact Deformation Measurement of Strain Field and Mechanical Property

This paper presents a new method that combines a physics-based model and finite-element (FE) based digital image correlation (DIC) with an interfacial continuity condition for simultaneous deformation measurements of strain field and material mechanical properties. The dual-domain global DIC method incorporates a model-based kernel that offers useful physical insights into the complex deformation and constrains the solutions to the iterative minimization of the DIC criterion, which leads to a faster convergence. Unlike local subset-based and global FE-based methods where the DIC is formulated to solve for the nodal displacements, the model-based DIC that accounts for complex deformation in the specific region is formulated to identify system parameters. Both domains, along with their interface and the continuity condition enforced on the global displacement field, are discretized and solved simultaneously in a single calculation. The dual-domain global DIC method has been investigated by comparing reconstructed strain fields and estimated parameters against targets simulated using the commercial FEA software COMSOL and published experimental data. Unlike FE-DIC which is noise sensitive, the model-based DIC is robust against the Gaussian noise effects.

Accessibility Measurement for Project Prioritization in Virginia

This paper describes the accessibility scoring approach applied by the Virginia Department of Transportation (DOT) in the Smart Scale project prioritization process in 2018. The accessibility scoring approach identifies the increase in jobs accessibility for candidate projects submitted for state funding. The Smart Scale process was implemented in 2015 and entered its third round of applications in 2018; some 800 projects were evaluated during its first two years. This paper contains the following elements: an general overview of jobs accessibility as defined and measured by Virginia DOT for the Smart Scale approach; the development of the Smart Scale accessibility scoring system, including a summary of research performed to identify system parameters; the relationship between mobility and accessibility; and the Smart Scale accessibility transferability to other locations and initiatives and the possible evolution of the Virginia DOT approach in the future.

Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System.

To improve the controllability of an electro-hydraulic position servo control system while simultaneously enhancing the anti-jamming ability of a PID controller, a compound PID controller that combines the beetle antennae search algorithm with PID strategy was proposed, and used to drive the position servo control system of the electro-hydraulic servo system. A BAS-PID controller was designed, and the beetle antennae search algorithm was used to tune PID parameters so that the disturbance signal of the system was effectively restrained. Initially, the basic mathematical model of the electro-hydraulic position servo control system was established through theoretical analysis. The transfer function model was obtained by identifying system parameters. Then, the PID parameter-tuning problem was converted into a class of three-dimensional parameter optimization problem, and gains of PID controllers were adjusted using the beetle antennae search algorithm. Finally, by comparing the effectiveness of different algorithms, simulation and experimental results revealed that the BAS-PID controller can greatly enhance the performance of the electro-hydraulic position servo control system and inhibit external disturbances when different interference signals are used to test the system’s robustness.

Computer-Aided Controller Design for a Nonlinear Process Using a Lagrangian-Based State Transition Algorithm

Model parameter estimation and controller tuning of the nonlinear clarification process are primary issues in the sugar industry. This paper proposes a Lagrangian-based state transition algorithm (LSTA) for controlling the clarification process and identifying the model parameters. First, the standard system identification procedure is used to estimate the model parameters for the clarifier. Then, the LSTA is used to enhance the accuracy of the identified system parameters. Once a suitable model is obtained, the proposed LSTA is used to fine-tune the optimal controller parameters off-line. We choose two types of evolutionary algorithms for comparison to show the efficacy of the proposed design. The results of the simulations indicate that the proposed controller achieves optimum transient and tracking performance in all cases. For fast convergence and the ability to provide the global optimum when estimating the modeling and controller parameters, the LSTA performance was found to be superior to that of the other algorithms.

Colloidal Brazil nut effect in microswimmer mixtures induced by motility contrast.

We numerically and experimentally study the segregation dynamics in a binary mixture of microswimmers which move on a two-dimensional substrate in a static periodic triangular-like light intensity field. The motility of the active particles is proportional to the imposed light intensity, and they possess a motility contrast, i.e., the prefactor depends on the species. In addition, the active particles also experience a torque aligning their motion towards the direction of the negative intensity gradient. We find a segregation of active particles near the intensity minima where typically one species is localized close to the minimum and the other one is centered around in an outer shell. For a very strong aligning torque, there is an exact mapping onto an equilibrium system in an effective external potential that is minimal at the intensity minima. This external potential is similar to (height-dependent) gravity such that one can define effective "heaviness" of the self-propelled particles. In analogy to shaken granular matter in gravity, we define a "colloidal Brazil nut effect" if the heavier particles are floating on top of the lighter ones. Using extensive Brownian dynamics simulations, we identify system parameters for the active colloidal Brazil nut effect to occur and explain it based on a generalized Archimedes' principle within the effective equilibrium model: heavy particles are levitated in a dense fluid of lighter particles if their effective mass density is lower than that of the surrounding fluid. We also perform real-space experiments on light-activated self-propelled colloidal mixtures which confirm the theoretical predictions.

A simple approach for system level thermal transient prediction

The approach proposed is a method for system level thermal transient calculation. The method is developed to predict the thermal behavior of an electronics system to optimize system performance while under thermal management control. The foundation of establishing the thermal transient response formula is energy method. It is used to simplify a complicated heat flow path into a mathematic model. From this mathematic model, an energy equation is established and a single RC pair of formula derived from the equation is identified. Using curve fitting technique, the unknowns on the formula, the equivalent thermal capacitance and resistance of the system, can then be acquired. This approach can quickly identify system parameters and establish the formula of a system’s thermal transient response without complicated calculation. Experimental data shows the error of the thermal response prediction is within 10%. This method can be used to predict the system performance of any electronics device including systems having thermal managing mechanism.

Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses

ABSTRACTThis paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-time system is a linear system, its output response is a highly nonlinear function with respect to the system parameters. In order to propose a direct estimation algorithm, a criterion function is constructed between the response output and the observation output by means of the discrete sampled data. Then a scheme by combining the Newton iteration and the least squares iteration is builded to minimise the criterion function and derive the parameter estimation algorithm. In light of the different features between the system parameters and the output function, two sub-algorithms are derived by using the parameter decomposition. In order to remove the associate terms between the two sub-algorithms, a Newton and least squares iterative algorithm is deduced to identify system parameters. Compared with the Newton iterative estimation algorithm without the parameter decomposition, the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced because the dimension of the Hessian matrix is lessened after the parameter decomposition. The experimental results show that the proposed algorithm has good performance.

Non-adiabatic quantum state preparation and quantum state transport in chains of Rydberg atoms

Motivated by recent progress in the experimental manipulation of cold atoms in optical lattices, we study three different protocols for non-adiabatic quantum state preparation and state transport in chains of Rydberg atoms. The protocols we discuss are based on the blockade mechanism between atoms which, when excited to a Rydberg state, interact through a van der Waals potential, and rely on single-site addressing. Specifically, we discuss protocols for efficient creation of an antiferromagnetic GHZ state, a class of matrix product states including a so-called Rydberg crystal and for the state transport of a single-qubit quantum state between two ends of a chain of atoms. We identify system parameters allowing for the operation of the protocols on timescales shorter than the lifetime of the Rydberg states while yielding high fidelity output states. We discuss the effect of positional disorder on the resulting states and comment on limitations due to other sources of noise such as radiative decay of the Rydberg states. The proposed protocols provide a testbed for benchmarking the performance of quantum information processing platforms based on Rydberg atoms.

A Fokker–Planck–Kolmogorov equation-based inverse modelling approach for hydrological systems applied to extreme value analysis

Abstract This paper takes a stochastic approach to identify uncertainties in hydrological systems that can be applied to the study of hydrological extremes. The system to be identified is supposed to be governed by a stochastic differential equation of the Langevin type, whose parameters are found through the inverse solution of the equivalent Fokker–Planck–Kolmogorov equation. The study presents the algorithmic and numerical implementation for the inverse modelling process, along with the implementation of this approach in three study areas. Results showed a flexible method that made it possible to consider hydrological variability and seasonality during system identification. The identified system parameters rely on the internal–external driving factors of the analysed river basin and provide indications about the behaviour of extreme events in possible future climate scenarios or situations where internal system parameters are altered. While the study cases presented refer to non-stationary Markov processes that allow for one-dimensional systems identification only, the proposed methodological approach is a step in the right direction when it comes to identifying n-dimensional Markov processes/systems.

Data-based controllability analysis for generalised linear discrete-time system

ABSTRACTIn this paper, we aim to propose a data-based method to testify whether the system is a normal linear system or a generalised linear system and then analyse its controllability via the measured state data and the control input satisfying certain condition. For this purpose, we first describe a linear discrete-time system in a general form and derive a necessary and sufficient condition for the equivalent condition of complete controllability with a normal linear discrete time system. Second, we check whether the system is normal or singular, and then construct the controllability matrices only based on the input and measured state data. Third, the controllability of the corresponding system is investigated thoroughly based on available data without identifying system parameters. Finally, a numerical example and a stock price example are used to show the effectiveness and feasibility of the proposed data-based method.