Accurate System Model Research Articles

Exploring HCl-HCl interactions: QZVPP calculations, improved Lennard-Jones potential, and second virial coefficient analysis for thermodynamics and industrial applications.

In this paper, we present a comprehensive analysis of HCl-HCl interactions, including QZVPP calculations, energy fitting, conformation validation, and the determination of the second virial coefficient B using improved Lennard-Jones (ILJ) potential parameters. To acquire accurate interaction energies, initial QZVPP calculations are performed on approximately 1851 randomly generated HCl-HCl conformations. Then, these energies are used to fit an improved Lennard-Jones potential energy surface, allowing for a robust description of HCl-HCl interactions. The ILJ potential parameters are then used to validate particular HCl dimer conformations, ensuring their stability and consistency with experimental observations. The correlation between calculated and experimental conformations strengthens the validity of the ILJ potential parameters. In addition, the second viral coefficient B is calculated at various temperatures using the ILJ potential. The obtained B values are compared to experimental data, demonstrating close agreement, and validating the ILJ potential's ability to accurately capture the intermolecular interactions and gas-phase behavior of the HCl-HCl system. The results of this study demonstrate the effective implementation of QZVPP calculations, energy fitting, and ILJ potential parameters in validating HCl-HCl conformations and accurately determining the second virial coefficient B. The high degree of concordance between calculated B values and experimental data demonstrates the validity of the ILJ potential and its suitability for modeling HCl-HCl interactions. This research contributes to a greater comprehension of HCl-HCl interactions and their implications for numerous chemical and atmospheric processes. The validated conformations, energy fitting method, and calculated second virial coefficients provide valuable instruments for future research and pave the way for more accurate modeling and simulations of HCl-HCl systems.

Control of unstable water flow in open channels

The analysis and modeling of hydrodynamic processes in open channels are crucial for effective water resource management and hydraulic structure design. This study investigates unsteady water flow in main channels using the Saint-Venant equations. Analytical and numerical methods were employed to model various flow scenarios. The developed mathematical model effectively describes and analyzes flow characteristics such as discharge, free surface elevation, and flow velocity. The obtained results have practical applications in water resource management, flood forecasting, and infrastructure design for water conservation. Overall, this work contributes to the advancement of knowledge in open channel hydraulics and provides a basis for developing more accurate and reliable water system modeling methods. These equations allow for the simulation of hydrodynamic processes in rivers, channels, and reservoirs, enabling the prediction of water level fluctuations, flow velocities, and pressure distribution under various operating conditions. This plays a vital role in water resource management and the design of hydraulic structures.

District Cooling System Control for Providing Operating Reserve Based on Safe Deep Reinforcement Learning

Heating, ventilation, and air conditioning (HVAC) systems are well proved to be capable to provide operating reserve for power systems. As a type of large-capacity and energy-efficient HVAC system (up to 100 MW), district cooling system (DCS) is emerging in modern cities and has huge potential to be regulated as a flexible load. However, strategically controlling a DCS to provide flexibility is challenging, because one DCS services multiple buildings with complex thermal dynamics and uncertain cooling demands. Improper control may lead to significant thermal discomfort and even deteriorate the power system's operation security. To address the above issues, we propose a model-free control strategy based on the deep reinforcement learning (DRL) without the requirement of accurate system model and uncertainty distribution. To avoid damaging “trial & error” actions that may violate the system's operation security during the training process, we further propose a safe layer combined to the DDPG to guarantee the satisfaction of critical constraints, forming a safe-DDPG scheme. Moreover, after providing operating reserve, DCS increases power and tries to recover all the buildings' temperature back to set values, which may cause an instantaneous peak-power rebound and bring a secondary negative impact on power systems. Therefore, we design a self-adaption reward function within the proposed safe-DDPG scheme to constrain the peak-power effectively. Numerical studies based on a realistic DCS demonstrate the effectiveness of the proposed methods.

Base on -Learning Pareto Optimality for Linear Itô Stochastic Systems With Markovian Jumps

This article investigate the cooperative differential game (CDG) for continuous-time linear Itô stochastic systems with markovian jumps (SSMJ) to obtain the Pareto solutions. Different from most existing works studying nonzero-sum games, this article studies the CDG on the quadratic infinite horizon for the Itô-type SSMJ with unknown system matrix and transition probability. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning online algorithm is developed, which consists of that (i) the optimal control problem is equivalent to solving a stochastic algebraic Riccatic equation (ARE); (ii) the joint cost function is approximated by a critic neural network (NN) and Pareto efficient is approximated by two actor NNs. The rigorous stability analysis shows that the system state for SSMJ and the NN weight errors are uniformly ultimately bounded (UUB). Finally, the theory analysis is validated by a numerical example with detailed discussions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by the change of external environment or the failure of internal components, which leads to the random jump of system parameters. Markovian jump system can effectively describe the above problems. And when the system model is disturbed by the internal parameters, the system control input and external environment, the random errors of state measurement, and other random factors, the deterministic model can no longer accurately describe the controlled system. Therefore, the SSMJ can describe practical problems more accurately. By cooperation, in general, the cost one specific player incurs is not uniquely determined anymore. If all players decide, for example, to use their control variables to reduce the cost of player 1 as much as possible, a different minimum is attained for player 1 compared with that in the case where all players agree collectively to help a different player in minimizing his cost. So, depending on how the players choose to ‘divide’ their control efforts, a player incurs different ‘minima’. Therefore, we will design an online learning algorithm to obtain pareto solutions with different weights. On the other hand, in practice, it is difficult to obtain an accurate system model. In order to solve this problem, a novel scheme is designed by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning technology, which does not need system matrix.

Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine

With the rapid development of renewable energy, higher requirements are put forward for the control ability of thermal power units. And an accurate system model is the premise and guarantee of controller design. In this article, a novel dynamic recurrent fuzzy neural network with rule self-adaptation modeling approach based on chaotic quantum pigeon-inspired optimization (NDRFNN-CQPIO) is proposed for the modeling of gas turbine system. The introduction of recursive layer, automatic generation of fuzzy rules and fast parameter solving are the main features of the proposed algorithm. First, set the appropriate threshold, and process the system error and distance index through 4 preset cases to generate fuzzy rules. When a new rule is generated, its antecedent parameters are determined at the same time, and define a boundary to determine whether the antecedent parameters need to be adjusted, so as to avoid the generation of redundant Gaussian functions. Then, a pruning technique based on the contributions of the normalized neuron and learning error is raised to trim the redundant fuzzy rules to ensure the model structure more compact and efficient. Finally, using the chaotic quantum pigeon-inspired algorithm to solve the consequent parameters quickly and accurately. The actual operation data of a gas turbine system were selected to identify and verify the model. In the established model, the root mean square error of output power, exhaust gas temperature, rotor speed, and exhaust gas flow are 0.0023, 0.0015, 0.0011 and 0.0069, respectively. The simulation results show that the proposed approach is effective and feasible, and it can be used for the model identification of gas turbine system. Moreover, we conducted robustness analysis of the model and proved that the proposed model has strong robustness, which provides a basis for its further application.

Visual investigation of static contact conditions at bolted joints

Accurate numerical models of physical systems are vital in engineering applications, and those for single components are generally accurate. However, there are significant errors in modelling the dynamics of bolted joints, which is a key source of friction in real-life systems, because existing experimental techniques are still far from offering sufficient understanding of the contact conditions of bolted joints. In the current study, static contact conditions of bolted joints are investigated with at least one component in the assembly being transparent. A novel visual identification method is proposed to (i) measure the clearance between plates in the vicinity of bolted joints and (ii) identify the contact conditions in receding contacts by applying a paste (known as Engineer’s blue) to the contact surface. The paste is thinner than most media applied in previous studies. Clearance results obtained by visual inspection are checked against micrometer profile tracing, and the contact regions predicted by the method are used to validate simulation results obtained from two contact mechanics algorithms. Difference between simulation and experiment is as low as 9% in terms of contact region diameters. Last but not least, the reproducibility of the method has been demonstrated, especially if the clearance is in the range of 2 to 5 μm.

Semi-analytical dynamic modeling and fluid-structure interaction analysis of L-shaped pipeline

The traditional semi-analytic modeling is mainly aimed at the straight pipeline with simple boundary conditions. In contrast, the actual aero-engine pipeline needs to consider the effects of complex configuration, boundary and multiple clamps, so proposing an improved semi-analytic method to model an arbitrary fluid-conveying L-shaped pipeline is challenging. Based on the Hamiltonian principle, the differential equations of the L-shaped pipeline are derived. Then, the modal shape functions are determined under the conditions that the elastic support points and the joints of the straight pipeline and the curved pipeline meet the deformation coordination requirements. The proposed semi-analytical model is verified by comparing the natural frequencies and mode shapes obtained from the finite element method using ANSYS and experiment; the error of each order natural frequency is less than 5 %. In addition, the model is verified under various working conditions: different center angle and boundary conditions (clamp position and pipeline end constraints). The effects of the fluid velocity and pressure on the natural characteristics of the L-shaped pipeline are also analyzed. The results show that under the critical pressure of 60.51 MPa and the critical velocity of 247 m/s, the natural frequency decreases with the increase of fluid velocity and pressure. The proposed method can provide theoretical support for efficient and accurate modeling of complex spatial pipeline systems.

Active Disturbance Rejection Predictive Control for Local Trajectory Planning of Unmanned Ground Vehicles

In this paper, an active disturbance rejection predictive control strategy is proposed for trajectory planning task of unmanned ground vehicles. Rather than error estimation of accurate system modeling, internal error and environment disturbance are processed via single extended state observer. Nonlinear feedback control law is applied to reduce steady-state error significantly. Then motion planning and nonholonomic constraints could be handled via nonlinear model predictive control. Simulation and experiment results show that the proposed algorithm is able to accomplish trajectory planning task with internal error and environment disturbance.

Stages and main tasks of the century-long control theory and system identification development. Part III. The problem of complex systems identifying by inaccurate data

The article presents the main most noteworthy research results on the identification problem and methods that are widely used in solving identification problems. The most significant here is the problem of modeling especially complex systems, for which it is impossible, even in general, but in constructive form for identification, to write down a class of models in which the system under study has the most accurate description in which there is one of them adequate to the available data obtained from experiments. If the available knowledge on the object under study does not allow us to write down a suitable class of models in mathematical form, then asymptotic modeling should be used, as is done in computational mathematics. Instead of differential, integral and other equations, for example, algebraic equations approximating them are solved, the solutions of which approach the exact ones with increasing dimension and discretization step. Various forms of asymptotic expansions are used in identification, including rational approximation as applied to irrational and infinite-dimensional systems. However, in the presence of errors in the data, the use of asymptotic modeling leads to the fact that, with intentions to increase the accuracy of the resulting model due to its large dimension, in many cases the identification problems becomes incorrectly posed. Therefore, we have to look for a compromise or trade-off between bias and variance, which should determine the best-quality model. In addition to the modeling problem, the article discusses two concepts on the basis of which most existing methods for solving identification problems are based. Both of them give their own interpretation of the errors present in the data, namely, stochastic and nonstochastic. With the stochastic concept, errors are treated as a random process or sequence. The concept of estimation consistency is introduced, which makes it possible to use widely the theory of statistics in justifying methods for solving identification problems. The nonstochastic identification paradigm allows for arbitrary but bounded uncertainties in the data, i.e. belonging to some bounded sets. The largest number of different methods, as well as algorithms and software implementations, are made within the framework of the stochastic concept. Within the framework of nonstochastic identification, we mainly consider various modifications of the so-called subspace methods. In relation to complex systems, problems were noted that did not allow the development of sufficiently universal and effective methods for solving identification problems. A number of research areas have been pointed out that need further development in order to obtain more accurate models of complex systems.

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results.

Control Optimization of Air Supply System Based on Asymptotic Method Identification

In order to optimize the control quality of the air supply system aiming at its multi-disturbance, based on the historical operation data of the boiler air supply control system of a 300 MW unit, this paper adopts a two-stage identification method based on asymptotic theory and establishes an accurate model and simulation system of the air supply system. Based on the model established by asymptotic identification, we design a constrained Model Predictive Control with Kalman Filter state estimation and an Active Disturbance Rejection Control based on extended state disturbance observation two optimization schemes. The simulation based on field data shows that both schemes can be considered to possess comparatively higher practicability in the optimization of the air supply control system.

Modular model reduction of interconnected systems: A robust performance analysis perspective

Many complex engineering systems consist of multiple subsystems that are developed by different teams of engineers. To analyse, simulate and control such complex systems, accurate yet computationally efficient models are required. Modular model reduction, in which the subsystem models are reduced individually, is a practical and an efficient method to obtain accurate reduced-order models of such complex systems. However, when subsystems are reduced individually, without taking their interconnections into account, the effect on stability and accuracy of the resulting reduced-order interconnected system is difficult to predict. In this work, a mathematical relation between the accuracy of reduced-order linear-time invariant subsystem models and (stability and accuracy of) resulting reduced-order interconnected linear time-invariant model is introduced. This result can subsequently be used in two ways. Firstly, it can be used to translate accuracy characteristics of the reduced-order subsystem models directly to accuracy properties of the interconnected reduced-order model. Secondly, it can also be used to translate specifications on the interconnected system model accuracy to accuracy requirements on subsystem models that can be used for fit-for-purpose reduction of the subsystem models. These applications of the proposed analysis framework for modular model reduction are demonstrated on an illustrative structural dynamics example.

An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification

Identifying models with Infinite Impulse Response (IIR) is crucial in signal processing and system identification. This paper addresses the challenges of IIR model identification by proposing an improved version of the Artificial Rabbits Optimization (ARO) algorithm called improved ARO (IARO). The IARO algorithm integrates an adaptive local search mechanism and an experience-based perturbed learning strategy as two key enhancements to improve the effectiveness of ARO. These additions aim to address the loss of accuracy during iterations and improve the algorithm’s ability to exploit promising areas. Four benchmark examples of different IIR plants are considered, and the performance of the proposed IARO algorithm is compared to existing competitive methods. The results consistently demonstrate that IARO outperforms ARO in accuracy and convergence for system identification across all orders of IIR systems. Visual analysis, convergence curves, coefficient comparison, and statistical metrics comparison all validate the superiority of the IARO algorithm. Additionally, the Wilcoxon signed-rank test results provide further statistical evidence supporting the superior performance of IARO. The comprehensive analysis showcases the efficacy and effectiveness of the IARO algorithm in accurately identifying IIR systems. This work represents a significant advancement in IIR system identification, offering a superior methodology for accurate and efficient system modeling.

Model construction and software design of computed tomography radiation system based on visualization

The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.

Spiking neural network based heterogeneous federal filter method for sensor failure in multi-actuator systems

A Spiking neural network based heterogeneous federal filter method is proposed in this paper to deal with sensor failure problems in multi actuator systems. Firstly, an accurate dynamic model of multi-motor system is built, which can provide a high precision estimated state information in viral system state simulator. Secondly, a heterogeneous federal filter is proposed to provide a stable and accurate state feedback whether the sensor is normal or not. This filter collects real-time measured state data from different kinds of sensors and the estimated state information, and gives out an filtered state output for each sensor according the spiking neural network based reliability diagnosis module. By this way, the noise and disturbance can be eliminated. When one or more sensors break down, an estimated accurate state feedback can be obtained. Finally, this heterogeneous federal filter is applied into an four-motor servo system. The experiment results indicate that: the spiking neural network based reliability diagnosis module could quickly monitor sensor status accurately and the heterogeneous federal filter could handle sensor failures with control accuracy remained the normal level.

Cascaded Control System Design for Quadrotor UAV through Relay with Embedded Integrator-Based Automatic Tuning Approach

Due to their enormous characteristics and applicability, quadrotor unmanned aerial vehicles (UAVs) have enjoyed much popularity lately. However, designing a stable control strategy for quadrotors still remains one of the major concerns mainly due to the requirement of an accurate system model. They are naturally underactuated systems, with complex and nonlinear dynamics as well as interaxes couplings. Considering the dynamical complexities of these vehicles, one of the efficient methods is to utilize the relay feedback experiments and automatic tuning approach to tackle these issues. This paper investigates the employment of the relay with embedded integrator approach, wherein the quadrotor dynamics are estimated effectively with minimal parameters as compared to previously utilized relay with hysteresis technique. Frequency sampling filter (FSF) is further utilized for the extraction of the needful data through the signals obtained using the relay experiments, followed by the estimation of the plant dynamics. PID controllers have then been developed using the approximated quadrotor models. Which are used in the proposed cascade control structure for the quadrotor. The demonstrated results and analysis present the efficacy of designed control system technique for the quadrotor UAV.

An Improved Approach for Implementing Dynamic Mode Decomposition with Control

Dynamic Mode Decomposition with Control is a powerful technique for analyzing and modeling complex dynamical systems under the influence of external control inputs. In this paper, we propose a novel approach to implement this technique that offers computational advantages over the existing method. The proposed scheme uses singular value decomposition of a lower order matrix and requires fewer matrix multiplications when determining corresponding approximation matrices. Moreover, the matrix of dynamic modes also has a simpler structure than the corresponding matrix in the standard approach. To demonstrate the efficacy of the proposed implementation, we applied it to a diverse set of numerical examples. The algorithm’s flexibility is demonstrated in tests: accurate modeling of ecological systems like Lotka-Volterra, successful control of chaotic behavior in the Lorenz system and efficient handling of large-scale stable linear systems. This showcased its versatility and efficacy across different dynamical systems.

A Three-element Risley Prism Continuous Scanning System Based on Linear Constraint

In the three-element Risley prism system, non-linear errors, the infinite solution, the blind zone and the singularity are primary factors that affect the scanning accuracy of the system. In this paper, an accurate analytical model of the three-element Risley prism system is proposed. To reduce the errors of single-wedge nonlinear constraint equation, a dimensionality reduction method constrained by a single wedge is adopted. In addition, a reliable accuracy of 5mm is employed to achieve a full-area scanning of the system, and a single wedge linear constraint equation with an angular increment is established to control singularities. Simulation results show that the wedge rotation angle curve is stable and continuous in the process of inverse solution, and the full coverage of the scanning area can be achieved by assigning scanning accuracy to 5mm, which indicates that the proposed method can effectively solve the problems and achieve full-area scanning in the system.

An improved development process of production plants using digital twins with extended dynamic behaviour in virtual commissioning and control – Simulation@Operations

AbstractThe challenges in automation system development are driven by short development cycles and individualization along with resource‐constraints. State of the art solutions do not provide the necessary digital tools to apply model‐based methods in automation engineering to achieve higher performing systems. To overcome these issues this paper presents a novel approach to address some of the current challenges in automation systems development using digital twins with extended dynamic behaviour. The study underscores how dynamic models can be imported through standardised interfaces into virtual commissioning (VC) tools, improving the development process by effectively utilising domain‐specific expertise. The paper highlights how these digital twins enhance not only the VC process but can also be applied to model‐based control methods. Initial experiments showcase the utility of digital twins in calculating dynamic acceleration limits during trajectory planning of CNC control and enhancing feed‐forward control. Further, the importance of parameter identification in achieving accurate system models is stressed. The initial results are promising, and future work aims to combine these methods in an industrial application involving a newly developed, individual lightweight robot, demonstrating the potential for enhanced design, accelerated development, and resource efficiency in automation systems.

Vibration Control of Marine Flexible Riser with Uncertain Model

When flexible risers in the ocean are subjected to various environmental loads in the ocean, vibration will inevitably occur. Due to the nonlinearity and uncertainty of the actual riser system, it is impossible to obtain an accurate mathematical model of the riser system. The existing control algorithms designed based on the precise model of the riser system cannot meet the actual control requirements. A flexible riser boundary control method based on fuzzy control algorithm is proposed to suppress the vibration of marine flexible riser systems with uncertain models. Firstly, the dynamic models of existing marine flexible risers were studied. In response to the uncertainty of the riser system model, the control experience was transformed into an automatic control strategy in the form of fuzzy language control rules to improve the adaptability of the controller. Then, combining boundary control technology with fuzzy control, a boundary controller is constructed to stabilize the riser in a small neighborhood of its original position and reduce fatigue damage to the riser. Finally, the effectiveness of the designed fuzzy boundary control algorithm was verified through MATLAB simulation results, and the vibration suppression effect of the algorithm on the vertical tube was better than that of traditional PD control.