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Investigation and implementation of Active Willis Metamaterials (AWM) have been done exclusively, in all the available literature, by approaches that do not rely on any solid control theory basis. When coupled with piezoelectric control elements, the available approaches have not included, from the first principles, the exact form of the constitutive relationship of the piezoelectric materials. Furthermore, in all these approaches, stability analysis, robustness, ability to accommodate uncertainty or parameter changes, or consideration of disturbance rejection has not been addressed at all. More importantly, the available formulations have always mixed the flow and effort variables of the AWM, resulting in a form that is totally incompatible for the use in generating, investigating, or even designing any appropriate sensing or control applications of the material. In this paper, the piezoelectric-based AWM is modeled, from the first principles, to develop a constitutive coupling form that enables its use in actuation, sensing, and as an integrated controller that can be analyzed, designed, and optimized using the classical, optimal, and robust control system theories. Lagrange dynamics formulation is used to generate the equations governing the Willis coupling, the piezoelectric coupling, as well as the active robust controller. With this developed controlled-based structure of the AWM, the inherent and powerful capabilities of the AWM that lie in its ability to robustly control the material properties themselves such as the compliance (or stiffness) and specific volume (or density) are demonstrated in great detail via several numerical examples. Controlling these properties enables the AWM to be used in numerous important and imaginative applications such as cloaking, beam shifting, beam focusing, as well as many other applications that are limited only by our imagination. [ABSTRACT FROM AUTHOR]

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively. [ABSTRACT FROM AUTHOR]

In automated manufacturing systems (AMSs), because of unpredictable failures, resources can lose functions such that the deadlock control methods, in existence, are invalidated. In this paper, a robust deadlock control approach is proposed for AMSs with multiple unreliable resources. The considered AMSs modelled by Petri nets (PNs) allow to acquire different types of resources at each processing stage. In order to visualise the fact that resource failures occur in AMSs, recovery subnets are designed for the modelling AMSs to depict the failures and recoveries of resources. Based on a siphon detection method performed by a set of integer linear programming formulations, a control specification is proposed. Control places (monitors) with their control variables are designed for the detected unmarked siphons at a marking to guarantee that they are always marked even if some unreliable resources break down. Iteratively, all unmarked siphons are detected and controlled. Therefore, a robust deadlock supervisor is synthesised to ensure the controlled system's liveness no matter there exist resource failures or not. The theoretical analyses and proof are given to verify the correctness of the proposed method. Finally, the comparative studies are presented to expound the proposed method's effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event‐triggered control. Introducing a time‐varying constraint function transforms the original practical prescribed time‐tracking control issue into a tracking error constraint problem. An event‐triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, the model‐free robust control problem is investigated for nonlinear systems with a relaxed condition of initial admissible control. An advanced integral reinforcement learning method is developed, which merges the adaptive network‐based fuzzy inference system (ANFIS) and pre‐training of the initial weights. To loose the condition for choosing the initial control law, pre‐training of initial weights is established by utilizing the ANFIS to provide the information corresponding to the system model, which is applicable to the model‐free issue. Based on the actor‐critic structure, the approximate optimal control law is obtained by employing adaptive dynamic programming. Redesigning the obtained control law, the robust controller can be derived to stabilize the system with the uncertain term. Eventually, two examples are utilized to verify the effectiveness of the constructed algorithm. [ABSTRACT FROM AUTHOR]

The traditional Nussbaum‐type functions encounter challenges when extended to systems with multiple unknown control directions, as the coupling between the Nussbaum‐type function and Nussbaum‐type gain may lead to mutual elimination. To address this issue, a novel Nussbaum‐type function is introduced to handle multiple unknown control directions for stochastic systems. The stability of the proposed Nussbaum‐based design can be guaranteed using the Lyapunov analysis. To conserve computational resources, a static composite event‐triggered mechanism is integrated with intermittent feedback in the Nussbaum‐based design. In contrast to conventional event‐triggering mechanisms that depend solely on control signals, the presented design incorporates tracking errors into the event‐triggering conditions. Moreover, to further alleviate computational burdens, we further develop a dynamic composite event‐triggered mechanism. With the overall design scheme, bounded tracking errors and reduced computational burden can be ensured. Simulation examples validate the efficacy of the proposed adaptive Nussbaum composite event‐based robust control design. [ABSTRACT FROM AUTHOR]

Modular multilevel green converter based high voltage direct current (MMC‐HVDC) transmission system has become a practical solution to interconnect renewable energy sources to main AC grids. Connecting the MMC to a weak AC system is still a challengeable problem. This paper proposes a sliding mode control‐based method for the MMC. By considering short circuit ratio parameter of the AC grid as uncertainty, a suitable mathematical model is developed. Also, relations among control parameters and their validity conditions are obtained. The proposed control scheme has faster dynamic responses to uncertainty and external disturbances compared to the conventional vector current control method with proportional–integral controller. At last, simulation results in the MATLAB/SIMULINK software environment are presented that the proposed control scheme is effective. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a new event‐triggered robust control system for robot manipulators, based on two event‐triggered mechanisms (ETMs), which lead to intermittent communication not only for the sensor‐to‐controller channel but also for the controller‐to‐actuator channel. The proposed control system enjoys the advantage of requiring fewer resources of data communication and less signal updating of the control actuators. The control performance of the proposed control system is analyzed based on the Lyapunov approach. Furthermore, the absence of the Zeno behavior in the triggering sequence is proved rigorously. Finally, experiments are carried out on a two‐link robot manipulator system to support the theoretical results. [ABSTRACT FROM AUTHOR]

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

In this article, we address the topics of fault reconstruction and robust fault‐tolerant tracking control for periodic piecewise polynomial uncertain systems with time‐varying delays, faults and external disturbances. In detail, the periodic piecewise polynomial learning observer system is configured with the intent to concurrently reconstruct the faults and immeasurable states of the system that is being considered. Precisely, the key intent of this study centers around in devising a robust fault‐tolerant tracking control to achieve the tracking performance of the system as intended with mixed H∞$$ {H}_{\infty } $$ and passivity performance. To this end, a robust fault‐tolerant tracking control is designed by incorporating the reconstructed faults and error feedback, which aids in vouching for the outcomes that were desired. Besides, by building the periodic piecewise polynomial Lyapunov–Krasovskii functional and making use of the matrix polynomial lemma and Wirtinger's inequality, certain adequate conditions are procured in the frame of linear matrix inequalities. Subsequently, the required gain values for the devised controller and the configured observer are found by using the established linear matrix inequalities. Ultimately, in order to attest the controller's intrinsic potential and relevance, numerical example along with the simulation results is supplied. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the problem of adaptive robust safety‐critical control (ARSCC) for switched systems, where the safety of subsystems is not necessary. A novel ARSCC framework and an executable algorithm are presented to solve the ARSCC problem by finding a switching signal and controllers of subsystems. To estimate uncertainties, a novel switched piecewise‐constant adaptive law is presented, which guarantees a pre‐computable estimation error boundary. A single barrier function (SBF) method is proposed to ensure safety of switched systems with uncertainties, where the safety of subsystems is satisfied only in some subregion of a given safe set, instead of the whole safe set. Based on the SBF method, a novel state‐dependent switching law possessing different dwell times for different subsystems, is established to orchestrate the switching among potentially unsafe subsystems. As a special case of the SBF method, a common barrier function method is presented to achieve safety of switched systems with uncertainties under switching signals with given dwell times. In addition, some sufficient conditions are derived to obtain safety and asymptotic stability for switched systems with uncertainties by combining SBF and single Lyapunov function. Finally, a switched RLC circuit system is given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates the merging control for underactuated connected and automated vehicle (CAV) systems with mismatched uncertainty. The objective is to guarantee the safe and prescribed dynamic performance of the system during multilane merging. For CAV multidimensional motions, a strongly coupled vehicle dynamics with time‐varying uncertainties is constructed. For the nominal system, the control objectives are designed as system constraints, with equality constraints guaranteeing merging and time‐varying inequality constraints guaranteeing dynamic performance bounds. Based on the diffeomorphism method and bounded constraint, the system constraints are reconstructed to a unified representation. Combining system constraints and underactuated structure, the nominal controller is derived based on constraint‐following. For the uncertain system, the uncertainty is decomposed into matched and mismatched portions by orthogonal decomposition. The adaptive robust controller is designed based only on matched uncertainty. Through Lyapunov minimax analysis, the control renders the errors uniformly bounded and uniformly ultimately bounded. Simulation results show that merging and platooning are effectively achieved with the proposed control. The system has excellent transient and steady state performance even in the presence of time‐varying uncertainties. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self‐organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time‐varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed‐loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme. [ABSTRACT FROM AUTHOR]

To address the issue of poor robustness in the model predictive control of grid‐connected inverters due to disturbances in load model parameters, this article developed an ultra‐local model robust predictive controller based on Linear Matrix Inequality (LMI). First, to avoid dependence on model parameters, an ultra‐local model of the system was constructed. Second, in the process of setting the state variable compensation gain, to simplify the complex computational steps of the traditional Lyapunov algorithm, the task of finding the gain that satisfies the Lyapunov stability condition was ingeniously transformed into an optimization problem of solving constrained LMI. Third, the optimized state variables are utilized to design a new cost function predictive model, thereby enhancing the precision of system control. Finally, the effectiveness of the proposed approach has been validated through simulations and experiments. [ABSTRACT FROM AUTHOR]

The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply the exact feedback linearization to the agents. This transformation divides their dynamics into a linear observable part and a non-observable part. It is shown that consensus of the linearizable parts of the agents implies consensus of the entire multi-agent system. To achieve the consensus of the original system, the inverse transformation of the exact feedback linearization is applied. However, its application causes changes in the dynamics of the multi-agent system; a way to mitigate this effect is proposed. Two examples are presented to illustrate the efficiency of the proposed synchronization algorithm. These examples demonstrate that the synchronization error decreases faster when the proposed method is applied. This holds not only for the states constituting the linearizable dynamics but also for the hidden internal dynamics. [ABSTRACT FROM AUTHOR]

This paper presents the trajectory tracking control design and implementation of feedback linearization (FL) and robust feedback linearization (RFL), applicable to a robotic lawnmower with four mecanum driving wheels. The RFL control design additionally includes a robust control law. These two nonlinear control laws are developed to enable the controlled robotic lawnmower to accurately follow any specified trajectory. The simulation outcomes illustrate that the suggested control law based on RFL displays superior trajectory tracking accuracy and resilience compared to the FL control method in the case of a robotic lawnmower operating under demanding conditions. These conditions encompass environmental disturbances and uncertainties in modeling. The RFL control method also exhibits lower energy consumption compared to the FL control method. Finally, using the RFL controller derived from this study, the error in trajectory tracking in computer simulations and the actual mowing performance have demonstrated outstanding results. [ABSTRACT FROM AUTHOR]

In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques. [ABSTRACT FROM AUTHOR]

The Permanent Magnet Synchronous Motor (PMSM) drive system is extensively utilized in high-precision positioning applications that demand superior dynamic performance across various operating conditions. Given the non-linear characteristics of the PMSM, a neuroadaptive sensorless controller based on B-spline neural networks is proposed to determine the control signals necessary for achieving the desired performance. The proposed control technique considers the system's non-linearities and can be adapted to varying operating conditions, all while maintaining a low computational cost suitable for real-time operation. The introduced neuroadaptive controller is evaluated under conditions of uncertainty, and its performance is compared to that of a conventional PI controller optimized using the Whale Optimization Algorithm (WOA). The results demonstrate the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to improve cache placement for various rendering algorithms using caching techniques. The proposed method comprises two stages. The first stage computes an initial cache distribution based on shading points' geometric proximity. We present a view-guided method to cluster shading points based on their world-space positions and surface normals, while considering the camera view to avoid producing small clusters in the final image. The proposed method is more robust and easier to control than previous shading point clustering methods. After computing the shading functions at the initial cache locations, the second stage of our method utilizes the results to allocate additional caches to regions with shading discontinuities. To achieve this, a discontinuity map is created to identify these regions and used to insert new caches based on importance sampling. We integrate the proposed method into several cache-based algorithms, including irradiance caching, importance caching, and ambient occlusion. Extensive experiments show that our method outperforms other cache distributions, producing better results both numerically and visually. [ABSTRACT FROM AUTHOR]

To ensure the human–machine co-driving vehicle path-tracking accuracy, as well as to diminish the effects of vehicle underactuated characteristics and undesirable driver operation, a parallel shared control framework is proposed in this paper. By structuring a rational driving weights allocation module, the framework enables cooperative driving between the driver and automated system. First, for the front steering vehicle, a robust path-tracking controller is proposed using the constraint-following approach. The system uncertainty is decomposed into matched and non-matched uncertainties. As the non-matched portions are orthogonal to the constraint-following geometric space, they will not affect control performance. Therefore, the constraint-following control (CFC) is designed to handle the path-tracking equality constraint, initial state deviations, and matched uncertainties. Second, a fuzzy rule-based driving weights allocation module is proposed. According to the lateral deviation and the relative accuracy of the steering angle, the allocation module adjusts the driving weight of the driver and automated system to improve the vehicle condition. Finally, the simulation results show that the framework can effectively reduce the emergence caused by the driver or automated system, and significantly improve the path-tracking performance. Additionally, as the controller exhibits sufficient robustness, it can handle multiple sources of uncertainty in vehicle systems. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an adaptive nonlinear control scheme to ensure the stability of a boost converter in a DC microgrid that supplies a constant power load (CPL) and a resistive load. The proposed controller comprises a passivity‐based control (PBC) and a Nonlinear Disturbance Observer‐Based Robust Control (NDOBRC). The PBC guarantees the overall stability of the proposed controller, whereas the NDOBRC compensates for various sources of disturbances and uncertainties in the system, such as CPL power. The large‐signal stability of the PBC is established using the Lyapunov function. Simulation and experimental results demonstrate the effectiveness of the proposed controller in terms of superior performance compared to the PI method. [ABSTRACT FROM AUTHOR]

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction. [ABSTRACT FROM AUTHOR]

Enhanced error monitoring, as indexed by increased amplitude of the error‐related negativity (ERN) event‐related potential (ERP) component, has been suggested to reflect a vulnerability neuro‐marker of anxiety disorders. Another error‐related ERP component is the error positivity (Pe), which reflects late‐stage error processing. The associations between heightened ERN and Pe amplitudes and anxiety levels in the nonclinical population have been inconsistent. In this preregistered study, we examined the association between anxiety, ERN, and Pe, using different analytical methods (mass‐univariate analyses, MUAs and conventional analyses), self‐reported anxiety scales (STAI and STICSA), and trial numbers (all correct trials and equal numbers of correct and error trials). In a sample of 82 healthy adults, both conventional and MUAs demonstrated a robust enhancement of the ERN and Pe to errors relative to the correct‐response ERPs. However, the mass‐univariate approach additionally unveiled a wider array of electrodes and a longer effect duration for this error enhancement. Across the analytic methods, the results showed a lack of consistent correlation between trait anxiety and error‐related ERPs. Findings were not modulated by trial numbers, analyses, or anxiety scales. The present results suggest a lack of enhancement of error monitoring by anxious traits in individuals with subclinical anxiety and those with clinical anxiety but without a clinical diagnosis. Importantly, the absence of such correlation questions the validity of the ERN as a neural marker for anxiety disorders. Future studies that investigate neuro‐markers of anxiety may explore alternative task designs and employ robust statistics to provide a more comprehensive understanding of anxiety vulnerability. Our research challenges the proposed use of the ERN as a biomarker to identify individuals at risk of developing anxiety disorders. We used robust statistics and controlled for several potential confounds, providing strong evidence against an association between ERN amplitude and trait anxiety levels in a nonclinical population. [ABSTRACT FROM AUTHOR]

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of an electromagnetic actuator used in a switching valve are influenced by self-induced effects, resulting in instability in its parameters when subjected to changes in working frequency, load, and other conditions. To quantitatively analyze the impact of variable working conditions, this study establishes an analytical expression for periodic signals through Fourier series expansion. It subsequently solves for transient current, yielding a new model for the response of the electromagnetic actuator under periodic signal excitation. Simulations and analyses are conducted to investigate the variation in action parameters of the electromagnetic actuator in the realm of compressor gas regulation when working conditions, such as rotation speed and load force, are altered. The results indicate that dynamic characteristics of the actuator change within the range of 8% to 35%, significantly affecting the gas regulation efficiency of the compressor. To mitigate this effect, a backstepping adaptive high frequency controller (BAHF) is proposed. Comparative assessments with the backstepping adaptive sliding model controller (BASM) and backstepping adaptive high gain controller (BAHG) reveal that the BAHF exhibits superior overall performance. An experimental platform is constructed to validate both the model's accuracy and the controller's effectiveness. The error between model simulations and experiments does not exceed 7.5%, confirming the accuracy of the model. The proposed BAHF reduces the fluctuation of compression and exhaust phase angles in the compressor by 21.6% and 6.6%, respectively, under variable working conditions. This reduction effectively suppresses the influence of working condition disturbances, yielding positive outcomes. [ABSTRACT FROM AUTHOR]

Childhood adversity (CA) has a substantial correlation with mental health problems. Keeping a healthy lifestyle is essential for mental health interventions; it is unclear, however, how healthy lifestyle affect the relationship between CA and persistent mental health problems. This longitudinal study (n = 1112, 54.5 % male) collected the data on CA (measured through three dimensions: threat, deprivation and unpredictability), mental health problems, and lifestyle factors. Group-based multi-trajectory modeling (GBMTM) was utilized to estimate trajectories for three mental health problems (i.e., depression, ADHD and overanxiety). Close friendships, regular physical activity, appropriate sleep duration, shorter screen time, and healthy eating were combined to establish a healthy lifestyle score (which ranges from 0 to 5). Higher scores indicated a healthier lifestyle. Three trajectories of mental health problems were identified: persistently low risk (24.9 %), persistently medium-high risk (50.0 %), and persistently high risk (25.1 %). Multinomial logistic regression showed that high adversity (high-threat: β = 2.01, P < 0.001; high-deprivation: β = 1.03, P < 0.001; high-unpredictability: β = 0.83, P = 0.001; high-overall adversity: β = 1.64, P < 0.001) resulted in a persistently high risk of mental health problems; these outcomes were maintained after robust control for covariates. Further lifestyle stratification, null associations were observed among children with a healthy lifestyle, irrespective of their gender; however, after controlling for covariates, the above associations remained relatively stable only among boys. The generalizability of our findings is restricted by 1) limited racial diversity and 2) missing data. This finding underscores the benefits of promoting a healthy lifestyle in children to prevent persistent mental health problems caused by CA. • We identified three trajectories of multiple mental health problems in children. • Children exposed to high adversity are more likely to be at persistently high risk for mental problems. • A healthy lifestyle can help buffer the risk of a persistently high risk of mental health problems caused by adversity [ABSTRACT FROM AUTHOR]

Robust control of uncertain fully actuated systems (FASs) with nonlinear uncertainties and perturbed input matrices is considered. Motivated by the recent work on this issue, two novel robust controllers are further developed for two cases under different assumptions. For both cases, the assumption on the perturbation input matrix in the previous work is relaxed to a significant extent, which allows many typical perturbation input matrices, such as constant ones, to be handled, while the previous method cannot. Moreover, for the first case, the assumption on the system uncertainty is further relaxed, and the states of the closed-loop system are globally bounded and converge into an arbitrarily small spherical domain centered at the origin. For the second case, with another requirement on the system nonlinearity imposed, the global exponential stability of the closed-loop system is achieved. The successful application in an electromechanical system verifies the effectiveness of the method. • The nonlinear uncertainty and the uncertainty in the input matrix are considered. • A controller makes the states converge globally into an arbitrarily small region. • Another controller achieves the global exponential stability. • The assumptions on the perturbed input matrix in the previous work are relaxed. • A successful application in the control of electromechanical systems. [ABSTRACT FROM AUTHOR]

In multi-mass systems, torsional vibration is a common and annoying phenomenon. Effective vibration suppression and robustness to wide-range parameter variations are essential for a sound motion system. However, most control methods focus on the primary resonance mode, and the high-order resonance modes are not actively treated in the control design, resulting in the control bandwidth not being high enough and limiting the control performance. This paper proposes a novel two-stage design scheme to realize a wideband control to improve control performance. First, a hybrid uncertainty model is tailored for multi-mass systems, which uses an equivalent and uncertain spring constant to describe the variation of the primary mode and a dynamic uncertainty to cover the other resonance modes. This hybrid model strikes a better balance between the model conservatism and the feasibility of a less conservative design. Then, the passivity of the parameter uncertainty is utilized to conduct a phase compensation on the nominal system. After the phase compensation, all uncertainties are converted into norm-bounded ones, and the robust performance design is carried out. This method is applied to vehicle drivetrain benches, and its superiority is validated through simulation comparisons and experiments on two typical types of drivetrain benches. • Hybrid model for multi-mass systems characterizes primary resonance and other modes. • Two-stage design method with phase-shaping achieves high performance robust control. • The method is applied to drivetrain benches, outperforming the conventional methods. [ABSTRACT FROM AUTHOR]

In control theory, typically a nominal model is assumed based on which an optimal control is designed and then applied to an actual (true) system. This gives rise to the problem of performance loss because of the mismatch between the true and assumed models. A robustness problem in this context is to show that the error because of the mismatch between a true and an assumed model decreases to zero as the assumed model approaches the true model. We study this problem when the state dynamics of the system are governed by controlled diffusion processes. In particular, we discuss continuity and robustness properties of finite and infinite horizon α-discounted/ergodic optimal control problems for a general class of nondegenerate controlled diffusion processes as well as for optimal control up to an exit time. Under a general set of assumptions and a convergence criterion on the models, we first establish that the optimal value of the approximate model converges to the optimal value of the true model. We then establish that the error because of the mismatch that occurs by application of a control policy, designed for an incorrectly estimated model, to a true model decreases to zero as the incorrect model approaches the true model. We see that, compared with related results in the discrete-time setup, the continuous-time theory lets us utilize the strong regularity properties of solutions to optimality (Hamilton–Jacobi–Bellman) equations, via the theory of uniformly elliptic partial differential equations, to arrive at strong continuity and robustness properties. Funding: The research of S. Yüksel was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). [ABSTRACT FROM AUTHOR]

By fully taking the effects of data injection attacks, nonlinear disturbances and limited communication resources, the stability of Lurie industrial cyber physical systems with the time‐varying delays is investigated. The two channels event‐triggered dynamic robust H∞$$ {H}_{\infty } $$ control (ERHIC) strategy is proposed for achieving the stability of systems. On one hand, a dynamic output feedback controller is designed to ensure the stable operation of the system under attacks. On other hand, the measurement channel and the control channel (two channels) event‐triggered mechanisms are designed to improve the utilization rate of system resources. The simulation results with the size of capacitor voltage vC$$ {v}_C $$ and inductor current iL$$ {i}_L $$ under data injection attacks as the object verity the correctness and the effectiveness of the proposed two channels event‐triggered robust H‐infinity control method and the feasibility of the designed dynamic output feedback controller are verified by the simulation results. [ABSTRACT FROM AUTHOR]

A robust predictive fault‐tolerance control method integrating dynamic output feedback is proposed for discrete industrial processes with unmeasurable states, actuator faults, uncertainties, and external disturbances. First, a new iterative error model is developed, which extends the output tracking error to increase the freedom of controller regulation and ensure the tracking performance of the system. Second, a robust dynamic output feedback predictive fault‐tolerant controller is designed based on the preceding model. According to the Lyapunov stability theory, the robust stability criterion of the system is performed in the case of actuator faults, and the stability conditions of the system in the form of linear matrix inequality are given. The real‐time optimal control law gains are obtained by solving the stability conditions online. This method can effectively suppress the effects of actuator faults, uncertainties and external disturbances on the system with unmeasurable states, which can guarantee the tracking performance and stability of the system. The effectiveness of the proposed method is finally verified by using an injection molding process as an example. [ABSTRACT FROM AUTHOR]

In this paper, a solution for sliding mode controller gain adaptation is proposed. As is well known, tuning the gains in such a controller is a challenging task since it depends on the bounds of uncertainties and disturbances, which are unknown in most cases. An overestimation of the gains may lead to significant activity of the control input, causing high demands on the actuators and producing the chattering phenomenon. The amplitude of this phenomenon is proportional to the amplitude of the control and can be mitigated by reducing the control gain to the minimal value ensuring convergence. Thus, the gain can be adapted to follow a sufficient value capable of rejecting parameter uncertainties and disturbances. The adaptation methodology presented in this paper is based on PID control scheme where a novel thermodynamic approach to analyze stability in the sense of Lyapunov is presented confirming a class of asymptotic stability. Moreover, some simulation results are provided to validate the effectiveness and reliability of the proposed control schemes. The theoretical significance of this work lies in its potential to establish a foundation for designing a general control equation that bridges the gap between discontinuous and continuous control methods. Moreover, this hybrid control approach holds practical value for control application designers in various industries, including processes that demand chattering-free operation (where the widely used PID control is beneficial), and in processes that require high robustness (where the sliding mode control finds its strength). [ABSTRACT FROM AUTHOR]

This research examines the effects of information disclosure ratings (IDR) on firm-specific stock price crash risk. We present evidence that there is a statistically significant negative relationship between stock price crash risk and IDR. Specifically, effective information disclosure attracts greater investor attention and leads to more liquidity, which mitigates the stock price crash risk. Our findings remain robust after controlling for relevant variables and addressing the issue of endogeneity. This research proves that high IDR mitigates the stock price crash risk by eliciting market reaction, which not only introduces a novel perspective for investors in their analysis of corporate risks but also offers valuable directions for policy formulation to guide the market. [ABSTRACT FROM AUTHOR]

A principal challenge facing the control of floating offshore wind turbines (FOWTs) is the problem of instability, or "negative damping," when using blade pitch feedback to control generator speed. This closed‐loop instability can be attributed to non‐minimum phase zeros in the transfer function from blade pitch to generator speed. Standard approaches to improving stability and performance include robust tuning of control gains and introducing multiple feedback loops to respond to platform motion. Combining these approaches is nontrivial because multiple control loops complicate the impact of coupling in the system dynamics. The single‐loop approach to analyzing stability robustness neglects inter‐loop coupling, while a simplistic multi‐loop approach is highly sensitive to dimensional scaling and overestimates the robustness of the single‐loop controller. This work proposes a sensitivity representation that separates some of the natural FOWT dynamic coupling into a parallel feedback loop in the sensitivity function loop to address both of these concerns. The modified robustness measure is used with a simplified linear FOWT model to optimize scheduled multi‐loop control parameters in an automated tuning procedure. This controller is implemented for the 10‐MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) and compared against conventional single‐ and multi‐loop controllers tuned using frequency‐domain analysis and high‐fidelity OpenFAST simulations. The multi‐loop robust controller shows the highest overall performance in generator speed regulation and tower load reduction, though consideration of power quality, actuator usage, and other structural loading leads to additional trade‐offs. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of robust coordination control based on interval observers for multi‐agent systems in the presence of input saturation and disturbances. Firstly, in the scenario where the system state is not directly measurable, two types of interval observers are, respectively, constructed by resorting to the upper and lower bounds on external perturbances as well as the output information. Secondly, a parametric Lyapunov equation‐based low‐gain feedback control method is proposed to guarantee semiglobal bounded consensus. In contrast to the conventional approach based on parametric algebraic Riccati equations, the proposed method offers the advantage of allowing the parameters to be determined in advance. Finally, a simulation example and a practical electrical circuit model are conducted to verify the theoretical results. [ABSTRACT FROM AUTHOR]

Due to the flexible components, damping, underactuation, and uncertainties, the control for flexible transmission system (FTS) becomes very difficult. A constraint‐following servo control is designed to address this issue. The damping and applied load of system are integrated into the constraints, so that the desired performance could be achieved by accomplishing the constraints with Udwadia–Kalaba (U–K) equation. The system is divided into two subsystems by introducing a designed fictitious control to the load end, and the controllers of subsystems are designed, respectively. Since the accurate value of uncertainty is usually unknown, a corresponding leakage type of adaptive law is designed to estimate the size of its boundary without requiring the uncertainty itself. The effectiveness and superiority of proposed controller are verified by simulations. [ABSTRACT FROM AUTHOR]

In this paper, a novel controller designed for robust tracking control of a flexible‐link manipulator operating in the presence of parameter uncertainties and external disturbances within the joint space is introduced. The proposed controller employs an adaptive sliding mode control approach, incorporating an improved barrier function, to ensure that trajectory errors remain within predefined performance bounds. This design enhances the tracking performance without overestimating control‐switching gains. Additionally, a fixed‐time adaptive sliding mode control, featuring a rapid nonsingular terminal sliding mode variable, is introduced to expedite the convergence rate of the system state during the initial stages. The efficacy of the proposed control scheme is established through the Lyapunov method, demonstrating finite‐time convergence of the trajectory error to a specified neighborhood of zero. Experimental validation on a flexible‐link system supports the effectiveness and advantages of the proposed control strategy, as evidenced via comparisons with two existing adaptive control schemes. [ABSTRACT FROM AUTHOR]

Unreliable resources in conventional power systems have created new challenges for the users of these systems. These sources of production, including wind and solar sources, with a significant share in the power generation of many power systems, cause problems in the safe and stable operation of power systems due to uncertainty. Therefore, it is very important to provide a method or model for safe and stable operation of power systems. The main innovation of this paper is to present a new SCUC model to maximize the flexibility of the system along with reducing the operating cost in the form of a security constrained multi-level robust optimization problem in the presence of wind uncertainty sources. Also, a set of demand response programs and rapid response hybrid cycle units are used as sources of flexibility. Accordingly, the tolerable range of uncertainty and system slope reservation is developed with a low-cost combination and with the commitment to flexible covering resources. The desired optimization problem has been solved with the help of the NCCG algorithm and MILP method. The efficiency and reliability of this proposed model have been simulated and evaluated in IEEE 6-bus and 118-bus standard power systems, and the simulation results confirm the technical and economic efficiency of this model. [ABSTRACT FROM AUTHOR]

This paper is devoted to the iterative interval estimation for nonlinear discrete-time systems. To reconstruct the system state, a sequence of iterative observers is established based on the iterative disturbance estimation and measured output. By means of the Lipschitz condition and H ∞ technique, sufficient conditions are built by the Lyapunov function method to make observation errors convergent. Resorting to the zonotope-based reachability analysis, the reachable set of nonlinear terms and observation errors are analyzed such that the state interval can be supplied. The presented approach is validated by a simulation comparison. [ABSTRACT FROM AUTHOR]

Risk and uncertainty are intrinsic characteristics of natural resources that must be taken into account in their management. Harvest control rules (HCR) used to be the central management tool to control stock fisheries in an uncertain context. A typical HCR determines fishing mortality as a linear relationship of the biomass binding only when the biomass is above a critical risk value. Choosing the linear relationship and the risk value is a complex task when there is uncertainty because it requires a high level of data and an in-deep knowledge of the stock. This paper fully characterizes robust HCRs that explicitly include scientific uncertainty using the robust control theory approach. Our theoretical findings show that under uncertainty: i) Constant HCRs are not robust; ii) Robust HCRs show a steeper linear relationship between fishing mortality and biomass and a higher value of biomass to be consider at risk than non-robust HCRs. From the implementation viewpoint, we assume a three-sigma rule and show that robustness is achieved by selecting a fishingmortality such that its deviation fromthe fishingmortality target is twice the deviation of the biomass from the biomass target, and the critical value of the biomass (the point below which fishing should cease, or become as close to zero as possible) is half of the biomass associated with the maximum sustainable yield when this is the target. [ABSTRACT FROM AUTHOR]

With the continuation of the infectious disease, pathogens mutate and interact with the resident pathogen. In this paper, we develop an SIR model that considers the evolution of viral virulence under the regulation of different defense strengths. By using infectious disease dynamics and evolutionary adaptive dynamics, we obtain the evolutionary criteria allowing for evolutionary branching and continuously stable strategy across varying intensities of prevention and control measures. Our study finds that robust prevention and control measures can contribute to a rapid evolutionary trend toward higher virulence. We perform a critical function analysis to reveal the evolutionary consequences of trade-offs of arbitrary shape and analyze a diverse range of evolutionary dynamics. Furthermore, we investigate the coexistence of two different strains and construct an evolutionary model to obtain their invasion fitness functions and the evolutionarily singular coalition of the dimorphism. We contemplate the presence of secondary branching on a significantly extended evolutionary time scale. Last, we conduct the numerical simulations to prove our theoretical findings. This reveals the law that the evolutionary state of pathogens is affected by the intensity of prevention and control strategies. The results and methods can be used as references for studying the effects of other factors on virus evolution. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates a multiplayer nonzero‐sum differential game (MNDG) with switching topology and input delay. The Hamilton–Jacobi–Isaacs (HJI) equation is used to derive the optimal control strategies. To solve the issue of switching topology, the control strategies are decoupled with the communication topology. Then, the stability of the system can be achieved regardless of changes in communication topology with the designed control strategies. This does not require a restriction on the dwell time, making the system stability conditions more relaxed. The Lyapunov–Krasovskii functional (LKF) is constructed to prove the stability of the system in the presence of time‐varying input delay. To cope with the real‐world situation, the input delay is considered to be disturbed by a time‐varying perturbation in this article. With these treatments, the system is more in line with real‐world applications. Simulation examples are given to validate the efficiency of the presented methods. [ABSTRACT FROM AUTHOR]

We examine the nexus between financial inclusion and energy poverty. Analysing data for 27 energy-poor countries in the Sub-Saharan Africa region over 2004–2021, we employ sequential (two-stage), panel-corrected standard error (PCSE) and two-step dynamic system GMM (generalized method of moments) regression models, and control for endogeneity, CSD, slope heterogeneity as well as stationarity and cointegration patterns of the variables. Our empirical results show that financial inclusion significantly reduces energy poverty in the selected energy-poor countries. The study also finds a positive significant association between energy access and GDP per capita, while oil price and energy intensity are inversely associated with energy access. The results are robust to different control variables, estimation methods and subsamples. These findings have strong policy implications for energy-poor countries and point to the need for appropriate policies to promote financial inclusion for reducing energy poverty. [ABSTRACT FROM AUTHOR]

ABSTRACT The cooperative localization technology of multi‐AUV has been a research hotspot in the area of underwater localization in recent years. Aiming at the situation that the localization accuracy is reduced and diverging due to uncertain information such as outliers and unknown time‐varying noise in the measurement information in the dynamic topology environment, a new measurement filtering module is designed by using factor graphs. First, the K‐means algorithm is introduced based on the cooperative localization algorithm based on factor graphs in the dynamic environment, and an outlier filtering module is designed to filter measurement outliers. After outlier correction, the EM algorithm is introduced to resolve the issue of unknown measurement noise variance caused by unknown time‐varying noise and design an adaptive filtering module. Finally, to address the issue of degraded real‐time performance due to a large number of system communication nodes and diverse quality, the paper introduces the CRLB algorithm, distance evaluation factor, and measurement smoothing factor to design a node optimization module. Finally, these modules are integrated into the cooperative localization system through factor graphs. Through experimental analysis, the proposed algorithm effectively reduces outliers and unknown time‐varying noise in measurement information, as well as the impact of a large number of system nodes on localization accuracy and real‐time performance. In addition, the proposed algorithm also shows strong robustness in the face of extreme underwater environments. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

Cyanogenic glycosides are naturally occurring compounds found in numerous plant species, which can release toxic hydrogen cyanide upon hydrolysis. The quantification of cyanogenic glycosides is essential for assessing their potential toxicity and health risks associated with their consumption. Liquid chromatographic techniques coupled with various detectors have been widely used for the quantification of cyanogenic glycosides. In this review, we discuss recent advances in chromatographic quantification methods for cyanogenic glycosides, including the development of new stationary phases, innovative sample preparation methods, and the use of mass spectrometry. We also highlight the combination of chromatographic separation with mass spectrometric detection for the identification and quantification of specific cyanogenic glycosides and their metabolites in complex sample matrices. Lastly, we discuss the current challenges and future perspectives in the development of reliable reference standards, optimization of sample preparation methods, and establishment of robust quality control procedures. This review aims to provide an overview of recent advances in chromatographic quantification methods for cyanogenic glycosides and their applications in various matrices, including food products, biological fluids, and environmental samples. [ABSTRACT FROM AUTHOR]

A plentiful number of facial expressions is the basis of natural human–robot interaction for high-fidelity humanoid robots. The facial expression imitation of humanoid robots involves the transmission of human facial expression data to servos situated within the robot's head. These data drive the servos to manipulate the skin, thereby enabling the robot to exhibit various facial expressions. However, since the mechanical transmission rate cannot keep up with the data processing rate, humanoid robots often suffer from jitters in the imitation. We conducted a thorough analysis of the transmitted facial expression sequence data and discovered that they are extremely redundant. Therefore, we designed a two-stage strategy for humanoid robots based on facial keyframe detection and facial keypoint detection to achieve more natural and smooth expression imitation. We first built a facial keyframe detection model based on ResNet-50, combined with optical flow estimation, which can identify key expression frames in the sequence. Then, a facial keypoint detection model is used on the keyframes to obtain the facial keypoint coordinates. Based on the coordinates, the cubic spline interpolation method is used to obtain the motion trajectory parameters of the servos, thus realizing the robust control of the humanoid robot's facial expression. Experiments show that, unlike before where the robot's imitation would stutter at frame rates above 25 fps, our strategy allows the robot to maintain good facial expression imitation similarity (cosine similarity of 0.7226), even at higher frame rates. [ABSTRACT FROM AUTHOR]

This paper introduces a novel leader–follower formation control strategy for autonomous vehicles, aimed at achieving precise trajectory tracking in uncertain environments. The approach is based on a graph guidance law that calculates the desired yaw angles and velocities for follower vehicles using the leader's reference trajectory, improving system stability and predictability. A key innovation is the development of a Neural Adaptive Prescribed Performance Controller (NA-PPC), which incorporates a Radial Basis Function Neural Network (RBFNN) to approximate nonlinear system dynamics and enhances disturbance estimation accuracy. The proposed method enables high-precision trajectory tracking and formation maintenance under random disturbances, which are vital for autonomous vehicle logistics and detection technologies. Leveraging a graph-based guidance law reduces control complexity and improves robustness against external disturbances. The inclusion of second-order filters and adaptive RBFNNs further enhances nonlinear error handling, improving control performance, stability, and accuracy. The integration of guidance laws, leader–follower control strategies, backstepping techniques, and RBFNNs creates a robust formation control system capable of maintaining performance under dynamic conditions. Comprehensive computer simulations validate the effectiveness of this controller, highlighting its potential to advance autonomous vehicle formation control. [ABSTRACT FROM AUTHOR]