Robust Control Research Articles

A study on clinico-demographic and microbiological profile of surgical site infection (SSI) in a tertiary care hospital, Kolkata: An alarming trend

The Centres for Disease Control and Prevention (CDC) estimates surgical site infections SSI is major contributor of healthcare associated infections (HAI). Multidrug resistant (MDR) Gram-negative bacilli are emerging pathogens. This study aimed to determine the magnitude of SSI and identify predominant pathogens with their antimicrobial susceptibility patterns. It is a hospital-based descriptive cross-sectional study including 2070 suspected SSI specimens from 25809 surgeries between 1July 2021 to 30 June 2023. Organisms’ identification and AST was done by both conventional and automated methods. Data was collected and analysed on MS-Excel sheet with various charts and tables. In our study SSI rate was 6.3%, much higher than previous study (2.83%) from this institution. SSI rate was highest in plastic surgery (8.2%). Major pathogens of SSI were Gram-negative bacilli e.g., (26.34%), (25.59%) and (74.69%) was predominant among Gram-positive cocci. Gram-negative bacilli including enterobacterales and non-fermenter and showed resistance to major classes of broad- spectrum antibiotics. Methicillin resistance (MRSA) was 43.9%, which indicates need to improve infection control practices. Our study showed significant higher proportion of SSI as compared to previous studies from the same institute with alarming number of isolated MDR Gram-negative bacilli. So, this study focusses the need of robust infection control practices and strict implementation of antimicrobial stewardship to overcome challenges of antimicrobial resistance.

Coordinated management of oxygen excess ratio and cathode pressure for PEMFC based on synthesis variable-gain robust predictive control

In this research endeavor, a novel synthesis variable-gain robust model predictive control (SVGRPC) method using the min-max technique with parameter dependent Lyapunov functions is introduced to achieve the desired trajectory tracking of oxygen excess ratio (OER) and cathode pressure in the polymer electrolyte membrane fuel cells (PEMFCs) system. Initially, a simplified control-oriented model of the air supply system is developed and then transformed into a polytopic form to accommodate the inherent uncertainties in the PEMFC. Subsequently, the polytopic model is integrated with the reference trajectory to construct an augmented state-space model for deriving an error state representation. In the framework of robust model predictive control (RPC) utilizing a parameter-dependent Lyapunov function, a series of variable feedback gains are employed to mitigate conservatism. Additionally, the online solution process imposes a significant computational burden, presenting challenges for the real-time implementation of RPC-based control strategy. Therefore, the offline computing is introduced to significantly reduce the computational burden, resulting in the development of the SVGRPC strategy proposed in this paper. Subsequently, the SVGRPC strategy computes explicit linear state-feedback control laws by solving linear matrix inequalities (LMIs) using an offline control algorithm. The effectiveness of the proposed controller is then validated through assessments conducted under three distinct operating conditions of the PEMFC system. The outcomes of this study affirm that the proposed controller improves the requirements of control performance when compared to the online RPC with a quadratic Lyapunov function while significantly alleviating the computational workload.

An Improved Voltage Control Method for EAST Fast Control Power Supply

The Experimental Advanced Superconducting Tokamak (EAST) fast control power supply (FCPS) is an important device for controlling the vertical displacement of plasma during the nuclear fusion power generation process, adopting a multiple H-bridge invertor branch parallel operation structure to output total current. At the beginning of each shot of plasma discharge, FCPS works in open-loop voltage control mode (VCM) or closed-loop current control mode (CCM) determined by the plasma control system to output current for exciting the load coil, to achieve plasma vertical displacement control. VCM has the characteristics of fast dynamic response speed but poor consistency of branch current and insufficient branch current control accuracy and stability because of open-loop control. CCM has the characteristics of high branch current control accuracy but poor dynamic response and robustness because of control delay and control parameters determined based on engineering experience. To achieve fast and robust control, an improved voltage control method (IVCM) is proposed by combining the advantages of VCM and CCM. In the beginning of establishing the output current, FCPS operates in VCM, and rapid establishment of the output current is ensured. After the output current rapidly increases to the critical value, closed-loop current control is added to VCM to ensure the accuracy of output current control. In closed-loop current control, linear super-twisting sliding mode control is designed to achieve fast and robust control, ensuring good consistency and fast dynamic response performance of each branch current. Simulations and experiments verify that the designed IVCM has better compatibility characteristics in output current stability, control accuracy, and consistency of each branch current compared to VCM.

Robust optimal tracking control of multiple autonomous underwater vehicles subject to uncertain disturbances

AbstractThis paper considers the problem of robust optimal tracking control of multiple autonomous underwater Vehicles (AUVs) subject to uncertain external disturbances. First, the Takagi‐Sugeno (T‐S) fuzzy based technique is utilized to convert the high‐order nonlinear multi‐AUV system into a series of linearized subsystems. Second, a novel fully distributed sliding mode control (FDSMC) strategy is proposed to attenuate the disturbances. Meanwhile, the leader‐following consensus and the nearly optimization of the energy‐cost function for the multi‐AUV system can be achieved simultaneously through the designed optimal nominal control protocol. Moreover, the proposed control strategy has more mild constraints on the communication topologies. Finally, the effectiveness of the proposed FDSMC strategy is verified by numerical simulation studies.

Phase I quality control framework for monitoring organ-at-risk dose

Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling’s T2 chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40’s plan and updating the DVH and control chart, P40 was IC, but P14* (P14’s re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

Adaptive backstepping sliding mode subject-cooperative control for a pediatric lower-limb exoskeleton robot

The passive-assist rehabilitation mode with a fixed desired trajectory neglects the subject’s active involvement and degrades the therapeutic performance in case of partial muscle strength. Therefore, this study proposes a novel subject-cooperative control based on a variable admittance control scheme and a robust trajectory control scheme for a pediatric lower-limb exoskeleton robot. Initially, the system description and dynamic modeling are briefly explained. Thereafter, a neural-fuzzy–based variable admittance control ( nf VAC) is designed to incorporate a realistic subject-exoskeleton interaction and consider the subject’s active participation. Finally, a robust adaptive backstepping sliding mode control with rapid reaching law is used to handle parametric uncertainties and external disturbances. A stepwise selection of Lyapunov functions is utilized to address the stability of the trajectory control. The effectiveness of the proposed adaptive backstepping sliding mode–neural-fuzzy variable admittance control (ABSM- nf VAC) scheme is compared with two contrast control schemes, namely, adaptive backstepping-fixed admittance control (AB-FAC) and adaptive terminal sliding mode-fuzzy variable admittance control (ATSM- f VAC) for the active-assist mode with the effect of sudden reflex. Based on the numerical results, the suggested cooperative controller has demonstrated favorable tracking performance, compliant interaction, and safety aspects during gait training.

Robust Fixed-Time Fault-Tolerant Control for USV with Prescribed Tracking Performance

Robust Fixed-Time Fault-Tolerant Control for USV with Prescribed Tracking Performance

Enhancing disturbance rejection in offshore drilling platforms: a dynamic positioning control scheme using equivalent-input-disturbance method with separated observers

This paper presents a dynamic positioning control scheme for offshore drilling platforms using the equivalent-input-disturbance (EID) method with separated observers. The scheme incorporates observer-based disturbance compensation, wave-filtering state estimation, input transformation, and state feedback control to achieve robust control performance. The main contributions include the design of an EID scheme with separated observers, enabling enhanced disturbance-rejection performance, incorporating a notch filter in the EID estimator to reduce energy loss, and giving an observer tuning scheme to achieve a balanced performance between disturbance rejection and wave filtering. Simulation results demonstrate the effectiveness of the proposed scheme. This work provides valuable insights into designing stable and efficient dynamic positioning systems for offshore drilling platforms.

Investigations on application of curve fitting in power system inertia estimation

ABSTRACT As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation.

Late Holocene tropical cyclones linked to climatic and solar variability

Response of tropical cyclone (TC) frequency to future climate change has important implications for society but remains poorly understood due to a lack of long-term reliable observational records. Here, we use high-resolution organic geochemical proxies (OGPs) with robust chronological control from two coastal lakes, >150 km apart, to reconstruct past TC activities in the northeastern Gulf of Mexico (GOM). Results show multi-decadal-to-centennial-scale fluctuations in TC frequency over the last 5500 years. We found that TC frequency either exceeded or was comparable to modern observations during a prolonged interval (∼1500-720 cal yr BP) that encompasses the Medieval Warm Period. We also found a nearly 90% reduction in TC activity, relative to modern, during an exceptionally quiescent period (650-280 cal yr BP) that overlaps the Little Ice Age. Warmer temperatures in the North Atlantic did not always support increased TC frequency in this region. We show, for the first time, that increased TC activities in the northeastern GOM track increased solar irradiance. High TC frequency is also coupled to periods of increased sea surface temperatures in the northern GOM, enhanced El Niño-Southern Oscillation, and positive phases of the Atlantic Multi-Decadal Oscillation. Our results highlight the importance of natural variability in climate and solar activity in modulating TC frequency.

Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality.

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Real-time and hardware in the loop validation of electric vehicle performance: Robust nonlinear predictive speed and currents control based on space vector modulation for PMSM

The transport industry's environmental impact has prompted various stakeholders to focus more on electric vehicles as a potential solution. Extensive research on electric vehicle motor control reveals their complexity, highlighting the crucial role of the traction motor control mechanism being one of their essential subsystems. This study presents a novel approach to control the drive system of an electric vehicle's permanent magnet synchronous motor, using a robust non-linear model predictive control in a cascaded structure combined with a space vector modulation technique. The formulation of the robust non-linear model predictive control law involves optimizing a new cost function with the inclusion of an integral action in the controller to ensure zero steady-state error. An important aspect of this control approach is its ability to enhance robustness without needing information about external perturbations and parameter uncertainties. Moreover, the space vector modulation technique is designed to address the issue of current harmonic distortion by carefully selecting appropriate switching vectors. The appropriate selection for space voltage vectors for every sampling period maintains a consistent switching frequency. As a result, the SVM can enhance torque-rippled regulation and accomplish effective flux and torque control. The performance of the proposed control system is evaluated through a series of numerical simulations using MATLAB/Simulink. The simulation results are analyzed and compared to assess the effectiveness of the proposed control system in terms of tracking accuracy, disturbance rejection, robustness against parameter uncertainties, reduction of torque, and current ripples. To further validate the numerical simulation results, a hardware-in-the-loop (HIL) setup is established using the OPAL-RT platform. Furthermore, experimental validation on a PMSM utilizing the dSPACE DS1202 board is carried out, demonstrating the efficacy of the proposed control system. These findings validate the suggested control technique's robustness and efficiency.

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

Robust Control Based on Adaptative Fuzzy Control of Double-Star Permanent Synchronous Motor Supplied by PWM Inverters for Electric Propulsion of Ships

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510.

Exponential-form predefined-time convergent controller and its applications to Van-der-Pol system and 2-DOF helicopter

This paper proposes a predefined-time convergent stabilization controller for a class of nonlinear systems. First, a controller is introduced for linear systems where the convergent time is selected in advance, independent of initial conditions, and can be assigned as a parameter of a control input. The corresponding result is then obtained for a multidimensional system consisting of [Formula: see text] nonlinear differential equations. The block control technique is employed to consequently stabilize each state component by a virtual control until the real control appears. Finally, the presented result is applied to a multivariable nonlinear system to design a predefined-time convergent robust controller. To show effectiveness of the proposed convergent controller, numerical simulations have been carried out for a Van-der-Pol system and a 2-degree-of-freedom (2-DOF) helicopter model.

Electric Vehicle Steering System Fuzzy Modeling and Controller Design

Electric vehicle (EV) steering control becomes much more important in many senses such as driving comfort and safety. Unlike the traditional internal combustion engine powered vehicles that possess only one control input related to the steering wheel, the so‐called direct yaw moment control is yet another control input in EVs thanks to the dispersedly located in‐wheel motors. In view of controller design crucially depending on a proper mathematical model, this paper first focuses on the modeling of EV steering system with uncertainty in the system considered for the latter robust controller design. Then a Takagi‐Sugeno (T‐S) fuzzy model is obtained based on the EV's model, which is capable of varying vehicle velocity in a range. Finally, a robust controller with inputs constraint is designed based on the model, and the effectiveness is verified by computer simulations. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A robust fault-tolerant control algorithm based on a novel modeling method for quadrotor application with accurate fault estimation

A robust fault-tolerant control algorithm based on a novel modeling method for quadrotor application with accurate fault estimation

Assessment of Food Processing Technologies on Allergen Reduction in United States

Purpose: The aim of the study was to analyze the assessment of food processing technologies on allergen reduction in United States. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Recent advancements in food processing technologies in the U.S. are focusing on reducing allergenicity in food products. Thermal methods are traditionally used, but novel nonthermal techniques, such as high-pressure processing and cold atmospheric plasma, are now being explored for their potential to create hypoallergenic products. These methods aim to modify allergenic proteins without using heat, which can preserve food quality while reducing allergen reactivity. Despite these advancements, ensuring the complete removal of allergens remains challenging, highlighting the ongoing need for robust detection and control strategies​. Unique Contribution to Theory, Practice and Policy: Risk assessment theory, health belief model (HBM) & diffusion of innovations theory may be used to anchor future studies on analyze the assessment of food processing technologies on allergen reduction in United States. Implementing advanced food processing technologies focused on allergen reduction can revolutionize industry practices. Regulatory bodies could use empirical data from these assessments to set more precise guidelines and safety standards for allergen levels in food products.

Adaptive dynamic programming and distributionally robust optimal control of linear stochastic system using the Wasserstein metric

SummaryIn this paper, we consider the optimal control of unknown stochastic dynamical system for both the finite‐horizon and infinite‐horizon cases. The objective of this paper is to find an optimal controller to minimize the expected value of a function which depends on the random disturbance. Throughout this paper, it is assumed that the mean vector and covariance matrix of the disturbance distribution is unknown. An uncertainty set in the space of mean vector and the covariance matrix is introduced. For the finite‐horizon case, we derive a closed‐form expression of the unique optimal policy and the opponents policy that generates the worst‐case distribution. For the infinite‐horizon case, we simplify the Riccati equation obtained in the finite‐hozion setting to an algebraic Riccati equation, which can guarantee the existence of the solution of the Riccati equation. It is shown that the resulting optimal policies obtained in these two cases can stabilize the expected value of the system state under the worst‐case distribution. Furthermore, the unknown system matrices can also be explicitly computed using the adaptive dynamic programming technique, which can help compute the optimal control policy by solving the algebraic Riccati equation. Finally, a simulation example is presented to demonstrate the effectiveness of our theoretical results.

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/$$\mathcal H_\infty $$ approach

AbstractIn this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/$$\mathcal H_\infty $$ H ∞ control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/$$\mathcal H_\infty $$ H ∞ control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal $$\mathcal H_\infty $$ H ∞ performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.