Control Framework Research Articles

Contributing to the global public good: the case of NATO

ABSTRACT While the provision of global public goods generally suffers from free-riding, standard theory also predicts that countries with stronger preferences for the public good contribute more. We take the case of NATO countries’ military expenditures after Russia’s annexation of Crimea as a natural experiment to test the prediction within a synthetic control framework. We find that NATO members with a land border with Russia or Ukraine increased their military expenditures more than synthetically constructed control countries, confirming the prediction. Moreover, NATO countries without a land border to Russia or Ukraine did not decrease their military expenditures.

Multi-layer process control in selective laser melting: a reinforcement learning approach

AbstractPowder bed fusion (PBF) is an original additive manufacturing technique for creating 3D parts layer-by-layer. While there are numerous benefits to this process, the complex undergoing physical phenomena are challenging to analytically model and interpret. Hence, integrated and control-oriented 3D models are lacking in the current literature. As a result, the state of the art in process control for the powder bed fusion (PBF) process is not as advanced as in other manufacturing processes. Reinforcement learning is a machine learning, data-driven mathematical and computational framework that can be used for process control while addressing this challenge (lack of control-oriented models) effectively. Its flexible formulation and its trial-and-error nature make reinforcement learning suitable for processes where the model is intricate or even unknown. The focus of this research work is selective laser melting, which is a laser-based PBF process. For the first time in the literature we demonstrate the benefits of a reinforcement learning process control framework for multiple layers (complete 3D parts) and we highlight the importance of stability during training. The presented case studies confirm the effectiveness of the proposed control framework, directly addressing heat accumulation issues while demonstrating effective overall process control, hence opening up opportunities for further research and impact in this area.

Normative data for instrumented posturography: a systematic review and meta-analysis

Postural control is a multisensory adaptive system performing predictive (anticipatory) and/or reactive (compensatory) actions, with varying degrees of accuracy, to maintain balance in a changing environmental context. Common instrumentation to evaluate balance includes static and dynamic force platforms; added sway-referenced perturbations on the dynamic platform constitute its main advantage. Clinical applications notwithstanding, normative data are needed for interpretation in clinical settings. Posturography norms are used to compare a reference group (healthy individuals) and a specific patient population. This work, to the best of our knowledge, represents the first attempt to synthesize the literature on normative data for computerized posturography using a combined mixed method. The search strategy resulted in the retrieval of 1,244 articles from PubMed, Web of Science, and Science Direct. After deduplication, 689 articles were screened based on title and abstract. One hundred and seven articles met the criteria after the first screening. In-depth, full-text screening resulted in the inclusion of 44 studies for the systematic review and 17 studies for the meta-analyses. The main findings of the systematic review are (1) extensive heterogeneity was found in methodological characteristics, (2) there was insufficient risk of bias mitigation, (3) the majority of tasks evaluated less than four components of the systems framework for postural control (SFPC), and (4) studies mostly used distance domain sway parameters and did not report the influence of other variables on postural sway. Based on the multilevel meta-analyses, females appeared to outperform males in eyes closed (EC) conditions significantly. Based on the network meta-analyses, we found that younger children swayed more than those aged between 8 and 14 years both in eyes open (EO) conditions and EC conditions significantly. The results also revealed a significant difference in sway between individuals of age range between 50 and 79 years old and younger individuals, with more instability observed in older participants both in EO conditions and in EC conditions. Thus, future studies need to ensure that enough information about participants is provided. Standardization of experimental conditions and sway parameters harmonization are still needed to ensure high-quality assessment (QA). Finally, evidence-based postural impairment management requires both age- and sex-related normative data.Systematic review registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023378144, identifier PROSPERO 2023 CRD42023378144.

Optimal Control of a Harmonic Oscillator with Parametric Excitation

This paper addresses the time-optimal control problem for a harmonic oscillator characterized by a time-dependent frequency. The primary objective is to determine the minimal time required to transition the system from an initial state, defined by a given position and velocity, to a specified final state, while ensuring that the frequency remains within prescribed bounds. The key challenge lies in identifying the optimal switching times between two available frequencies to meet all boundary conditions efficiently. By examining various boundary scenarios, constructing the reachable set of all admissible trajectories, and employing both analytical techniques and control theory, we develop a robust solution strategy. This work holds particular relevance for practical applications demanding rapid state transitions, such as mechanical vibration control and signal processing, where achieving time-optimal performance is critical. Furthermore, the methods presented are adaptable to a wide range of systems facing similar constraints, providing a versatile and effective framework for time-optimal control.

Heat Stress Prevention in Construction: A Systematic Review and Meta-Analysis of Risk Factors and Control Strategies

In hot and humid work environments, construction workers can experience heat stress and heat-related illnesses (HRIs). While several studies have investigated engineering and administrative control methods to prevent certain heat stress risk factors, a comprehensive understanding of all existing risk factors and their corresponding control strategies is still lacking. It is crucial to identify gaps in current control strategies and develop a safety management framework for effective heat stress control by implementing existing measures. In addition, the effectiveness of the most common control strategies must be rigorously evaluated to ensure their efficacy and to guide future research aimed at enhancing these strategies or developing more effective ones. This study employed a mixed literature review methodology to address this knowledge gap. A structured literature review investigated and synthesized heat stress risk factors and control methods to find the gaps in control options to address underestimated risk factors. Furthermore, a comprehensive systematic literature review, including trend analysis, scientometric analysis, and meta-analysis, determined research foci and evaluated the effectiveness of the heat stress control methods. The scientometric analysis identified 11 clusters, encompassing key research themes such as environmental risk factors (e.g., high-temperature environments, climate change), administrative controls (e.g., work–rest schedules, climate change risk assessment), and personal interventions (e.g., cooling vests and sleep-related strategies). These findings highlight that the most commonly studied control methods are cooling vests, work–rest schedules, and cooling interventions. According to these results and the availability of quantitative results, the meta-analysis evaluated nine datasets of reductions in core body temperature by using types of cooling vests and anti-heat-stress uniforms and established the significant effectiveness of this control strategy in mitigating heat stress with a medium effect size. Moreover, five potential research studies have been identified to address gaps in control strategies for certain underestimated risk factors, including leveraging sensor technologies, conducting control training, dynamic work–rest schedules, using cutting-edge PPE, and governmental initiatives. Insights gained from this study enhance decision making for resource allocation, selection of control options, and intervention prioritization within a heat-stress-control framework based on the safety management system. The findings also highlight the effectiveness of cooling vests and areas that need to be developed, and evaluate potential heat-stress-control methods in construction.

Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NOx emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.

Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped

This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg schedule patterns for the complex configuration of the Pegasus, the requirement for various gaits, and the design of reward functions for MA-RL agents. Agents were trained using two variations of policy networks based on the framework, and real-world tests show promising results with easy policy transfer from simulation to the actual hardware. The proposed framework models acquired higher rewards and converged faster in training than other variants. Various experiments on the robot deployed framework showed fast response (0.8 s) under disturbance and low linear, angular velocity, and heading error, which was 2.5 cm/s, 0.06 rad/s, and 4°, respectively. Overall, the study demonstrates the feasibility of the proposed MA-RL control framework.

Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC

ABSTRACTThe organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.

Real-time economic following velocity and gear planning for commercial vehicles based on the bi-level optimization

ABSTRACT Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings.

Stochastic Plantwide Optimizing Control for an Acrylic Acid Plant

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations.

Theory and experiment for autonomous quadrotor flight via bounded robust finite-time homogeneous sliding mode control: A roadmap to search and rescue

This paper introduces a new saturated robust control technique for quadrotor aircraft modeled by second-order Ordinary Differential Equations (ODEs), considering disturbances in the control channel (matched disturbances). The control design employs a Sliding Mode Control (SMC) approach, featuring in: (i) a novel saturated homogeneous sliding manifold, (ii) a novel tracking controller, namely, Bounded Robust Finite-Time Homogeneous Sliding Mode Control (BRFTHSMC). An Improved Fixed-time Convergent Extended State Observer (IFCESO) is incorporated into the control scheme to handle disturbances. Together the BRFTHSMC and the IFCESO establish a reliable Active Disturbance Rejection Control (ADRC) framework. The latter ensures finite-time convergence of the errors quantities to the origin along with effective disturbance rejection. Rigorous stability analysis is conducted based on Lyapunov theory. Beside control design, another distinguishing theoretical outcome of this paper in the form of Corollary is the extension of the present results to integrator-type systems (higher-order systems). The study is substantiated through MATLAB®/Simulink simulations and Robot Operating System (ROS)/Gazebo implementation, validating the theoretical foundation. Extensive experimental tests on real hardware, including attitude and Cartesian trajectory tracking under various disturbances, further affirm the theoretical findings. The synthesized control system surpasses alternative methods in terms of control signal’s boundedness, finite-time tracking stability, transient response performance, and steady-state precision. Notably, the control input circumvents singularity challenges observed in conventional SMC approaches. In addition to trajectory tracking experiments, the controller’s effectiveness is demonstrated in real-world search and rescue scenarios. Therefore, a Deep Neural Network (DNN) algorithm, based on a MS COCO-pretrained Single Shot Detector (SSD-Mobilenet-v2), is employed for a person detection mission in an unknown search area.

Robust Speed and Spacing Control Framework for Autonomous Vehicles via µ-synthesis with Descriptor Form Representation

Robust Speed and Spacing Control Framework for Autonomous Vehicles via µ-synthesis with Descriptor Form Representation

Shared Control Design of a Walking-Assistant Robot

Walking-assistant robots serve as a vital aid for individuals with mobility challenges, enhancing their independence and mobility. Shared control systems combine user input and robotic intelligence to ensure safe, adaptable, and intuitive navigation. This paper presents a shared control framework for a walking-assistant robot, focusing on user intention prediction and real-time obstacle avoidance. A Python-based simulation demonstrates the system's performance in a scaled-up environment with larger obstacles and a broader robot trajectory.

Multi-plateau water adsorption of pyrene-based covalent organic frameworks for potential humidity control

Indoor air humidity significantly influences air quality, human health, and work performance. Covalent organic frameworks (COFs) have been proven effective in humidity regulation; however, challenges remain in broadening their range of humidity control. This study designs and synthesizes a series of pyrene-based crystalline COFs as humidity control materials, investigating the moisture adsorption–desorption characteristics under structural modulation. Among these, PY-3,3′-BPY-COF exhibits unique multi-step adsorption isotherms, achieving the highest water uptake capacity (1 g/g) and efficient water uptake-release cycles. Theoretical and experimental analyses reveal a linear relationship between the maximum water uptake rate and the specific surface area, while the adsorption inflection points are influenced by the water binding energy and pore size distribution. Importantly, the introduction of strong binding sites promotes multi-step adsorption phenomena, significantly enhancing the humidity control capabilities of COFs across a wide range of humidity levels. This research introduces an innovative approach for broad-spectrum indoor humidity control and reveals the fundamental relationship between the structure characteristics of COFs and their capabilities in water adsorption and release.

Improved direct control of single stage photovoltaic powred system

This paper introduces a novel direct control quasi-Z-source inverter (qZSI) topology as a viable alternative to conventional two-stage converters in photovoltaic (PV) systems. The proposed control strategy effectively merges duty cycle and modulation index within a space vector pulse width modulation (SVPWM) framework for both DC and AC control. To assess the system’s performance under diverse weather conditions, the INC and P&O maximum power point tracking maximum power point tracking (MPPT) algorithms are employed. Rigorous simulations conducted using MATLAB/Simulink demonstrate the proposed method’s ability to achieve multi-objective optimization of PV systems, enhancing overall system efficiency and reliability.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

An Orbital-attitude Coupled Control Framework for a Full-scale Flexible Electric Solar Wind Sail Spacecraft in Orbital Transformation Missions

In this work, a novel orbital-attitude coupled control framework is developed for the electric solar wind sail (E-sail) spacecraft, aiming to handle the orbital transformation and attitude maneuver simultaneously. In the framework, the desired attitude parameters and the slow-varying voltage component are provided by the orbital control strategy, while the current attitude parameters and the fast-varying voltage component are manipulated by the attitude control strategy to approach their desired values. The orbital-attitude coupled characteristics of the E-sail spacecraft, including the flexibility-induced coupling effect, are fully described by the referenced nodal coordinate formulation. Considering the input saturation conditions, the governing equation for the orbital control strategy is then derived, in which the in-plane and out-of-plane displacement and velocity errors are prescribed as the state variables to be eliminated. An integral sliding mode control (ISMC) scheme is proposed to improve the robustness against the unmeasurable disturbance term. A model predictive control (MPC) scheme is introduced to enhance the convergence efficiency, where a quadratic optimization is performed to plan the desired attitude parameters and voltage components within the prediction horizon. To evaluate the control performance in the orbital transformation and attitude maneuver missions on the displaced non-Keplerian orbit, a series of scenarios with complex initial conditions are simulated under different control schemes, including the ISMC-MPC compound scheme. The results show that the control strategy designed under the rigid-body assumptions may not be feasible for the flexible E-sail spacecraft, while the investigated control strategy realizes the accurate and efficient convergence of the orbital and attitude variables on both the rigid and flexible E-sail spacecraft with the tether deformation stabilized.

India’s Consensus Statement Tobacco Control Priorities: Review of Current Strategies and the Way Forward

IntroductionTobacco consumption remains a significant global public health challenge, causing over 8 million preventable deaths annually, with India bearing a substantial burden as second largest producer and consumer of tobacco products. Despite some advanced tobacco control policies, there are certain gaps, which requires effective implementation. To address these challenges, a national consultation was organized, bringing together experts to identify priorities in tobacco control in India and formulate an action plan. Design/MethodsA national consultation on ‘tobacco control priorities in India- challenges and way forward’ was organised where a total of 40 experts from the government, developmental organizations, and academia participated to formulate Short, Intermediate and Long-term priorities for tobacco control in India. Preceding the consultation, an extensive literature review and policy analysis were conducted to establish initial priorities. Subsequently, a consensus statement was developed to assist policymakers in identifying tobacco control priorities in India with agoal to achieve SDG 3a, aiming to strengthen the implementation of the WHO FCTC. ResultsThe proposed short-term priorities focused on promoting anti-tobacco curricula in health educational institutions, expanding cessation services, creating a national task force for Tobacco Control, training, monitoring and evaluation, and strengthening Tobacco Control Act. The intermediate priorities focused on advancing tobacco taxation, creating Tobacco-Free environments, adopting and implementing FCTC Article 5.3, building the capacity of National Tobacco Testing Laboratories and implementating Tobacco Vendor Licensing. The development of a comprehensive, multi-sectoral strategy to address the policy gaps and maintain resources to support the sustainability of tobacco control efforts to reach the goal of tobacco-free India were long-term priorities. ConclusionsNational policies and legislation play a crucial role in strengthening the comprehensive tobacco control framework. The collaborative formulation of short, intermediate, and long-term priorities for tobacco control in India is essential for their phased implementation toward achieving a Tobacco-Free Generation, emphasizing a holistic approach for addressing challenges and leading the way ahead.