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Based on the variable gain extended state observer, a finite-time fault-tolerant control strategy is developed for the quadrotor unmanned aerial vehicle with actuator faults and external disturbances. Firstly, a novel variable gain extended state observer is designed to estimate the unknown external disturbances, which mitigates the initial peaking phenomenon existing in traditional extended state observer-based methods. Meanwhile, the neural networks are applied to accurately approximate unknown couplings online. Moreover, with the help of the projection operator technique, the unknown actuator faults are observed in real time. Combined with the backstepping framework, the finite-time robust fault-tolerant control scheme is constructed and the stability is strictly proved via Lyapunov's theory. Finally, the validity of the developed control scheme is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this work, an adaptive learning robust controller is proposed to suppress the vibration of offshore platforms, which are subject to waves, winds, varying control delays and parametric perturbations. To realize nonlinear uncertainty approximation under the bounded H∞$H_\infty$ performance, the H∞$H_\infty$ controller incorporates both an online adaptive part and an offline fixed part. The adaptive part constructed by neural networks adjusts online, while the fixed part is obtained by regulating the H∞$H_\infty$ performance. Importantly, adaptive updating strategy does not require accurate values or upper bounds for real‐time control delay or uncertainty. Several comparable experiments demonstrate the feasibility and effectiveness in vibration‐suppression of the designed adaptive controller in shallow/deep water. This scheme significantly reduces system response variations due to structural and hydrodynamic uncertainty, as well as additional random environmental forces caused by winds. [ABSTRACT FROM AUTHOR]

This paper presents a novel input channel gain adaptive active disturbance rejection control (ICGA‐ADRC) framework for the nonlinear control system with large load variation. This paper first introduces the design of ICGA‐ADRC and proves its stability through the rigorous Lyapunov approach. Subsequently, the proposed controller is simulated by subjecting it to a nonlinear mass‐spring‐damping system characterized by significant load variations. Furthermore, the position‐tracking performance of the controller is tested experimentally by using a motor‐driven pendulum system with various loads. Finally, the results of both simulations and experiments show that the ICGA‐ADRC is capable of compensating for system model uncertainty and external perturbation while accurately estimating the system input channel gain in real‐time. The innovation of this paper is that the robustness of active disturbance rejection control (ADRC) to the system load variation is significantly strengthened by integrating robust adaptive finite‐time parameter estimation method into the conventional ADRC control architecture. [ABSTRACT FROM AUTHOR]

The quadratic matrix equation (QME) Q(X)=AX2+BX+C=0,$$\begin{equation*} Q(X)=AX^{2}+BX+C=0, \end{equation*}$$occurs in the branches such as the quadratic eigenvalue problems and quasi‐birth‐death processes. Also, the numerical solution of QMEs is an essential step in many computational methods for linear‐quadratic and robust control, filtering, controller order reduction, inner‐outer factorization, spectral factorization, and other applications. In this study, schemes are presented to solve the QME based on the Hermitian and skew‐Hermitian splitting (HSS). It is shown that the proposed schemes converge to the solutions of the QME. Finally, some examples are solved to discover the application of the schemes in comparison with Newton's method. [ABSTRACT FROM AUTHOR]

In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s -1 yr -1 (R2 = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments. [ABSTRACT FROM AUTHOR]

Existing methods for nonlinear robust control often use scenario‐based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time‐varying uncertainty. This paper draws from local reduction methods used in semi‐infinite optimization to solve robust optimal control problems with parametric and time‐varying uncertainty. By iteratively adding interim worst‐case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. We show that the local reduction method for optimal control problems consists of solving a series of simplified optimal control problems to find worst‐case constraint violations. In particular, we present examples where local reduction methods find worst‐case scenarios that are not on the boundary of the uncertainty set. We also provide bounds on the error if local solvers are used. The proposed approach is illustrated with two case studies with parametric and additive time‐varying uncertainty. In the first case study, the number of scenarios obtained from local reduction is 101, smaller than in the case when all 214+3×192$$ {2}^{14+3\times 192} $$ extreme scenarios are considered. In the second case study, the number of scenarios obtained from the local reduction is two compared to 512 extreme scenarios. Our approach was able to satisfy the constraints both for parametric uncertainty and time‐varying disturbances, whereas approaches from literature either violated the constraints or became computationally expensive. [ABSTRACT FROM AUTHOR]

Sliding mode control strategies are widely applicable to systems subject to disturbance and modeling uncertainties. However, since sliding mode controllers are typically only concerned with the evolution of a virtual sliding variable, potential state constraints placed on the system are often neglected when applying such strategies. Motivated by this problem, in this article, we propose a novel sliding mode control strategy for discrete‐time systems with the aim of satisfying constraints placed on their output. The proposed approach uses a new sliding hyperplane designed to ensure a linear relationship between the sliding variable and the system output. The main contribution of the article involves demonstrating that when using this approach, one can indirectly impose bounds on the output rate of change by limiting the convergence rate of the sliding variable to zero. The obtained result has been documented for two cases. In the first part of the article, the proposed approach has been applied to systems with full state information always known. Then, the strategy has been extended to plants with only output signal available, where the full state needs to be estimated. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations. [ABSTRACT FROM AUTHOR]

Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive control framework integrating modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC). The goal is to achieve precise tracking performance, high robustness, a fast response, a finite convergence time, reduced chattering, and effective handling of unknown system dynamics. A key feature is the incorporation of a higher-order sliding mode observer, eliminating the need for velocity feedback. This provides a new solution for overcoming the inherent variations and uncertainties in robot manipulators, enabling improved accuracy within fixed convergence times. The efficacy of the proposed approach was validated through simulations and experiments on an exoskeleton robot. The results successfully demonstrated the controller's effectiveness. Stability analysis using Lyapunov theory proved the closed-loop system's uniform ultimate boundedness. This contribution is expected to enable enhanced control for rehabilitation robots and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um. [ABSTRACT FROM AUTHOR]

In this paper, a delayed distributed proportional control is developed for a class of multiple-input-multiple-output systems in order to reduce the effects of external disturbances and uncertain dynamics. The system under consideration is a three degrees-of-freedom planar robot, consisting of an active joint driving the first link and passive flexible joints for the second and third links. The control challenge for this system is to drive the flexible arm around the desired setpoint or trajectory by controlling the first link. Actually, by using a specific Lyapunov-Krasovskii storage functional associated with a constrained optimization problem, the robustness of the proposed proportional retarded control action is achieved. This analysis, together with the attractive ellipsoid concept allows to conclude with Ultimately-Uniformly-Bounded stability. The closed-loop control algorithm is tested in numerical simulation and a comparative study with some existing recent control schemes based on the performance error is presented. • The proposed control scheme belongs to a class of multivariable proportional time delay distributed controller. • After finite time the trajectories of a flexible manipulator stay within reduced size attractive ellipsoid. • Ultimate Uniformly Bounded Stability of the controlled system is demonstrated by using a Lyapunov-Krasovskii functional. • The proposed algorithm can also be adapted for a class of nonlinear uncertain/disturbed multivariable output systems. • A sufficient condition to guarantee the feasibility of specific Bilinear Matrix Inequality is presented. [ABSTRACT FROM AUTHOR]

This paper provides a new robust control approach to uncertain mechanical manipulators in the computed torque framework. With respect to the fact that model uncertainties occurring from the framework could make the overall system unstable although the resulting nominal plant is stabilized, this paper develops a readily applicable method for estimating such uncertain elements. More precisely, we propose a time‐delay compensation method, by which prior values relevant to the model uncertainties in a sufficiently small time instant are used for such an estimation. The theoretical effectiveness of the proposed method is validated by establishing the two different arguments on operator‐theoretic exponential stability and Lypaunov‐based disturbance input‐to‐state stability. Finally, some simulation and experiment results are provided to demonstrate the overall arguments developed in this paper for both the theoretical and practical aspects. [ABSTRACT FROM AUTHOR]

This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in the violation of state constraints and instability of the controlled system. Firstly, the conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady‐state optimisation problems. Then the contractive state set, which takes into account the effect of disturbances on system states, is constructed to guarantee the robust satisfaction of state constraints. Finally, a stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC. An isothermal chemical reactor system is employed to verify the effectiveness of the controller proposed here. [ABSTRACT FROM AUTHOR]

The robust tracking control problem of the leader‐follower multiagent systems affected by asymmetric input saturation and external disturbances is addressed in this article, by following three steps. First, an radial basis function neural network (RBFNN) is developed to estimate the external disturbances and supersaturation, where the supersaturation is induced by asymmetric saturation actuator. Second, combining with the developed radial basis function neural network, a reduced‐order observer‐based static protocol and a reduced‐order observer‐based adaptive one are designed for leader‐follower systems with un‐directed communication topology as well as those with directed communication topology, respectively. Third, some mild premises are given to guarantee the semiglobal robust leader‐follower consensus for the above mentioned two kinds of multiagent systems. Finally, the theoretical results are verified by several numerical simulations. [ABSTRACT FROM AUTHOR]

This article investigates the robust tracking control problem for a class of Euler–Lagrange systems in presence of parameter uncertainties and external disturbances. Through system transformation and theoretical analysis, an adaptive dynamic programming (ADP) algorithm with two adaptive neural networks (NNs) and a suitable triggering mechanism is proposed to attain the robust stability of the closed‐loop system. A single critic NN is leveraged to implement the approximate optimal controller design. Particularly, an NN‐based feedforward compensation is developed to cope with the uncertainties with unknown bounds. Two different triggering mechanisms are respectively constructed to reduce the budget of sampling, communication and computation, namely the dynamic event‐triggering mechanism (DETM) and the self‐triggering mechanism (STM). The DETM is utilized to decide the update of remote controller and critic NN weight, which can yield a larger inter‐event interval than the static event‐triggering mechanism. Also, the Zeno‐free behavior is guaranteed. Moreover, it is a novel attempt to introduce the STM into ADP design, which relaxes the demand of dedicated hardware online monitoring the event‐triggering condition. Then it is demonstrated that all signals in the closed‐loop system are uniformly ultimately bounded (UUB) via Lyapunov‐based stability analysis. Finally, a simulation example of 2‐link robotic system is implemented to verify the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In allusion to the multi‐variable robust control for the turbofan engine with the large envelope, an L2−L∞$$ {L}_2-{L}_{\infty } $$ almost output regulation (AOR) problem is discussed for a class of turbofan switched systems under multi‐source disturbances and time delay. The addressed disturbances include the regulated disturbance signal, the modeled disturbance from the input channel and the energy‐bounded disturbance. First of all, a disturbance observer is constructed to estimate the unknown disturbance in the system input. Then, by combining the output regulation method and the disturbance observer‐based control (DOBC) method, a switched composite regulation controller is developed to deal with the AOR problem of the turbofan switched systems and reject the adverse effects of disturbances. Through the multiple Lyapunov function method, a set of sufficient conditions are obtained to guarantee the AOR property with the L2−L∞$$ {L}_2-{L}_{\infty } $$ performance and disturbance suppression ability. Finally, the validity of the proposed method is demonstrated by applying the composite anti‐disturbance regulator to the regulation of the speed and pressure ratio for the turbofan model. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive augmented linear quadratic integral (LQI) controller for a 2 degrees of freedom (DoF) helicopter to improve the closed-loop performance of the baseline controller even when the system contains unmodeled dynamics and exogenous disturbances. Although the LQI controller is widely used in aircraft control applications for its excellent reference-tracking capability and inherent robustness, the performance of the baseline LQI controller degrades when the aircraft model has unmodeled dynamics and external disturbances, which are inevitable in aircraft applications. Hence, to deal with the parameter uncertainty and unmeasured disturbances, we present an adaptive control framework using a model reference adaptive control (MRAC) scheme combined with the baseline LQI controller. The key aspect of the proposed control scheme is that the robustness of the closed-loop control system to deal with the matched uncertainty is enhanced by tracking error augmentation with the control law. Moreover, this work proves the asymptotic stability of the closed-loop system using the inverse Lyapunov function and Barbalat's lemma. The efficacy of the adaptive augmented LQI is experimentally validated through hardware in loop (HIL) implementation on a laboratory scale 2 DoF helicopter workstation for several real-time test cases. Experimental results accentuate that the adaptive augmented control scheme can significantly improve not only the reference-tracking capability but also the robustness of the closed-loop system. [ABSTRACT FROM AUTHOR]

In this study, the issue of simultaneous fault detection and control (SFDC) for a class of switched linear systems with input and state delays is being addressed. A detector/controller unit (dynamic observer/observer-based controller) is suggested for a residual generation that simultaneously takes into account the control and fault detection objectives using specific performance indices. It is assumed that the delay will fluctuate within a range with a lower bound that is not zero. Based on a Lyapunov–Krasovskii functional (LKF), and thanks to the free-weighting matrix and projection lemma, a new sufficient condition for developing a new method is put forth in terms of a set of linear matrix inequalities (LMIs). To illustrate how well the suggested results work, the proposed method is applied to a vehicle roll dynamic. [ABSTRACT FROM AUTHOR]

This article presents a new control approach for the optimal and reliable operation of the microbial fuel cell, as one of the most important sources of renewable energy. Considering several adverse effects on the performance of the microbial fuel cell, including the effects of uncertainty, disturbance, and of course, taking into account the nonlinear dynamics and the necessary conditions for the stable and optimal operation of the fuel cell, this paper presents a novel robust hybrid approach to its performance management. The innovative approach by using adaptive and sliding mode techniques, by focusing on providing a theoretical solution and not changing the structure of the fuel cell, reduces and suppresses the effects of disturbance and uncertainty of the model, and by guaranteeing the stable and reliable operation of the microbial fuel cell in the Lyapunov concept, provides rapid regulation of system states. The results of simulation and comparison in the MATLAB environment show the efficiency and optimal performance of the fuel cell system under the planned control method from the perspective of improving the transient and permanent response. [ABSTRACT FROM AUTHOR]

In response to the influence of motor interference, damping, friction, and other uncertain factors on the operation of electric power steering systems under extreme working conditions, this study proposes a control strategy for electric power steering systems based on an active disturbance rejection algorithm. In ADRC, the fastest tracking differentiator is used to arrange the transition process for the target signal, and the extended state observer compensates for the total disturbance in the system. Phase compensation has been performed on the monitoring torque by using the torque differentiation method. The Simulink/Carsim simulation results show that ADRC has significantly improved anti-disturbance performance compared to PID and fuzzy PID. When using ADRC, the tracking accuracy of the assisted current is enhanced by 45.8%–75.8%, and the current adjustment time is reduced by 35.6%–61.7%. After phase compensation, the monitoring torque overshoot is reduced by 83.3%. Therefore, the proposed control strategy improves EPS's robustness and steering feel. [ABSTRACT FROM AUTHOR]

In this paper, a power hardware in the loop (p-HIL) validation of a wind energy conversion system (WECS) interconnected into the balanced or unbalanced grid is presented. WECS power is obtained from wind fluctuations of the Yucatan Peninsula, Mexico, and transferred through the DC-AC power electronic converter (PEC). A robust control law based on a simple PI regulator and phasor analysis is proposed, having advantages, such as continuous power generation, operating both under balanced and unbalanced grid conditions; total harmonic distortion (THD) reduction less than 3%; power factor (PF) correction close to the unit; balanced grid currents; a simple mathematical analysis. The main objective is to keep the WECS connected to the electrical grid even in the presence of unbalanced threephase voltages, including contributions such as: i) VDC ripple reduction; ii) Balanced currents; iii) Sinusoidal currents; iv) Free of Sequence calculation; v) Free of dq0 or αβ Transforms; vi) Simple PI Loop control; and vii) Experimental Validation. The WECS efficiency and robustness are assessed by a complete mathematical examination, validated by the simulations in MATLABSimulink®, and support with experimental results through the real-time simulator Opal-RT Technologies®, a low-scale laboratory prototype, and p-HIL methodology. The results present a DC link voltage constant at 75 V and optimal and reliable power integration with an efficiency of 95%. [ABSTRACT FROM AUTHOR]

Structural and functional brain adaptations in bilingual speakers are well documented in the neurolinguistic literature. However, far less is known about neural changes evidenced in multilingual speakers. This study investigates brain plasticity in a group of highly proficient multilinguals, fluent in four or more languages, compared to a group of monolinguals. An ROI analysis used to evaluate differences in core linguistic regions and regions associated with language control revealed robust decreases for multilinguals in grey matter thickness of two brain regions within the parietal lobe (i.e., precuneus and angular gyrus), involved in lexico-semantic processing, memory retrieval, and control maintenance. We discuss our findings in the context of emerging models characterizing trajectorial changes in brain structures associated with language experience. We consider how the demands of optimal functioning within multi-linguistic environments may foster cortical changes that manifest as decreased GM thickness in highly proficient multilingual compared to monolinguals. [ABSTRACT FROM AUTHOR]

Summary: The transmission distance and the output load of wireless power transfer (WPT) system are particularly prone to change, which results in higher requirements for the robustness and dynamic performance of the control. In order to reduce the influence of these characteristics, an H∞ loop shaping controller based on generalized state space averaging (GSSA) method is proposed in this paper. Based on the GSSA model, an appropriate compensation function is constructed for the WPT system to the target loop transfer function. The nominal coprime factorization of WPT system is carried out, and on this basis, H∞ controller is achieved by Riccati equation to minimize the infinite norm from disturbance to error output. The designed controller meets the requirements of robust. Furthermore, the obtained high‐order controller is discretized by a reasonable discretization method, and the details and limitations of its practical application are discussed based on the hardware computational capacity. The experimental results show that the proposed controller has better robustness and dynamic performance and flexibility than the mixed sensitivity H∞ controller and the Z‐N PI controller. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22μm (standard deviation is 0.2μm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution. [ABSTRACT FROM AUTHOR]

Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at https://youtu.be/aJMq8ROW51g. [ABSTRACT FROM AUTHOR]

There has been much debate in recent years about how open research practices, which have been promoted in efforts to improve research robustness, may (not) be appropriate for qualitative methodologies, particularly in educational research. Among these is a concern for replication efforts. Makel et al. (2022) make the case that "replication is relevant to qualitative research". The authors argue that concerns surrounding the transferability, intentionality, and transparency of qualitative research may be eased, or responded to, by replication studies. Here, I argue that there are three fundamental questions that need unpacking before declarative claims can be made about the relevance of replication to qualitative research. This includes critical questioning of what we mean by: replication, qualitative research, and rigour. I address each of these issues and encourage a more nuanced appraisal of how replication may, or may not, be epistemologically, ontologically, or methodologically compatible with the goals of qualitative research. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on the design of fixed‐time fuzzy adaptive robust H∞$$ {H}_{\infty } $$ control of nonlinear systems in a nonstrict‐feedback form that includes output constraints, input saturation, and external disturbance. Fuzzy systems have been utilized to deal with uncertainties and non‐linearity in the system dynamic model. By introducing novel control and adaptive rules, there is no need to approximate the input saturation function with the hyperbolic tangent function. A Barrier Lyapunov Function (BLF) ensures that systems outputs do not violate the defined constraints. By combining the backstepping and BLF method, asymptotic convergence of tracking error signal to zero in fixed‐time is proved. At the same time, H∞$$ {H}_{\infty } $$ index is also met in dealing with the disturbance effects. Finally, the simulation results of the proposed control method in two examples confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Defining and measuring resilience using a unified framework has been a topic of intense research. This article presents a perspective on how resilience could be quantitatively assessed through a set of indices. It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience. It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle. In particular, it is proposed that resilience should be evaluated according to different time periods, i.e. before, during and after a disruption has occurred. Four dimensions of resilience, namely reliability, robustness, recovery and reconfigurability, can then be used to make up a set of indices for resilience. For numerical illustration, these indices are computed based on recovery data arising from Hurricane Sandy in October 2012. Finally, it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations, and in turn, that of sovereign countries in their quests for developing smart cities. [ABSTRACT FROM AUTHOR]

In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is efficient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown terms, providing information for the disturbance observer. Besides, the tuning parameters are chosen optimally by adopting the grey wolf optimization (GWO) algorithm. The closed-loop system stability is also proven by the barrier Lyapunov function (BLF) while the total uncertainties, including system dynamics, friction, and disturbance, are tracked by the total estimation. Thus, the predetermined performance, robust behavior, and high-precision estimation are the achievements of the presented controller for PTS. To confirm the ability of the proposed control idea, simulations are provided. Furthermore, a comparison scenario is also considered to emphasize the supremacy of the proposed control system. [ABSTRACT FROM AUTHOR]

The Vedde Ash, originating from the Katla central volcano, Iceland, and taken to be dispersed across the North Atlantic and Europe at ~ 12 ka BP, is widely used as a geochronological marker. However, distal tephra layers with compositions like the Vedde Ash but of younger ages question the reliability of Vedde-like tephra layers as robust age control. Vedde-like tephra layers are rare in Icelandic sedimentary sequences and, where present, lack firm age control. Providing well-constrained local records of Early Holocene Katla layers is therefore critical to assess uncertainties related to the use of the Vedde Ash. Here we report three visible and stratigraphically separated Early Holocene Katla tephra layers from Torfdalsvatn, a lake in north Iceland, each with chemistry similar to the Vedde Ash. Using high-resolution 14C chronologies, we provide ages (± 1σ) for these tephra layers of 11,315 ± 180, 11,295 ± 195, and 11,170 ± 195 cal a BP. These observations reinforce that multiple explosive eruptions of Katla occurred over a 1000-year interval in the Early Holocene and challenge the precision of some paleoclimate records using the Vedde Ash as a geochronometer where age control is equivocal. This may lead to a re-evaluation of age models for some Early Holocene North Atlantic records. [ABSTRACT FROM AUTHOR]

A high speed rigid rotor system supported by active magnetic bearings (AMBs) puts forward higher requirements on the performance of controllers. The fuzzy variable gain H‐infinity robust control (FVGRC) method is proposed to simplify controller design while suppressing system uncertainty. First, the T‐S fuzzy model of four‐degree‐of‐freedom (4‐DOF) AMBs was established based on the 4‐DOF AMBs rigid rotor equivalent coupling model optimised by the μ‐gap metric. Then, based on the fuzzy models of AMBs systems under different operating conditions, the relative optimal weight function value of H‐infinity robust controller was calculated when AMBs system was at value of the speed endpoint, and the fuzzy variable gain H‐infinity robust (FVGR) controller was designed on this basis. Finally, the simulation and experimental results showed that the FVGRC can simplify the model of controller and ensure the stable operation of AMBs rigid rotor system in the full speed range under high‐speed conditions. [ABSTRACT FROM AUTHOR]

To suppress parameter mismatch and improve the output performance of model predictive control (MPC), a new robust virtual‐vector MPC strategy is proposed for a five‐phase permanent‐magnet motor in this study. Firstly, the incremental predictive model is applied to remove the impact of flux mismatch. Then the d‐q axis inductance parameter sensitivity of MPC is analysed, which produces the predictive current error between the normal parameter and mismatched parameter. Based on the current error, a new cost function is designed to select the voltage vector more accurately when the d‐q axis inductance parameter mismatch occurs. Thus, good robustness to the parameter's variation can be guaranteed. Afterwards, the duty cycle modulation technology is applied to allocate the duration time of two adjacent vector‐vectors and zero vector. So the motor current harmonics and torque ripple can be considerably suppressed. Finally, the experimental results are provided to show the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

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, proportional-integralderivative (PID) and H-infinity 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 PID and H-infinity controllers. The PID controller have the advantages of simplicity and high response speed while the H8 controller provides better nominal performance, robustness, and stability. The H8 controller can handle parametric uncertainty without requiring pure integral term which is a significant advantage over the PID controller. On the other hand, the PID controller falls short of achieving robust performance, making it less suitable for systems that require high levels of performance and robustness. In summary, the H8 controller is a more comprehensive solution for ensuring the best performance of a system. In contrast, the PID controller may be more suitable for systems with less stringent performance requirements. [ABSTRACT FROM AUTHOR]

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison's equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive learning structure based on neural networks (NNs) to solve the optimal robust control problem for nonlinear continuous-time systems with unknown dynamics and disturbances. First, a system identifier is introduced to approximate the unknown system matrices and disturbances with the help of NNs and parameter estimation techniques. To obtain the optimal solution of the optimal robust control problem, a critic learning control structure is proposed to compute the approximate controller. Unlike existing identifier-critic NNs learning control methods, novel adaptive tuning laws based on Kreisselmeier's regressor extension and mixing technique are designed to estimate the unknown parameters of the two NNs under relaxed persistence of excitation conditions. Furthermore, theoretical analysis is also given to prove the significant relaxation of the proposed convergence conditions. Finally, effectiveness of the proposed learning approach is demonstrated via a simulation study. [ABSTRACT FROM AUTHOR]

Consumption of probiotics, which are beneficial live microorganisms, has received a lot of attention because of their potential to improve health and wellness. Robust quality control measures are necessary to ensure the safety of probiotics and maximize their health effects. This review delves into the topic of quality management in probiotics, highlighting the significance of sticking to strict guidelines from manufacture to storage to distribution. Probiotic quality standards, Good Manufacturing Practices (GMP) implementation, quality control and testing techniques, and documentation and traceability systems are all discussed in detail. The importance of taking precautions to avoid microbial contamination, meeting all applicable regulations, and clearly marking and packaging probiotic products is also emphasized. In addition, it reviews the clinical evidence supporting the possible health advantages of probiotics and investigates the processes through which probiotics enhance health. The review continues by stressing the significance of educating and informing consumers about probiotics and their proper use in order to maximize health benefits. Probiotics' potential health benefits can be maximized and consumer faith in these helpful microbes can be bolstered by adopting thorough quality management measures to ensure their safety, efficacy, and consistency. [ABSTRACT FROM AUTHOR]

In this paper, a composite fixed‐time sliding mode controller is proposed to address the problems of finite‐time escape and large initial error inherent in disturbance observer (DOB) based controllers. The proposed hybrid control strategy utilizes a fixed‐time DOB and is designed to stabilize a class of second‐order systems in the presence of both matched and mismatched disturbances, while ensuring fixed‐time convergence. First, a fixed‐time DOB is developed to estimate both types of disturbances. Second, a robust sliding mode variable with variable exponent coefficient and a former finite‐time sliding mode controller are proposed to guarantee state convergence without using DOB estimation, thereby preventing the state from diverging due to the unknown large initial error caused by DOB and ensuring convergence prior to switching. Third, a latter robust fixed‐time DOB‐based controller is designed after exact estimation of disturbances is achieved and an estimation of the overall convergence time is provided. Furthermore, strict Lyapunov analysis is employed to prove that the disturbed system under the fixed‐time DOB and composite sliding mode controller is fixed‐time stable. Simulation results for a standard planar uncertain system and Buck converter with mismatched disturbance demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop an advanced flight controller that can effectively manage aircraft systems, maintain control, and effectively reject external disturbances and faults. To achieve this goal, the controller is designed using K-control gains in combination with the extremum-seeking control algorithm. The K-gains, crucial for ensuring stability and control of the fixed-wing unmanned aerial vehicle, are determined using the Full State Feedback (FSF) technique. However, FSF, does not have the capabilities to mitigate significant external threats. To mitigate these risks, the Extremum Seeking Control (ESC) method is incorporated into the controller. This adaptive approach enables the system to adapt and adjust parameters in the presence of unknown inputs, ensuring the system's response remains under control. In addition to the ESC-based controller, a Linear Quadratic Gaussian (LQG) regulator is also designed, and our work is compared with it to show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This document discusses the challenges of achieving high-power quality in modern power grids with a large share of distributed power production, particularly due to the integration of renewable energy sources (RES). The variable nature of RES can lead to disturbances and power quality degradation. The document highlights the importance of voltage source converters and control systems in enabling the flexible operation of RES. It also mentions the concept of microgrids, which are closely connected to power quality and other factors such as the Internet of Things and cyber security. The document includes eight research papers covering various topics related to power quality analysis and control of renewable energy sources in power grids. These topics include power factor correction, supercapacitor parameters identification, pulse width modulation, disturbance analysis, vibration analysis of motors, control of converters, phasor measurement units, and optimization of controller parameters. The document concludes by expressing gratitude to the authors, staff, and reviewers involved in the research papers. [Extracted from the article]

This paper proposes a modification of a Sliding Mode Classical Observer (SMCO) to adapt it to the fractional approach. This adaptation involves using a set of definitions based on fractional calculus theory, particularly the approach developed by Riemann–Liouville, resulting in a Sliding Mode Fractional Observer (SMFO). Both observers are used to perform disturbance reconstruction considered additive in a Quadrotor Unmanned Aerial Vehicle (UAV) model. Then, this work presents the fractional-order sliding mode observer's mathematical formulation and integration into the Quadrotor UAV model. To validate the quality of the disturbance reconstruction process of the proposed SMFO observer scheme, numerical simulations are carried out, where a reconstruction quality indicator (BQR) is proposed based on the analysis of performance indices such as the Mean Square Error (MSE), the First Probability Moment (FPM), and Second Probability Moment (SPM), which were obtained for both the SMCO and the SMFO. The simulation results demonstrate the efficacy of the proposed observer in accurately reconstructing disturbances under various environmental conditions. Comparative analyses with SMCO highlight the advantages of the fractional-order approach in terms of reconstruction accuracy and improvement of its transitory performance. Finally, the presented SMFO offers a promising avenue for enhancing the reliability and precision of disturbance estimation, ultimately contributing to the advancement of robust control strategies for Quadrotor UAV systems. [ABSTRACT FROM AUTHOR]

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system's robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system's potential for real-world application. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]