Robust Feedback Research Articles

A robust ordinal regression feedback consensus model with dynamic trust propagation in social network group decision-making

This paper proposes a robust ordinal regression feedback consensus model based on trust relationships for social network group decision-making problems in intuitionistic fuzzy environment. The model consists of two main parts: (a) the construction of the complete trust matrix among decision makers (DMs) and (b) the design of a feedback mechanism formulated on robust ordinal regression. In Part (a), to complete the initial trust matrix between DMs, a dynamic trust propagation mode founded on maximum credibility is proposed to investigate indirect trust relationships between unfamiliar DMs from the perspective of overall optimization. In Part (b), when the group consensus does not reach the consensus threshold, the utility function compatible with the preference information of DMs is constructed through robust ordinal regression, and the most representative utility function is selected by maximizing the difference of the necessary weak preference relationship. In addition, the performance of DMs under four evaluation dimensions, individual consensus degree, individual comprehensive influence, individual opinion retention, and individual proximity centrality, is aggregated into a total utility value. In each iteration, the DM with the lowest total utility value is identified for opinion adjustments. Further, based on trust-similarity analysis, recommendations are designed for opinion adjustments to improve the group consensus. An illustrative example along with the related sensitivity analysis and comparative study is used to examine the effectiveness and advantages of the proposed model.

Trajectory tracking of two-stage anaerobic digestion process: A predictive control with guaranteed performance and saturated input, based on ultra-local model

To address the robust output feedback tracking problem for total outflow rate of two-stage anaerobic digestion (AD) process with input saturation, a novel performance guaranteed ultra-local model (ULM)-based predictive control (PG-ULMPC) is proposed. First, model of the two-stage AD process is considered which presents the characteristics of the production rate of hydrogen and methane. Then, a new control strategy is developed, which relies only on input/output data and does not require any model knowledge of two-stage AD process. Based on ULM, the designed PG-ULMPC strategy consists of time-delay estimation (TDE), prescribed performance function (PPF) as well as predictive control using feedback correction (FC). The ULM is utilized to approximate the complex model of two-stage AD process in a short sliding time window and simplify controller design. The TDE is employed to estimate the lumped disturbance. The PPF guarantees that the tracking error is limited within the performance bounds whose convergence time and precision can be artificially preset. The predictive control using FC stabilizes the closed-loop system and provides saturated control input. To demonstrate the PG-ULMPC performances, numerical simulations of two-stage AD process in presence of unknown parameter disturbances with nonlinear Proportional–Integral (PI) controller, with ULMPC and with PG-ULMPC are realized by using Matlab/Simulink. The obtained results illustrate the better performance of the proposed PG-ULMPC strategy. Moreover, under the measurement noise, by using filtered derivative technique, the proposed method can still achieve satisfactory performance and has little control chattering.

Robust optimal consensus feedback mechanism with private interest in the context of uncertain cost

During the process of obtaining a consensus, the moderator may recommend modifications based on his personal interests to steer decision-makers (DMs) toward modifying their viewpoints so that the consensus evolves in the desired direction. In the process of revising opinions, the moderator must provide sufficient monetary remuneration to those who are willing to modify their opinions. Nevertheless, there is a lot of uncertainty and both internal and external factors affect the adjustment cost of decision-making. This paper proposes two robust optimal consensus feedback mechanisms with private interests in the context of uncertain cost (UCRFM), taking into account the moderator’s private interests and uncertain cost, minimizing the consensus cost under the established consensus threshold, and maximizing the fuzzy consensus measure under the given cost budget. Initially, two types of UCRFMs are presented by employing two uncertainty sets to characterize the uncertain cost, and then two UCRFMs are turned equivalently into a 0-1 programming model. Secondly, based on different uncertainty sets, the corresponding robust counterparts of UCRFMs are constructed to obtain the optimal total consensus cost and the maximum fuzzy consensus. Finally, comprehensive experimental research is conducted to confirm the effectiveness of the proposed approach, and a comparison between the models is given.

Generalized dissipativity dynamic output feedback control for coronary artery system

This work addresses the problem of synchronizing pathological changes in a patient with coronary artery obstruction to the corresponding nominal coronary artery system (CAS) model. The CAS model is characterized by nonlinear terms, so the synchronization problem is transformed into an equivalent time-varying delay T-S fuzzy framework using the sector of nonlinearity approach. New bilinear matrix inequalities (BMIs) conditions for robust stability analysis and dynamic output feedback gain synthesis are derived based on a less conservative condition for stability assessment of T-S fuzzy systems. A simple change of coordinate is used to solve the cross terms of the bilinearities, allowing the problem to be formulated in terms of linear matrix inequalities (LMIs) and solved using standard semi-definite programming. The effectiveness of the controller design approach is demonstrated through extensive simulations under diverse performance criterion that the controller can achieve.

Event-triggered robust distributed output feedback model predictive control for nonlinear MASs against false data injection attacks

In this paper, an event-triggered distributed output feedback model predictive control scheme for the nonlinear disturbed multiagent systems with sensor–controller channel false data injection attacks is proposed. To provide valid system states to the controller in the event of cyber attacks, a robust multivariate observer is designed to realize the estimation and separation of uncompromised system states, false data injection attacks, and measurement disturbances, simultaneously. Based on these reconstructed signals and a newly-designed linear robustness constraint, the distributed predictive controller is established to achieve smooth cooperative stabilization among agents. Meanwhile, an event-triggered mechanism is applied to save computing resources, and it restricts the error of predictive states and estimated states to guarantee the feasibility of the optimization control problem. Theoretical analyses on robustness and security for the nonlinear multiagent systems under event-triggered distributed output feedback model predictive control are presented. Finally, a simulation on two pairs of one-link flexible joint manipulator systems verifies the theoretical results.

LetsTalkShots: personalized vaccine risk communication.

Vaccine hesitancy is a global health threat undermining control of many vaccine-preventable diseases. Patient-level education has largely been ineffective in reducing vaccine concerns and increasing vaccine uptake. We built and evaluated a personalized vaccine risk communication website called LetsTalkShots in English, Spanish and French (Canadian) for vaccines across the lifespan. LetsTalkShots tailors lived experiences, credible sources and informational animations to disseminate the right message from the right messenger to the right person, applying a broad range of behavioral theories. We used mixed-methods research to test our animation and some aspects of credible sources and personal narratives. We conducted 67 discussion groups (n = 325 persons), stratified by race/ethnicity (African American, Hispanic, and White people) and population (e.g., parents, pregnant women, adolescents, younger adults, and older adults). Using a large Ipsos survey among English-speaking respondents (n = 2,272), we tested animations aligned with vaccine concerns and specific to population (e.g., parents of children, parents of adolescents, younger adults, older adults). Discussion groups provided robust feedback specific to each animation as well as areas for improvements across animations. Most respondents indicated that the information presented was interesting (85.5%), clear (96.0%), helpful (87.0%), and trustworthy (82.2%). Tailored vaccine risk communication can assist decision makers as they consider vaccination for themselves, their families, and their communities. LetsTalkShots presents a model for personalized communication in other areas of medicine and public health.

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

Hydraulic support is important equipment in the fully mechanized mining face, and the control performance of the hydraulic support multi-cylinder system directly affects the smooth progress of coal mining process, which is the basis for the continuous advancement of the coal face. However, the friction force of the hydraulic support in the process of pulling the frame is complex due to the underground environmental load. Moreover, the parameters of the moving cylinder are uncertain, and the state of the system cannot be fully measured, which increases the difficulty of control. A proportional-integral-derivative controller is usually used in electro-hydraulic closed-loop control systems because of its computational complexity, but its robustness is poorly adapted to variable load conditions in the coal mine. Therefore, a robust output feedback position controller is proposed in this paper to improve control accuracy and system robustness with only position signal. The multi-cylinder system of hydraulic support is modeled as a standard type, and then a high-order differentiator is proposed to estimate the immeasurable system states using the output position signal. A neural network compensator is applied to estimate and compensate for the external disturbance of the moving cylinder. Furthermore, the parameters of the ZY3200/08/18D hydraulic support are adopted to analyze the effectiveness of the designed controller in simulations. Finally, a real-time control system of hydraulic support is built, and the experimental results show that the novel robust output feedback controller has improved by 47.2% and 30.6% in tracking accuracy compared to PI controller.

FlexiArch: A computational tool to assess the longevity of buildings through flexibility

The term “Flexibility” in architecture refers to the capability of a building to continuously alter its spatial arrangement to accommodate evolving demands. Flexibility is a crucial aspect of sustainability as it reduces the need for costly structural renovations, relocations, and demolitions. Currently there are limited tools available to evaluate architectural Flexibility during the design process, and the available tools are unwieldy and/or limited in the range of parameters that they consider. The current project was conducted to help fill this gap, by (1) developing a new computational tool, “FlexiArch,” to consolidate and implement Flexibility factors from the prior research literature; and (2) conducting a practitioner survey (n = 237) to assess similarities or differences between working architects’ perceptions of floorplan Flexibility vs. the literature-based computational scoring. We found that the aggregated architects were unable to differentiate literature-based Flexibility features in the floorplans, rating all designs essentially the same. The computational approach, in contrast, demonstrated a much higher level of discrimination. We interpret this result to mean that there is not a strong agreement between current working architects and the academic literature on the topic of floorplan Flexibility. While additional research is needed to fully ascertain the reasons for this discrepancy, we feel it is likely that architectural designers may be, on average, insufficiently trained in understanding the importance of Flexibility and how to achieve it. Tools such as FlexiArch can potentially assist in filling this training/knowledge gap by providing robust quantitative feedback and encouraging designers to consider Flexibility issues in their work.

Model reference adaptive controllers with improved performance for applications in LCL-filtered grid-connected converters

This work presents a control strategy combining a direct Model Reference Adaptive Control (MRAC) with a robust state feedback for current control of grid-connected converters with LCL filters operating under grid impedance variations. The MRAC is based on a gradient law, providing properly current reference tracking and voltage disturbance rejection. The inclusion of a state feedback inner loop provides robust active damping of the filter resonance for an entire range of grid impedances. A systematic control design procedure is proposed, and the combination of both control actions in a multi-loop approach does not add significant computational complexity. The proposal allows to increase the MRAC gains adaptation dynamics, providing suitable responses under grid current reference variations and under typical grid voltage disturbances, including grid faults. Performance indexes as the grid-currents total harmonic distortion and the mean squared error are also improved. In addition, robust stability analysis based on Lyapunov functions are carried out for the inner control loop and for the outer control loop, considering the grid impedance uncertain and with unmodeled dynamics. The strategy is implemented in a commercial digital signal processor, and the compliance of the grid currents with the IEEE 1547 Std. is verified with controller hardware-in-the-loop and experimental results in a 5.4 kW prototype.

Robust stabilization of uncertain nonlinear systems with infinite distributed input delays

This paper studies the robust stabilization problem of a class of uncertain Lipschitz nonlinear systems with infinite distributed input delays. A novel robust predictor feedback controller is developed and the controller gain can be obtained via solving a linear matrix inequality. It is shown that the proposed robust predictor feedback controller can globally exponentially stabilize the concerned uncertain nonlinear system with infinite distributed input delays. The key to the proposed approach is the development of several new quadratic Lyapunov functionals. The obtained results are extended to the case of systems with both multiple constant input delays and infinite distributed input delays. It is noted that the obtained results include some existing results on systems with constant input delays or bounded distributed input delays as special cases. Finally, two examples of Chua’s circuit and spacecraft rendezvous system are presented to illustrate the effectiveness of the proposed robust controllers.

High precision structured H∞ control of a piezoelectric nanopositioning platform.

The inherent weakly damped resonant modes of the piezoelectric nanopositioning platform and the presence of model uncertainty seriously affect the performance of the system. A structured H∞ design is used in this paper to solve the accuracy and robustness problems respectively using a two-loop control structure. The multiple performance requirements of the system are constituted into an H∞ optimization matrix containing multi-dimensional performance diagonal decoupling outputs, and an inner damping controller d is set according to the damping of the resonant modes; the second-order robust feedback controller is preset in the inner loop to improve the robustness of the system; the tracking controller is connected in series in the outer loop to achieve high accuracy scanning; finally, the structured H∞ controller is designed to meet the multiple performance requirements. To verify the effectiveness of the proposed structured H∞ control, simulation comparison experiments are done with the integral resonant control (IRC) and H∞ controller. The results demonstrate that the designed structured H∞ controller achieves higher tracking accuracy compared to the IRC and H∞ controllers under grating input signals of 5, 10, and 20 Hz. Moreover, it has good robustness under 600g and 1000g loads and high frequency disturbances close to the resonant frequency of the system, meeting multiple performance requirements. Compared with the traditional H∞ control, yet with lower complexity and transparency, which is more suitable for engineering practice applications.

Automatic Levothyroxine Dosing Algorithm for Patients Suffering from Hashimoto's Thyroiditis.

Hypothyroidism is a condition where the patient's thyroid gland cannot produce sufficient thyroid hormones (mainly triiodothyronine and thyroxine). The primary cause of hypothyroidism is autoimmune-mediated destruction of the thyroid gland, referred to as Hashimoto's thyroiditis. A patient's desired thyroid hormone concentration is achieved by oral administration of thyroid hormone, usually levothyroxine. Establishing individual levothyroxine doses to achieve desired thyroid hormone concentrations requires several patient visits. Additionally, clear guidance for the dosing regimen is lacking, and significant inter-individual differences exist. This study aims to design a digital automatic dosing algorithm for patients suffering from Hashimoto's thyroiditis. The dynamic behaviour of the relevant thyroid function is mathematically modelled. Methods of automatic control are exploited for the design of the proposed robust model-based levothyroxine dosing algorithm. Numerical simulations are performed to evaluate the mathematical model and the dosing algorithm. With the help of the developed controller thyroid hormone concentrations of patients, emulated using Thyrosim, have been regulated under the euthyroid state. The proposed concept demonstrates reliable responses amidst varying patient parameters. Our developed model provides a useful basis for the design of automatic levothyroxine dosing algorithms. The proposed robust feedback loop contributes to the first results for computer-assisted thyroid dosing algorithms.

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller design challenging. In this paper, a robust adaptive repetitive control scheme is proposed to solve the periodic-trajectory tracking problem for a one-degree of freedom (one-DOF) manipulator driven by two PAMs. First, the system model of the one-DOF PAM-driven manipulator is analyzed and derived. Next, during output constraint design by using a barrier Lyapunov function, a sliding mode surface is reasonably constructed to compensate for the differential term of time-varying constraint parameter. Then, with the help of Lipchitz condition, signal replacement technique and reparameterization are implemented to deal with nonparametric uncertainties in the manipulator system for the subsequent uncertainty compensation by using difference learning method and robust feedback strategy. Moreover, the stability of closed-loop PAM-driven manipulator is proven theoretically by using Lyapunov synthesis. In the end, comparative experiments are provided on a self-built PAM testbed to demonstrated the effectiveness of the proposed robust adaptive repetitive control scheme.

Optimal Robust Tracking Control of Injection Velocity in an Injection Molding Machine

Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time.

Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability. In this paper, we developed a virtual-based haptic training simulator for Endoscopic Sinus Surgery by doing a dynamic characterization of the phenomenological sinus tissue fracture in the virtual environment, using an input-constrained linear parametric variable model. A parallel robot manipulator equipped with a calibrated force sensor is employed as a haptic interface. A lumped five-parameter single-degree-of-freedom mass-stiffness-damping impedance model is assigned to the operator’s arm dynamic. A robust online output feedback quasi-min–max model predictive control framework is proposed to stabilize the system during the switching between the piecewise linear dynamics of the virtual environment. The simulations and the experimental results demonstrate the effectiveness of the proposed control algorithm in terms of robustness and convergence to the desired impedance quantities.

ID: 209013 Combining Remote Programming and Remote Monitoring Towards a Closed-Loop DBS System

Deep Brain Stimulation (DBS) is an effective therapy for Parkinson’s Disease (PD). However, it is currently applied in an open-loop fashion and symptom management relies on iterative programming. The field is trending towards closed-loop solutions that enable stimulation parameters to be adjusted in real-time in response to patient-specific feedback signals. Identifying reliable and robust feedback signals is a challenge in developing closed-loop DBS solutions. As such, in this study, we aim to combine remote monitoring and remote programming to identify specific sensor signals that correlate with DBS programming and symptom profile changes.

Robust error feedback output regulation of a 2 × 2 hyperbolic system

Abstract In this paper, we consider the robust error feedback output regulation problem for a linear $ 2 \times 2 $ hyperbolic system with system uncertainties and disturbance signals. Using the backstepping method, the state feedback controller for the system without the system uncertainties and the external disturbance signals (nominal system) is designed. In terms of the measurable regulation error, an observer system is designed to recover the state of the nominal system, and an observer-based error feedback controller is obtained to solve the robust output regulation problem. Moreover, we prove that the controller is robust to the system uncertainties and the disturbance signals, and show that the state of the closed-loop system is bounded. The numerical simulations are presented to illustrate the effectiveness of the theoretical results.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Validation of a portable shoe tread scanner to predict slip risk

Problem: Worn shoes are an important contributor to occupational slip and fall injuries. Tools to assess worn tread are emerging; imaging tools offer the potential to assist. The aim of this study was to develop a shoe tread scanner and evaluate its effectiveness to predict slip risk. Methods: This study analyzed data from two previous studies in which worn or new slip-resistant shoes were donned during an unexpected slip condition. The shoe tread for each shoe was scanned using a portable scanner that utilized frustrated total internal reflection (FTIR) technology. The shoe tread parameters of the worn region size (WRS) for worn shoes and total contact area for new shoes were measured. These parameters were then used to predict slip risk from the unexpected slip conditions. Results: The WRS was able to accurately predict slip risk, but the contact area was not. Discussion: These findings support that increased WRS on the shoe outsole is associated with worse slip outcomes. Furthermore, the tool was able to offer robust feedback across a wide range of tread designs, but the results of this study show that the tool may be more applicable for slip-resistant shoes that are worn compared to their new counterparts. Summary: This study shows that FTIR technology utilized in this tool may be a useful and portable method for determining slip risk for worn shoes. Practical Applications: This tool has the potential to be an efficient, objective, end-user tool that improves timely replacement of shoes and prevention of injuries.

Stakeholder Analysis in Hospitality Management: An Examination of Tourism Infrastructure Reform and the Impact on the Workforce in Brazil

This study provides an in-depth analysis of stakeholder dynamics in Brazil's hospitality sector amid ongoing tourism infrastructure reforms and their implications for the workforce. Stakeholder analysis is a critical tool in hospitality management, aiding in understanding the perspectives, interests, and potential impacts of various stakeholder groups on industry trends and reforms. In the context of Brazil's hospitality industry, key stakeholders range from government agencies and investors to local communities, customers, and the workforce itself. Brazil has launched substantial tourism infrastructure reforms under programs such as "Brasil Mais Turismo," aiming to enhance the country's attractiveness as a global tourist destination. These reforms target improvements in accommodations, transportation, visitor amenities, and overall accessibility. However, the reform process is complex and multifaceted, implicating various stakeholder interests and carrying significant implications for the hospitality workforce. The study emphasizes that the interests and influences of stakeholders can shift rapidly in response to societal, economic, and industry changes. Therefore, stakeholder analysis should be an ongoing process, constantly updated and adapted to reflect these shifts. Further, the study analyzes the impact of tourism infrastructure reforms on the hospitality workforce in Brazil. The reforms present both opportunities and challenges for the workforce. While they promise improved working conditions, skill development opportunities, and job creation, they also pose challenges such as potential job displacement and skill mismatches. The study concludes with recommendations for enhancing stakeholder engagement in Brazil's hospitality sector and optimizing the benefits of tourism infrastructure reforms. These include strengthening partnerships among key stakeholders, adopting a proactive approach to workforce development, and implementing robust feedback and adjustment mechanisms to ensure the reforms remain responsive to evolving stakeholder needs and industry trends. Keywords: Stakeholder analysis, Hospitality management, Tourism infrastructure reform, Workforce, Brazil