Modeling Control Research Articles

Migration-connected networks of Lotka–Volterra and quasi-polynomial systems: modeling and decentralized control

AbstractThis paper introduces a modeling and a control approach for Lotka-Volterra systems that are interconnected through population size-dependent migration flows. First, a control-oriented model is proposed for networks of Lotka-Volterra systems. Based on this model, a decentralized control method is introduced which assures that the states of each Lotka-Volterra system in the network can be driven into a prescribed setpoint regardless of migration. The results have been generalized to quasi-polynomial systems, and networks of Lotka-Volterra systems having interconnections with distributed delay. Simulation experiments are also presented in the paper to show the implementability of the theoretical results.

Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

TinyML-powered ensemble modeling for greenhouse climate control using XGBoost and LightGBM

The cultivation of crops in smart greenhouses is experiencing a profound transformation, fueled by cutting-edge technological advancements in environmental control that significantly improve efficiency, sustainability, and productivity. Nonetheless, the intricate and ever-changing dynamics of microclimate conditions pose challenges in customizing environments to satisfy the specific requirements of various plants. Accurate prediction of these microclimate parameters emerges as a promising solution to this challenge. This study explores the integration of machine learning and TinyML platforms to create a groundbreaking ensemble approach for effectively forecasting microclimate conditions. We obtained exceptional prediction accuracy for temperature (R2 = 0.9972) and humidity (R2 = 0.9976) using a stacking ensemble of XGBoost and LightGBM models. We used Optuna for accurate hyperparameter optimization and thoroughly examined the best possible input variable combinations as part of our meticulous model construction approach. The results of this study demonstrate the revolutionary potential of machine learning in greenhouse climate management, opening the door for data-driven, intelligent agricultural systems that maximize crop yields while reducing energy consumption.

A control-oriented comprehensive model of PEM electrolyzer considering double-layer capacitance

The modeling of electrochemical and thermal characteristics of proton exchange membrane (PEM) electrolyzer is significant in the hydrogen production process. However, the calculation of electrode impedance and double-layer capacitance during operation is not accurate enough currently, and the electrothermal coupling characteristics of hydrogen production devices are not fully considered. In this paper, the multi-stage equivalent circuit of the PEM electrolyzer is designed using electrochemical impedance spectroscopy (EIS) and the time-constant fitting method. In addition, according to the temperature control system of the actual hydrogen production device, the electro-thermal model of the PEM electrolyzer is established, in which the fuzzy PID control is used to maintain the temperature stability. Moreover, the electrical characteristics and temperature characteristics are simulated in different time scales. Finally, compared to the existing models, the proposed model is more accurate, and it indicates that the proposed model has better accuracy against the existing models.

GSOR06 Presentation Time: 11:55 AM: Dosimetric Analysis and Machine Learning Predictive Modeling of Local Control in Cervical Cancer

GSOR06 Presentation Time: 11:55 AM: Dosimetric Analysis and Machine Learning Predictive Modeling of Local Control in Cervical Cancer

Comprehensive energy footprint of electrified fleets: School bus fleet case study

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel under the average price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

Synthesis of a parametrically robust controller with an extended state observer

This paper considers the problem of control of a parametrically uncertain dynamic system using an extended state observer. The control system is implemented using a two-loop scheme, where the outer loop ensures the fulfillment of the selected criterion for controlling the state vector, and the inner loop compensates for or reduces the influence of the vector of the total equivalent disturbance. The goal of the study is to develop a procedure for synthesizing an extended state observer taking into account the parametric uncertainty of the plant and the requirements for the closed loop of the combined control system specified in the frequency domain. Methods of control theory, robust control, and computer modeling are used for this study. The effect of the parametric uncertainty of the plant on its controlled motion is presented as a structured disturbance described in the form of a block-diagonal matrix. The concept of structured singular values is used to determine the measure of the system's robustness. Using the methodology of optimization of structured singular values, a procedure is proposed for synthesizing the extended state observer when considering a closed loop of a combined control system taking into account the parametric uncertainty of its mathematical model. The requirements ensuring the specified performance and stability of the closed loop of the control system taking into account the spectral properties of disturbances and sensor noise are formulated in the frequency domain using frequency-dependent weighting functions. For the synthesis of combined controllers based on the minimization of structured singular values, a D-G-L-K iteration algorithm is developed. The efficiency of the proposed approach is illustrated by a numerical example. Recommendations are given for the synthesis of parametrically robust combined controllers. The practical value of the obtained results lies in the fact that the procedure developed reduces the conservatism of robust combined control systems and, as a result, improves the control performance in the case of parametric uncertainty of the plant.

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

Preliminary Control-Oriented Modeling of the ITER Steering Mirror Assembly and Local Control System in the Electron Cyclotron Heating & Current Drive Actuator

Preliminary Control-Oriented Modeling of the ITER Steering Mirror Assembly and Local Control System in the Electron Cyclotron Heating & Current Drive Actuator

Research on trajectory planning based on optimal control modeling

With the advancement of autonomous driving technology, trajectory planning has emerged as one of the core technologies to achieve safe and efficient vehicle driving. An algorithm for single-vehicle trajectory planning based on the Optimal Control Problem (OCP) is proposed in this paper, which can not only plan a safe and efficient path for autonomous vehicles, but also realize the obstacle avoidance function. Firstly, the OCP is modeled for the cycle trajectory planning problem. Secondly, this paper transforms it into a Nonlinear Programming (NLP) with all discrete methods, and uses the interior point method to solve it. Then, the Y-type intersection is selected as the verification scene, and through a series of simulation experiments, the efficiency and safety of the algorithm in different driving environments are verified. The model and algorithm proposed in this paper are not only applicable to Y-type intersections, but also can be extended to other complex traffic roads, providing a new idea and method for trajectory planning in intelligent transportation systems.

Visuality of M. Elizarov’s Novels “Librarian” and “Cartoons” as a Means of Transforming Socialist Realist Canon

This article examines the role of the visual aspect of the text in the recontextualization of elements from the socialist realist canon. The study focuses on Mikhail Elizarov’s novels “Librarian” and “Cartoons.” It is established that, firstly, Elizarov consciously constructs his works according to the principles of literary cinematicity, with visuality serving as a prominent component. Secondly, visuality facilitates the integration of elements from the Soviet project into the narrative, expressed through spaces and objects. Furthermore, it allows for their recontextualization by embedding them within new contexts. Thirdly, the reactivation of spaces and objects is aided by the figure of the protagonist, who is presented through the technique of traveling. Fourthly, the narrative development of the novels bears similarities to socialist realist plots concerning re-education. Additionally, through the image of the protagonist, the relevance of the opposition between “eye” and “gaze” is emphasized, which in turn contributes to the actualization of ideas related to control and behavior modeling. The gaze of the Other, experienced by the characters, draws them into narrative structures akin to those found in socialist realism, compelling them to adopt specific roles — the role of a character burdened with socialist realist motives. Finally, it is noted that the protagonist’s distinctive position enhances reader identification and alters the very status of the Book.

Real-Time Analysis and Digital Twin Modeling for CFD-Based Air Valve Control During Filling Procedures

Air exchange in pressurized water pipelines is an essential but complex aspect of pipeline modeling and operation. Implementing effective air management strategies can yield numerous benefits, enhancing the system’s energy efficiency, reliability, and safety. This paper comprehensively evaluates an irregular profile pipeline filling procedure involving air-release through an air valve. The analysis includes real-time data tests and numerical simulations using Computational Fluid Dynamics (CFD). A Digital Twin model was proposed and applied to filling maneuvers in water installations. In particular, this research considers an often-overlooked aspect, such as filling a pipe with an irregular profile rather than a simple straight pipe. CFD simulations have proven to capture the main features of the transient event, which are suitable for tracking the air-water interface, the unsteady water flow, and the evolution of the trapped air pocket. Thus, they provide thorough and reliable information for real-time operational processes in the industry, focusing on the filling pressure and geometry of the air-valve hydraulic system. Additionally, this study provides details regarding the application of an efficient Digital Twin CFD approach, demonstrating its feasibility in optimizing the filling procedure in pipes with irregular profiles.

Gust load alleviation of a flexible flying wing with linear parameter-varying modeling and model predictive control

This paper presents a practical model predictive control (MPC) framework for gust load alleviation of a flexible flying wing. Both the controller solving and state estimation are based on a reduced-order model, which features a linear parameter-varying (LPV) form, avoiding online linearization and reducing the scale of the corresponding quadratic programming problem. An improved modeling and model reduction process is used to enhance modeling efficiency and ensure that the reduced-order model can accurately capture the rigid-flexible coupled characteristics of the flexible flying wing under arbitrary gusts. By reconstructing the output of the control-oriented model to include both rigid-body motion and flexible vibrations, the rigid-flexible coupled multi-objective control is established as an MPC problem for reference tracking. The online optimization is formulated in a sparse fashion and combined with an iterative algorithm based on predicted trajectories, describing the variation of model dynamics within the prediction horizon more accurately. With a time-varying Kalman estimator for state updating, the closed-loop simulations are performed for gust alleviation performance validation. Additionally, the real-time potential of the proposed MPC framework is demonstrated through Monte Carlo simulations.

Hierarchical MPC-based authority allocation strategy for human–machine shared vehicles considering human–machine conflict

The uncertainty of driver behavior is an important factor affecting the safety of human–machine co-driving vehicles. Traditional rule-based models often fail to capture the nonlinear and complex characteristics of human steering behavior. To overcome this, we propose a data network-driven approach utilizing gated recurrent unit (GRU) neural networks to accurately predict driver steering behavior. The GRU-based driver model is integrated with vehicle dynamics to construct a control-oriented driver–vehicle model. Considering that human–machine conflict may cause vehicle instability, an exponential function combining proportional–integral–derivative is proposed to quantify the human–machine conflict level based on steering difference. To reasonably allocate human–computer permissions based on human–machine interaction, a hierarchical authority allocation framework is proposed. The upper layer provides a reference authority allocation via an exponential function, while the lower layer employs a real-time model predictive control (MPC) optimizer to track this reference, ensuring optimal vehicle path tracking and stability. The proposed system’s effectiveness is validated through driver-in-the-loop testing, demonstrating significant improvements in safety and performance. The results show that in the human–machine conflict scenario, the proposed authority allocation strategy can still ensure the path tracking and safety of the vehicle.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

Koopman-Based Hybrid Modeling and Zonotopic Tube Robust MPC for Motion Control of Automated Vehicles

Koopman-Based Hybrid Modeling and Zonotopic Tube Robust MPC for Motion Control of Automated Vehicles

Koopman Modeling for Optimal Control of the Perimeter of Multi-region Urban Traffic Networks

The purpose of perimeter control is to regulate the transfer flow between the perimeters of the urban traffic network, so that the vehicle aggregation in each region is maintained at a desired level. It is regarded as one of the most important methods to solve traffic congestion in urban road networks. Accurate mathematical modeling of perimeter controlled traffic dynamics remains a challenge due to its high nonlinearity and uncertainty. Machine learning methods have been used to learn traffic dynamic models for perimeter control. However, these existing techniques lack generalization and interpretability. In this paper, we propose a Koopman modeling approach (i.e., a two-stage method) that uses a new eigenfunction of the Koopman operator based on a novel L0 norm approximation to construct an interpretable finite-dimensional approximation of the Koopman operator, which is a linear operator that describes how eigenfunctions evolve along the trajectory of a specific nonlinear dynamical system. The main advantage of utilizing the Koopman operator is that it can characterize the nonlinear system in a global linear lifted feature space. Furthermore, an algorithm based on the Koopman modeling method and model predictive control is proposed for real-time perimeter control of urban road networks. Some experiments are carried out to demonstrate the effectiveness of the proposed algorithm.

A novel robust adaptive control for nonlinear uncertain quarter-vehicle suspension system in presence of unknown time delay actuation

This paper presents a novel robust adaptive control approach for nonlinear uncertain vehicle suspension system with time delayed actuation and bounded disturbances. The uncertainty and disturbance as well as the input delay on the system are all limited and unknown. This paper explores a control-oriented nonlinear model to accurately describe the dynamics of the vehicle suspension which incorporates uncertainty, disturbance and actuator delay. The controller is designed based on robust and adaptive approaches, which along with guaranteeing general goals for the suspension system is able to assure the stability of the closed-loop system in the Lyapunov concept. Also, due to the use of smooth functions in the robust controller structure, sudden changes in the behavior of system states are prevented. The simulation and comparison results in MATLAB environment show the efficiency of the proposed robust adaptive method in covering the effects of uncertainty, disturbance and time-variant actuator delay.

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

A Regional-Scale Assessment-Based SARS-CoV-2 Variants Control Modeling with Implications for Infection Risk Characterization.

The emergence and progression of highly divergent SARS-CoV-2 variants have posed increased risks to global public health, triggering the significant impacts on countermeasures since 2020. However, in addition to vaccination, the effectiveness of non-pharmaceutical interventions, such as social distancing, masking, or hand washing, on different variants of concern (VOC) remains largely unknown. This study provides a mechanistic approach by implementing a control measure model and a risk assessment framework to quantify the impacts of control measure combinations on the transmissions of five VOC (Alpha, Beta, Delta, Gamma, and Omicron), along with a different perspective of risk assessment application. We applied uncontrollable ratios as an indicator by adopting basic reproduction number (R 0) data collected from a regional-scale survey. A risk assessment strategy was established by constructing VOC-specific dose-response profiles to implicate practical uses in risk characterization when exposure data are available. We found that social distancing alone was ineffective without vaccination in almost all countries and VOC when the median R 0 was greater than two. Our results indicated that Omicron could not be contained, even when all control measure combinations were applied, due to its low threshold of infectivity (~3×10-4 plague-forming unit (PFU) mL-1). To facilitate better decision-making in future interventions, we provide a comprehensive evaluation of how combined control measures impact on different countries and various VOC. Our findings indicate the potential application of threshold estimates of infectivity in the context of risk communication and policymaking for controlling future emerging SARS-CoV-2 variant infections.