Robust Control System Research Articles

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

IntelliDoor: Smart Access Control System with Automated Door Operation

Abstract: The Intelligent Access Control System with Automatic Door Operation is a project aimed at providing secure and efficient access control using artificial intelligence (AI) and automated door mechanisms. This system utilizes facial recognition technology for user identification, Firebase for data storage and management, and Arduino for controlling the automatic door operation. The objective is to develop a robust and intelligent access control system that automatically identifies authorized individuals through facial recognition and opens the door for them. The system also maintains a record of entry for each individual for attendance tracking or security purposes. The project features include facial recognition, automatic door operation, Firebase integration, an alert system, and real-time monitoring. The project workflow involves initialization, facial recognition, recognition results, database updates, and automatic door operation. The project report covers the introduction, literature review, methodology, implementation, results, and conclusion. The Intelligent Access Control System with Automatic Door Operation provides an efficient and secure solution for access control, enhancing security while offering convenience. With further refinement and integration, it has the potential to become a standard solution for access control in modern buildings and facilities.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

A Bayesian-Based Credible and Reliable Control Method for Active Vibration Suppression of Structures with Uncertain Parameters

As a classical design method for addressing uncertainty in control systems, robust control is designed with the goal of complete reliability of the control system. Nevertheless, the pursuit of robustness can inadvertently give rise to considerable performance degradation, rendering the resultant closed-loop control system excessively conservative in its design. Reliability-based design methods serve as an effective approach for managing uncertainties, which will achieve a balance between the robustness and performance of the active control system by taking advantage of the characteristic of reliability that allows for a certain level of permissible failure probability. The accurate quantification of uncertainty is the key to achieving reliable control, but common uncertainty quantification methods cannot achieve credible quantification of uncertainty in small samples and update the distribution of new samples. In this paper, a nonprobabilistic Bayesian inference based interval uncertainty quantification method is proposed. This method updates the distribution of interval boundaries and radii by introducing a small number of samples, and obtains interval parameters under a given confidence level. On the basis of uncertainty quantification, the dynamic reliability of the vibration system is derived, and nonprobabilistic reliability is introduced into the design of active controllers through optimization strategies. The reliable control strategy aims to select the optimal controller parameters on a reliable path with a given credibility, in order to solve the problem of overly conservative robust control theory. Two numerical examples were used to verify the feasibility and applicability of this method.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Enhancing financial reporting quality in village-owned enterprises: The role of organizational competencies

The quality of financial reporting in village-owned enterprises (VOEs) is critical for ensuring transparency and accountability in local governance. This study explores the impact of education level, training programs, work experience, internal control systems, and information technology on the quality of financial reports in VOEs. The objective is to determine how these factors influence financial reporting quality to support effective local governance. The paper involves a sample of 120 VOE managers and employs quantitative analysis to examine the relationships between these factors and the quality of financial reporting in Indonesia. The results indicate that higher education levels, comprehensive training programs, and extensive work experience positively affect the quality of financial reporting. Specifically, the study finds that educated personnel and well-structured training programs enhance the accuracy and reliability of financial reporting. In contrast, the anticipated positive impact of IT utilization on financial reporting quality was not observed, suggesting that issues related to IT infrastructure and integration may limit its effectiveness. Additionally, robust internal control systems significantly improve the quality of financial reports. Overall, the analysis emphasizes the importance of investing in education, training, and internal controls to enhance financial reporting quality in VOEs. The findings highlight the need for further research into the challenges of IT integration to fully harness its benefits for financial transparency.

Robust Shared Control With Stable Contact Servoing for Enhanced Object Transportation by Telerobotic Bimanual Mobile Manipulators

ABSTRACTBimanual mobile manipulation significantly enhances the capabilities of field robots that interact with diverse dynamic environments and expands the range of possible manipulation actions. This paper introduces a robust shared control architecture for remote manipulation of the Collaborative Dual‐arm Robot Manipulator (CURI), aimed at stable object transportation. Given that stable contact is pivotal for successful object transportation, our focus is on stabilizing the contact between the robot end‐effectors and the object using a contact servoing strategy. To maintain stable contact, it is essential that the contact wrenches consistently stay within the predefined friction cones throughout the task. We propose a novel shared control strategy to stabilize the contact using the contact parameterization model. This model formulates the contact stability manifold using a set of unconstrained reparameterized variables through a bijective mapping function, ensuring that the controller derived from these unconstrained reparameterized variables consistently maintains contact stability. The efficacy of the proposed approach is convincingly demonstrated through a series of experiments involving representative bimanual transportation tasks within a logistics context. Both theoretical analysis and experimental validations confirm that our proposed control strategy not only ensures the stable transportation of objects by CURI but also substantially enhances the overall robustness of the shared control system.

Coupling of Advanced Guidance and Robust Control for the Descent and Precise Landing of Reusable Launchers

This paper investigates the coupling of successive convex optimization guidance with robust structured H∞ control for the descent and precise landing of Reusable Launch Vehicles (RLVs). More particularly, this Guidance and Control (G&C) system is foreseen to be integrated into a nonlinear six-degree-of-freedom RLV controlled dynamics simulator which covers the aerodynamic and powered descent phase until vertical landing of a first-stage rocket equipped with a thrust vector control system and steerable planar fins. A cost function strategy analysis is performed to find out the most efficient one to be implemented in closed-loop with the robust control system and the vehicle flight mechanics involved. In addition, the controller synthesis via structured H∞ is thoroughly described. The latter are built at different points of the descent trajectory using Proportional-Integral-Derivative (PID)-like structures with feedback on the attitude angles, rates, and lateral body velocities. The architecture is verified through linear analyses as well as nonlinear cases with the aforementioned simulator, and the G&C approach is validated by comparing the performance and robustness with a baseline system in nominal conditions as well as in the presence of perturbations. The overall results show that the proposed G&C system represents a relevant candidate for realistic descent flight and precise landing phase for reusable launchers.

Prescribed-Time Dynamic Positioning Control for USV with Lumped Disturbances, Thruster Saturation and Prescribed Performance Constraints

This work studies the dynamic positioning (DP) control issue of unmanned surface vessels subjected to thruster saturation, error constraints, and lumped disturbances composed of time-varying marine environmental disturbances and model parameter uncertainties. Combining the disturbance-accurate estimation technique and the prescribed performance control strategy, a novel prescribed-time DP control scheme is established to address this challenging problem. In particular, the prescribed-time lumped disturbance observer is designed to accurately estimate external marine disturbances, which guarantees that the estimation error converges to zero within a prescribed time. Subsequently, a prescribed performance control strategy is proposed to guarantee that the positioning errors of DP surface vessels with thruster saturation constraints meet the error constraints requirements within a prescribed time. Furthermore, an anti-windup compensator is presented to mitigate the thruster saturation and improve the robustness of the DP control system. The stability analysis demonstrates that all positioning errors of the closed-loop system can converge to predefined performance constraints within a prescribed time. Finally, the numerical simulation confirms the efficacy and superiority of the proposed PTDP scheme.

Model-based assessment of a feedforward-feedback control strategy for ORC-based unit in waste heat recovery application

Research in the automotive sector is driven by the need to reduce the greenhouse gases emissions, while maintaining the expected vehicle performances. The electrification and hybridization ensure to achieve this goal, anyway, some issues still limit their full development in the international panorama. For this reason, the technological improvement of Internal Combustion Engines (ICEs) plays a crucial role in this transition period, also considering the opportunities related to sustainable fuels. Among the technological solutions allowing to improve ICEs performances, the energy recovery from the exhaust gases through Organic Rankine Cycle (ORC)-based power units are one of the most attractive alternatives, due to the high enthalpic content of the hot source. Despite these benefits, the ICE exhaust gases usually have considerable fluctuation of thermodynamic conditions. For this reason, it is necessary the adoption of a reliable and robust control system to keep the main operating ORC quantities (superheating degree, expander intake pressure and temperature) within a suitable and safe range. ORC control strategies for transportation applications are often based on detailed models that predict the unit behaviour, making use of Proportional-Integrative-Derivative (PID) regulators, whose coefficients are generally tuned through theoretical approaches and dedicated software. In the present work, an innovative control system has been developed, based on the integration of a feedforward (FF) and proportional feedback (FDB) regulating strategies. Despite the simplicity of the proposed approach, it ensures the proper plant operation even under severe fluctuations of the hot source. Particularly, the gain of FDB is based on a constitutive relationship between the expander intake pressure and working fluid mass flow rate. Such gain, indeed, is universally valid, not requiring to be tuned as generally done. The benefits of the proposed strategy are assessed thanks to a comprehensive model of the whole ORC unit, validated through experimental data carried out on a fully instrumented test bench in dynamic working conditions. Results demonstrate the robustness of the feedforward-proportional regulating approach: a superheating degree of 15–20 °C is ensured, keeping the plant power and efficiency close to the design value (1 to2 kW and 4 to6% respectively) even in off-design conditions. Moreover, the safe operating of the expander is guaranteed limiting the maximum temperature excursion under the safety limit of 160 °C.

Strengthening medical facility responses to respiratory infectious diseases: Global trends, challenges, and innovations post-COVID-19.

Respiratory infectious diseases have long been a serious public health problem. This study explores the significance of respiratory infectious disease prevention and control systems worldwide, particularly during and after the COVID-19 pandemic. The epidemiology of many respiratory diseases such as influenza changed over the past two years, and the incidence of pathogens such as Mycoplasma pneumoniae and Bordetella pertussis has also increased. Based on influenza surveillance data in China, the influenza season in 2023 was notably delayed and extended, with A(H1N1) pdm09 being the predominant strain. This editorial also reviewed the gradual establishment of China's infectious disease prevention and control system following the 2003 SARS outbreak, highlighting the role of medical facilities in managing public health emergencies, conducting infectious disease pre-screening, and reporting cases to networks in real time. In the future, China will further develop an intelligent, multi-trigger infectious disease surveillance and early warning system to increase the early detection of unknown infectious diseases and optimize the allocation of medical resources. A robust infectious disease control system is crucial to addressing future pandemic threats.

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.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Gas Leakage Risk Management Of Biogas Powerplant Using Aloha Gas Dispersion Modelling And Bowtie Analysis

Biogas, a fuel derived from organic waste, has gained popularity as a sustainable energy source. The adaptability of biogas allows its utilization for electricity and heating. This study aims to evaluate the risk of biogas leakage using ALOHA gas dispersion modeling and Bowtie analysis to identify hazard zones and evaluate the effectiveness of preventive and mitigation measures. The research method using Gas dispersion modeling in ALOHA software can simulate the spread of leaked biogas, identify potential hazard zones, and inform emergency response planning. The results show that gas dispersion reaches up to 296 meters from the leak source in a worst-case scenario without ignition, with significant implications for safety. The ignition scenario produces high thermal radiation, further exacerbating the risk. In conclusion, these findings highlight the intolerable risk of biogas leakage, requiring comprehensive risk mitigation measures. This study recommends the implementation of a robust control system, routine maintenance, and emergency response plan to ensure the safety and sustainability of biogas facility operations

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

ABSTRACTThis article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

Crime in the Architecture, Engineering and Construction (AEC) Industry—The Role of Subcontractors

This article examines the challenges posed by criminal subcontractors in the construction industry. It aims to delineate the specific crimes committed by these subcontractors and assess their impact against the broader backdrop of industry-related criminality. Employing a scoping literature review, the study explores the existing research, summarizes key findings, and highlights gaps in the current knowledge. The construction industry’s inherent complexities and reliance on extensive subcontracting create an environment ripe for criminal activities. The research questions addressed are the following: (1) What crimes are carried out by subcontractors? (2) What are the consequences of crimes carried out by subcontractors? This study identifies several major concerns: (1) adverse impacts on project management in terms of timelines, quality, and budgetary control; (2) widespread exploitation within supply chains, ranging from wage disparities to modern slavery; (3) prevalent fraudulent practices, such as bribery, collusion, and embezzlement; and (4) the detrimental effect on Health, Environment, and Safety (HES) standards. The article underscores the diversity of legal frameworks across jurisdictions and signals the need for concerted efforts to enhance crime prevention measures, foster industry-wide collaboration, and establish robust control systems. There is an urgent need for a profound understanding of the contractor–subcontractor dynamic and procurement of substandard materials. The findings suggest that the construction industry faces formidable challenges due to criminal elements, profoundly affecting project efficiency, legal compliance, and worker welfare.

Real-sea validation of a model predictive controller's inherent robustness for medium-scale unmanned trimaran heading

The development of medium-to large-scale and high-performance unmanned surface vehicles (USVs) is a burgeoning trend in intelligent marine systems. The heading controller is crucial for USVs to execute diverse missions, especially given their inherent underactuation characteristics and constraints. Recent efforts have focused on enhancing the robustness of controllers against external disturbances and employed two main strategies: one converges the control error to a bounded residual set through robust modifications, while the other eliminates the error by modelling disturbances. Notably, these enhancements have primarily catered to small USVs, where disturbances significantly impact their manoeuvrability, necessitating such robust control strategies. This focus has somewhat overshadowed the inherent robustness of closed-loop control systems. Compared with small USVs, medium-to large-scale USVs are less affected by external disturbances, despite undertaking more complex missions. Leveraging the intrinsic robustness of controllers presents an opportunity to simplify controller design, thereby reallocating computational resources towards enhancing mission capabilities. Model Predictive Control (MPC) has attracted significant attention recently, and its receding horizon and optimality theoretically provides a new level of inherent robustness, which remains under-explored in real sea. This paper focuses on the inherent robustness of MPC in managing the heading of a medium-scale unmanned trimaran subjected to the thrust angle and angular velocity constraints. A model predictive controller considering the constraints is designed based on the identified Nomoto model and the asymptotic stability is ensured with a terminal cost. Conducted real-sea experiments and comparative analyses with a Proportional-Integral-Derivative (PID) controller, the most widespread and dominant control algorithm in practical USV engineering, underscore the superiority of MPC in maintaining satisfactory closed-loop performance. Furthermore, the MPC controller is also successfully applied to real-sea path-following missions and demonstrated good tracking performance in various sea conditions ranging from level 3 to 5 and wind speeds spanning from level 6 to 8. This validation opens up new avenues for motion control strategies in the evolving landscape of larger-scale USVs.

Comparative Analysis of Two Robust Strategies for an Angular Velocity Control System

In this paper, a novel flow control strategy which is the inlet throttled pump was used to design an angular velocity control system for rotary actuator. Inlet throttled systems have good performance in addition to their high efficiency compared to traditional valve controlled systems. The flow in the proposed system is adjusted by a valve that is positioned at the pump inlet with the purpose of reducing the energy loses across the valve. This regulated flow is used then to control the actuator angular velocity. The system was modeled and the open loop stability and performance were studied. In order to improve the system performance, Robust-Proportional-Integral-Derivative (RPID) and structured singular value (Mu) controllers have been designed. The multiplicative uncertainty was analyzed to assess the robustness of the feedback control system where six parameters were considered uncertain within a range of +10%. The robust stability and performance requirements of the closed-loop angular velocity control system were assessed in the frequency domain. The time response of the system showed that the system is stable with both (RPID) and (Mu) controllers. The Mu controller can handle parametric uncertainty without requiring pure integral term which is a significant advantage over the (RPID) controller. On the other hand, the (RPID) controller could achieve robust performance, making it much suitable for systems that require high levels of performance and robustness. In summary, the the (RPID) and Mu controller is a more comprehensive solution for ensuring the best performance of a system. The results for each (RPID) and Mu-controllers showed no oscillations, zero percent overshoot. Each of the (RPID) and Mu-controllers meets the robustness needs. Index Terms— Pump, valve, inlet throttling valve, angular velocity control, robust control, Mu synthesis, D-K iteration, RPID controller .

Artificial Intelligence (AI) in the Banking Industry: A Review of Service Areas and Customer Service Journeys in Emerging Economies

The banking industry's shift towards digitalization, particularly Web 2.0 banking, signifies a profound change in the bank-client relationship. This transformation integrates digital elements into consumers' lives, digitising everyday activities and highlighting the importance of online social platforms and digital business operations. AI's growing interest in academia and commerce underscores its significant role in customer service. The study examines AI strategies in the banking industry, focusing on service areas and adaptation to the evolving technological landscape, particularly in customer service in developing economies. By adopting an interpretivist philosophy emphasising practicality, a qualitative approach was employed through a systematic literature review of 90 articles using thematic analysis. The research highlights AI's growing use in process automation, customer experience, stock market trend prediction, credit risk management, online banking, marketing management, and auditing. It concludes AI's positive impact on banking operational efficiency and customer service by recommending ethical AI usage, stakeholder collaboration, trust in data handling, and robust internal control systems. Additionally, it suggests exploring the association between organisational culture, leadership, and AI integration in developing economies. The research contributes to the theoretical and empirical frameworks that explain how AI is optimally integrated into banking operations, guiding policymakers and practitioners in leveraging AI to foster financial inclusion and economic growth. Future researchers should include non-profit institutions and non-financial performance metrics to gain insights into AI’s sustainability implications in banks beyond financial metrics.

Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control

With the widespread application of solar-energy-air-source heat pump composite heating, optimizing the regulation of air temperature in heating zones, enhancing system thermal comfort, and reducing energy consumption have become crucial. To ensure residential comfort while minimizing HVAC system energy costs, a control method combining guided policy search (GPS) with model predictive control (MPC) is proposed for composite heating systems. This method replaces state estimation in MPC with policy search and substitutes the offline trajectory optimization used in guided policy search with MPC, thus integrating the strengths of both approaches. This strategy not only improves control precision but also reduces energy consumption and enhances system robustness. The GPS-MPC control algorithm was validated against reinforcement learning and MPC. A simulation model was developed and validated on a real physical platform. Simulations compared the control effects of on-off control and MPC on pump frequency, flow rate, stratified thermal storage tank temperature, component, and system energy consumption. The data results indicate that the GPS-MPC algorithm offers superior predictive accuracy, efficiency, and robustness in composite heating control systems compared to conventional methods. Under the GPS-MPC strategy, indoor temperature fluctuations, pump frequency response speed, and energy consumption were significantly improved, with temperature fluctuation limited to only 0.8 °C, and the system achieving energy savings of over 12 %.