Search

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]

Motivated by the fully actuated system (FAS) approach, this paper proposes a direct robust control method for a type of uncertain multi‐order sub‐fully actuated systems (MOSFASs). Unlike numerous previous results, this paper broadens to a more general multi‐order model and the more challenging sub‐fully actuated case with the presence of singular problems. Firstly, a general uncertain MOSFAS model is presented, where the uncertainty merely needs to satisfy a common assumption. Secondly, a direct robust control method is proposed, giving a robust controller which only needs to satisfy very loose and basic requirements to ensure that the states ultimately converge into an arbitrarily small region. Simultaneously, by constraining the initial values of the states, it is perfectly guaranteed that the states always remain in a "safe" zone free of singular points, thus preserving the realizability of the controller. Finally, under certain conditions, a less conservative robust control scheme is introduced, which can relax the constraint of initial values easily. A simulation for a coarse‐fine angle system demonstrates the validity and value of the proposed robust control method. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization problem of uncertain switched nonlinear systems. Uncertainty and saturated impulses are first considered together in switched nonlinear systems. It appears that the considered systems are robustly locally exponentially stable under the fast average dwell time method and the designed saturated impulsive controller even though subsystems are unstable. At the same time, our conclusions cover some previous results. A step‐by‐step strategy for optimizing multiple objectives is designed to estimate the basin of attraction of the systems as large as possible. At last, the efficacy of our conclusions is illustrated by two given examples. [ABSTRACT FROM AUTHOR]

The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

In this article, we deal with finite‐time optimal control of the Euler–Lagrange (E–L) multi‐body mechanism under: uncertain its kinematics and dynamics, displacement blocking of the damaged joint caused by the actuator failure, undesirable forces/torques exerted on the end‐effector and unknown friction forces originating from joints directly driven by the actuators, as well as completely unstructured forces arising from the kinematic singularities appearing on the mechanism trajectory. Based on the suitably defined task space non‐singular terminal sliding manifold and the Lyapunov stability theory, we propose a class of new fault tolerant estimated generalized Jacobian controllers, which seem to be effective in reducing (minimizing) the joint velocity jumps as well as in counteracting the unstructured forces/torques. On account of the fact, that the multi‐body mechanism is a redundant one, a useful criterion function (energy consumption +$$ + $$ manipulability measure) has been utilized in our controller to temporarily optimally track a desired trajectory. The proposed controllers' performance is demonstrated by computer simulations conducted on a redundant manipulator in conditions of unexpected actuator failure and the corresponding joint lock. Additionally, numerical comparisons are also provided with other representative controllers, found in the literature. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of robust adaptive safety control of a class of nonlinear systems with parametric uncertainties. Two novel definitions, that is, robust adaptive control Lyapunov function (RACLF) and robust adaptive control barrier function (RACBF) are introduced in this article. Also, an estimation of unknown parameters is obtained based on multiple identification models, state predictors and a logic switching mechanism. The switching mechanism plays a vital role in enhancing the transient properties of nonlinear systems. Meanwhile, a safe motion framework for differential robots is provided based on the existence of parametric uncertainties in RACLF and RACBF, and a safe and stable controller is obtained by solving quadratic programming. Finally, simulation results of differential robot capable of safe motion in space are presented to verify the validity of the design method. [ABSTRACT FROM AUTHOR]

Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously. [ABSTRACT FROM AUTHOR]

The problem of fixed‐time formation control for a class of second‐order nonlinear multi‐agent systems is studied. For a class of such systems, a control algorithm is proposed to maintain the connections among the agents while avoiding collisions. Furthermore, a radial basis function neural network is used in the design to precisely approximate the nonlinear function for the nonlinear terms in the model. Then, a dynamic sliding mode control method is proposed to suppress the chattering phenomenon that may arise due to the sliding mode control. A sufficient condition for the system to achieve fixed‐time formation is obtained by using different methods, such as Lyapunov stability. Finally, the effectiveness of the proposed algorithm is verified by example. Simulation experiments reveal that the proposed method has faster error convergence and better robust control than conventional algorithms. [ABSTRACT FROM AUTHOR]

This article investigates the robust consensus problem for nonlinear multiagent systems against time‐varying false data injection attacks on actuators and sensors. First, the root‐mean‐square (RMS) theory is used to extend the assumption of the slow‐varying or constant attack signals to the case of time‐varying attack signals. Second, a novel distributed multivariate observer (DMO) is designed to estimate the followers' system states and the time‐varying attack signals on actuators and sensors. With the help of the outputs of DMO, a distributed robust consensus control arithmetic is proposed, which can compensate for actuator attacks and isolate sensor attacks so that exponential consensus and robust consensus are achieved. In particular, the robust performance of estimation errors and consensus errors is ensured by establishing the RMS gain index via linear matrix inequality, in which the zero initial conditions of estimation errors and consensus errors are not required. Finally, two simulation examples, including a network of four aircraft longitudinal dynamic systems, are given to verify the effectiveness of the proposed arithmetic. [ABSTRACT FROM AUTHOR]

This article is dedicated to event‐triggered anti‐disturbance control of networked control systems via a guaranteed cost triggering scheme. A distribution interconnected observer structure is built to estimate these unknown matched disturbances. A novel event triggered condition is constructed in terms of real‐time cost and averaged functions composed of observer states and the last successfully transmitted one. A composite control scheme is provided by an event‐triggered observer‐based state feedback controller and compensators for the matched disturbances. A sufficient condition in term of linear matrix inequality is supplied to ensure the asymptotic stability of the resultant closed‐loop system. A simulation is carried out to demonstrate the effectiveness of the proposed triggering control strategy. [ABSTRACT FROM AUTHOR]

This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum‐wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite‐time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer‐based finite‐time time‐varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite‐time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented. [ABSTRACT FROM AUTHOR]

This article considers the following problems for a parametric uncertain (affine) linear time-invariant system. The first problem is: Let a feedback gain matrix be designed such that the states of the closed loop system satisfy the specified transient performance bounds. Then, compute a ball in the uncertain parameter space such that for all parameter perturbations within it, the state response continues to satisfy the same transient performance bounds. The second problem is on the synthesis of a robust static state feedback control, which i) minimises the norm of the gain matrix, ii) maximises the radius of the ball in the uncertain parameter space, and iii) ensures achieving specified transient performance in the closed loop. For this, a sub-optimal linear matrix inequality optimisation is formulated. The developed results are demonstrated with numerical examples. It also highlights how the proposed methodology can be applied to design robust static state feedback control for a polytopic uncertain system. [ABSTRACT FROM AUTHOR]

This paper presents a modal analysis‐based impact analysis of the change in system inertia due to the integration of the large‐scale doubly fed induction generator (DFIG)‐based wind turbine system (WTS) on the power system damping in inter‐area oscillation modes. The impact of replacement of a synchronous generator by WTS of same MVA capacity on system dynamics such as emergence of new critical modes of inter‐area oscillations affecting system stability is studied. Further, a double‐channel H∞ scheme‐based wide‐area damping controller (WADC) is proposed for WTS, which provides sufficient damping of prominent oscillation modes and enhanced stability margin to alleviate the negative impact of reduction in system inertia. The comparative assessment of the proposed controller with a PSS‐based WADC on a modified New England 10‐machine system validates the performance and is found to be more effective in mitigating inter‐area oscillation in the system. The real‐time feasibility of the proposed double‐channel H∞ WADC is evaluated on the Real‐Time Digital Simulator (RTDS) platform. The simulation results show that the proposed WADC can successfully mitigate various inter‐area modes simultaneously. Furthermore, it provides robust performance for a variety of contingencies, time delays in feedback signals, and uncertainties associated with different operating scenarios of WTS. [ABSTRACT FROM AUTHOR]

Summary: The unstable operation of a direct‐drive permanent magnet synchronous generator (DPMSG) is exacerbated by periodic oscillation, having a significant impact on the safe and stable functioning of wind energy generation systems. This paper proposes an approximate solution method for analyzing the periodic oscillation of the Hopf bifurcation and a method for suppressing its bifurcation through H∞ robust control. Firstly, a three‐dimensional nonlinear dynamic model of the DPMSG is constructed. Secondly, the Hopf bifurcation of the system with changes in internal parameters is solved using the time domain and frequency domain methods, and the harmonic balancing method is used to approximate the periodic solution at the point of Hopf bifurcation. The order of the Hopf bifurcation neighborhood is then decreased using the central manifold theorem. Finally, this study suggests a H∞ output feedback control employing bifurcation parameters. The Nyquist criteria are used for evaluating small signal stability and convergence speed of the response system in DPMSG before and after dimensionality reduction. The simulation results suggest that the proposed strategy helps to tackle the periodic solution expression and instability difficulties caused by Hopf bifurcation. This paper provides theoretical suggestions for the future reliable operation of new energy generation systems. [ABSTRACT FROM AUTHOR]

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

Macroscopic fundamental diagram (MFD)-based perimeter control has a potential to improve traffic throughput and relieve the overall congestion. In practice, not only the MFD but also the traffic demand suffers from a variety of inherent uncertainties. Hence, this paper aims to contrive a robust control technique to address the perimeter traffic flow control issue in the oversaturated region. To this end, an integral sliding mode control is proposed to drive the traffic state to the desirable condition. The developed strategy deals with practical issues such as finite time stability and range of uncertainty in both MFD and traffic demand. Theoretical analysis verifies that the developed integral sliding mode control approach can guarantee the finite time convergence of the traffic state to the desired one. The performance of the developed scheme is attested by considering various traffic scenarios with uncertain MFDs and traffic demand, in which a comparative performance study with the proportional and integral control method is conducted. It is indicated that the developed scheme can alleviate the congestion in the urban network and improve the throughput of the urban network. Practical Applications: The research presented in this article proposes a novel perimeter control technique designed to enhance traffic flow management in densely populated urban road networks, particularly during peak congestion times. By leveraging the macroscopic fundamental diagram (MFD) and integral sliding mode control theory, the method significantly boosts traffic throughput and alleviates the impact of congestion. This advancement can be seamlessly integrated into current urban traffic management systems, leading to more efficient and convenient urban commutes. Additionally, it addresses the rise in vehicle emissions due to heavy traffic, aiding in the reduction of pollution and supporting sustainable development initiatives. The technique also curtails vehicle fuel consumption and cuts down on costs associated with congestion-related delays, thereby fostering resource optimization and economic growth within urban environments. Moreover, the method is designed to be resilient, taking into account the variability of traffic demand and ensuring the robustness of the traffic control strategy. Overall, the article demonstrates the practical application of advanced control technologies to address real-world traffic challenges, providing a scalable and robust solution adaptable to realistic situations with the uncertainties of urban traffic demand. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach. • We provide a systematic solution for addressing multi-model control systems (MMCS) for nonlinear dynamic systems. • Our approach stands out for its versatility, extending its applicability to a broad spectrum of nonlinear dynamical systems. • The solution is achieved through the utilization of s-gap metric concepts. • We tackle the optimization problem in a discrete form, introducing a novel clustering-based optimization approach. • The non-monotonic behavior of the s-gap metric is addressed by incorporating constraints into the problem formulation. [ABSTRACT FROM AUTHOR]

In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error. • Finite-time adaptive robust controller for Macro–micro composite platform. • Design and implementation of extended state observer for piezoelectric hysteresis. • Design of gain adjustment process to simplify calculation. [ABSTRACT FROM AUTHOR]

This article proposes a preview-based robust path-tracking control technique for maintaining lateral stability and tracking performance of autonomous vehicles, particularly in the presence of external disturbances and modeling uncertainties. First, a vehicle-road dynamic model with tire norm-bounded uncertainty is developed, which includes time-varying velocities and preview distances. The lateral and yaw dynamic characteristics are also analyzed in the frequency domain. Subsequently, an optimal preview model corresponding to sideslip-yaw rate states and longitudinal velocities is formulated employing a fuzzy logic model, and the sideslip angle is estimated using a sliding mode observer. Furthermore, a linear parameter-varying (LPV)/ H ∞ path-tracking controller that satisfies the pole placement and performance constraint is constructed to guarantee robustness and lateral stability across the whole parameters space with the coexistence of external disturbances and parametric uncertainties. Finally, the simulation results demonstrate that the proposed controller substantially enhances tracking performance while also maintaining excellent lateral stability. • A vehicle-road dynamic model with norm-bounded uncertainty for path-tracking controller design is built and analyzed. • An optimal-preview model is innovatively produced by utilizing velocity and the sideslip-yaw rate state of the vehicle. • An LPV/ H ∞ path-tracking control structure integrated optimal-preview model is proposed to improve tracking performance. [ABSTRACT FROM AUTHOR]

This article presents an extremum‐seeking control (ESC) algorithm for unmodeled nonlinear systems with known steady‐state gain and generally non‐convex cost functions with bounded curvature. The main contribution of this article is a novel gradient estimator, which uses a polyhedral set that characterizes all gradient estimates consistent with the collected data. The gradient estimator is posed as a quadratic program, which selects the gradient estimate that provides the best worst‐case convergence of the closed‐loop Lyapunov function. We show that the polyhedral‐based gradient estimator ensures the stability of the closed‐loop system formed by the plant and optimization algorithm. Furthermore, the estimated gradient provably produces the optimal robust convergence. We demonstrate our ESC controller through three benchmark examples and one practical example, which shows our ESC has fast and robust convergence to the optimal equilibrium. [ABSTRACT FROM AUTHOR]

This article develops an event-triggered cooperative robust formation control scheme for nonlinear multi-agent systems with dynamic uncertainties via reinforcement learning. By formulating a modified value function for each agent, the cooperative robust formation control problem of uncertain multi-agent systems is transformed into a cooperative optimal formation control problem of its nominal plant. To save communication and computing resources, a novel triggering condition is developed for each agent, and the controller is renovated only when an event occurs. Subsequently, the event-triggered optimal formation control law of each agent is derived by solving the coupled Hamilton-Jacobi-Bellman equation via single-critic structure. Furthermore, theoretical analysis indicates that the developed event-triggered cooperative robust formation control approach ensures the asymptotic stability of the formation error for each uncertain agent. Eventually, two simulation cases are adopted to confirm the effectiveness of the developed control approach. [ABSTRACT FROM AUTHOR]

Quantum systems are exceedingly difficult to engineer because they are sensitive to various types of noises. In particular, time-dependent noises are frequently encountered in experiments but how to overcome them remains a challenging problem. In this work, we propose a flexible robust control technique to resist time-dependent noises based on inverse geometric optimization working in the filter-function formalism. The basic idea is to parameterize the control filter function geometrically and minimize its overlap with the noise spectral density. This then effectively reduces the noise susceptibility of the controlled system evolution. We show that the proposed method can produce high-quality robust pulses for realizing desired quantum evolutions under realistic noise models. Also, we demonstrate this method in examples including dynamical decoupling and quantum sensing protocols to enhance their performances. [ABSTRACT FROM AUTHOR]

The past two decades have seen growing calls for the “tobacco endgame.” Its advocates are united by their commitment to two ideas. First, tobacco‐related harms represent a catastrophic health emergency, and second, current tobacco‐control approaches are an inadequate response to the scale of that emergency. To endgame advocates, tobacco policy should have more ambitious goals than merely “controlling” tobacco. Instead, it should aim to bring about a smoke‐free world. While a range of different policies are included under the umbrella of the “tobacco endgame,” the most radical proposal is for a complete ban on tobacco. Its advocates argue that in addition to improving global public health, an effective ban on tobacco would also promote overall autonomy and would have important egalitarian benefits. This article critically examines these arguments for a tobacco ban. I argue that they rely on idealizing assumptions about the likely effects of a ban. Because an effective ban would require robust enforcement to control the illegal market in tobacco, it would be more likely to undermine autonomy and equality than it would be to promote them. By relying on idealizing assumptions and ignoring the likely consequences of a tobacco ban, advocates of a ban obscure, rather than clarify, both the policy debate and the ethical stakes. I conclude by considering the ways that idealizing assumptions should—and should not—play a role in debates about ethical issues in public policy. [ABSTRACT FROM AUTHOR]

Robust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process. [ABSTRACT FROM AUTHOR]

This work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions. [ABSTRACT FROM AUTHOR]

This research is dedicated to developing a min–max robust control strategy for a dynamic game involving pursuers, evaders, and defenders in a multiple-missile scenario. The approach employs neural dynamic programming, utilizing multiple continuous differential neural networks (DNNs). The competitive controller devised addresses the robust optimization of a joint cost function that relies on the trajectories of the pursuer–evader–defender system, accommodating an uncertain mathematical model while adhering to control restrictions. The dynamic programming min–max formulation facilitates robust control by accounting for bounded modeling uncertainties and external disturbances for each game component. The value function of the Hamilton–Jacobi–Bellman (HJB) equation is approximated by a DNN, enabling the estimation of the closed-loop formulation for the joint dynamic game with state restrictions. The controller’s design is grounded in estimating the state trajectory under the worst possible uncertainties and perturbations, providing a robustness factor through the robust neural controller. The learning law class for the time-varying weights in the DNN is generated by studying the HJB partial differential equation for the missile motion for each player in the dynamic game. The controller incorporates the solution of the obtained learning laws and a time-varying Riccati equation, offering an online solution to the control implementation. A recurrent algorithm, based on the Kiefer–Wolfowitz method, adjusts the initial conditions for the weights to satisfy the final condition of the given cost function for the dynamic game. A numerical example is presented to validate the proposed robust control methodology, confirming the optimization solution based on the DNN approximation for Bellman’s value function. [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]

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]

Lunar-based equipment undertakes the task of movement and transportation in the construction of unmanned lunar base. In the process of moving, the mechanical legs of the equipment are influenced by the lunar soil with special mechanical properties. In order to avoid these uncertainties caused by the lunar soil and other lunar environmental conditions affecting the safety during the mission, a new robust control method with the form of sectionalized expression is proposed based on the dynamic model of the leg-soil system. Lyapunov second method is introduced to demonstrate that the proposed control method can maintain the stability of the leg-soil system successfully. In order to clarify the contact force in the dynamics model, CAS-1 lunar soil simulant that can accurately simulate real lunar soil is used in the calibration test to obtain the precise mechanical parameters. Simulation and experiment are also carried out to verify the proposed control method and the traditional control methods are introduced to make a comparison. Both the simulation and experiment results show that the proposed control method has a better control effect than traditional methods. The proposed method improves the accuracy by an average of 75.7% and 55.9% compared to the traditional methods and the error is limited to 0.2%. By maintaining the stability and accuracy of the leg-soil system, the stability of the lunar-based equipment is improved when performing construction tasks. This study lays the foundation for the construction of unmanned lunar base in advance. [ABSTRACT FROM AUTHOR]

• The 6-DOF attitude-orbit coupled model of SFF is established. The model has no redundant state and can be achieved globally without singularity. • A FxTDO is developed to estimate the external disturbance. Any prior knowledge of the disturbance is not required. The estimation error of the disturbance can converge to zero in fixed time regardless of the initial states. • A composite control scheme which consists of the NFFTSM surface and a feed-forward term based on the FxTDO is proposed such that each follower spacecraft can track the desired positions and attitudes in fixed time. This paper presents a distributed fixed-time control protocol for 6-DOF(degree-of-freedom) attitude-orbit coupled spacecraft formation flying with external disturbances. Firstly, the 6-DOF attitude-orbit coupled dynamics is established. The communication topology between the spacecraft in the formation is described by a directed graph. Secondly, a fixed-time disturbance observer (FxTDO) is designed to ensure that the estimation errors of the unknown external disturbances can converge to zero in fixed time. Thirdly, based on non-singular fixed-time terminal sliding mode (NFFTSM) surface, the fixed-time coordination control protocol is proposed combined with the fixed-time disturbance observer, which can guarantee that each spacecraft can track their desirable positions and attitudes in fixed time. The stability of the closed-loop system is proved through Lyapunuov method. Finally, simulation results for a formation with one virtual leader and five followers demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Robust regulation is predictively investigated in time-delay models including uncertainties, non-zero reference, and disturbance. Hence, to handle the complexities induced because of the time-delays, disturbances, and uncertainties, enhancing the transient performance would be a main control goal. To this purpose, the required transient and steady-state specifications are achieved through an integral control structure with adjustable gains. The constraints on the signals and aims of the control algorithm are also described by some linear matrix inequalities. Guaranteeing the stability, a minimisation procedure is found to specify the predictive controller. Several simulations are performed to establish the effectiveness of the introduced plan in comparison with the similar strategy. [ABSTRACT FROM AUTHOR]

AbstractThis article introduces a novel robust Shewhart-type control chart designed for monitoring the location of a bivariate process using the Comedian and Sn Covariance estimators. The process of setting control limits for the proposed control charts is demonstrated. To evaluate the performance of the newly proposed control charts, simulations are conducted and compared against existing methods found in the current literature. Furthermore, the article discusses the application of the proposed method using real-life data. [ABSTRACT FROM AUTHOR]

Investigating a long-standing conceptual question in bacterial physiology, we examine why DnaA, the bacterial master replication initiator protein, exists in both ATP and ADP forms, despite only the ATP form being essential for initiation. We engineered the Δ4 Escherichia coli strain, devoid of all known external elements facilitating the DnaA-ATP/ADP conversion and found that these cells display nearly wild-type behaviors under nonoverlapping replication cycles. However, during rapid growth with overlapping cycles, Δ4 cells exhibit initiation instability. This aligns with our model predictions, suggesting that the intrinsic ATPase activity of DnaA alone is sufficient for robust initiation control in E. coli and the DnaA-ATP/ADP conversion regulatory elements extend the robustness to multifork replication, indicating an evolutionary adaptation. Moreover, our experiments revealed constant DnaA concentrations during steady-state cell elongation in both wild-type and Δ4 cells. These insights not only advance our understanding of bacterial cell-cycle regulation and DnaA but also highlight a fundamental divergence from eukaryotic cell-cycle controls, emphasizing protein copy-number sensing in bacteria versus programmed protein concentration oscillations in eukaryotes. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered. [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]

Risk taking increases during the final round of a set of repeated risky decisions, a phenomenon known as the ending effect. Recent evidence suggests that the motivation to pursue an emotionally rewarding ending may account for this ending effect. This study tested this explanation using different task paradigms. Experiment 1 ruled out a financial motivational explanation for the ending effect. Specifically, when risk taking was only associated with emotional incentives without financial rewards, the ending effect remained significant. Experiment 2a demonstrated that the ending effect was robust after controlling for the relevant variables. Experiment 2b measured participants' motivation using a visual reaction time task. The results revealed that perceiving an ending led participants to be more concerned with emotional satisfaction than financial rewards. These findings consistently support the notion that the perception of an approaching ending leads to an increase in emotional rather than financial motivation, and this increased emotional motivation could lead to increased risk-taking toward an ending. This study also ruled out the alternative explanation that the ending effect is driven by the need for financial rewards. [ABSTRACT FROM AUTHOR]

Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model's interpretability diminishes as the ensemble's complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches. [ABSTRACT FROM AUTHOR]

The Multimodal Named Entity Recognition (MNER) task enhances the text representations and improves the accuracy and robustness of named entity recognition by leveraging visual information from images. However, previous methods have two limitations: (i) the semantic mismatch between text and image modalities makes it challenging to establish accurate internal connections between words and visual representations. Besides, the limited number of characters in social media posts leads to semantic and contextual ambiguity, further exacerbating the semantic mismatch between modalities. (ii) Existing methods employ cross-modal attention mechanisms to facilitate interaction and fusion between different modalities, overlooking fine-grained correspondences between semantic units of text and images. To alleviate these issues, we propose a graph neural network approach for MNER (GNN-MNER), which promotes fine-grained alignment and interaction between semantic units of different modalities. Specifically, to mitigate the issue of semantic mismatch between modalities, we construct corresponding graph structures for text and images, and leverage graph convolutional networks to augment text and visual representations. For the second issue, we propose a multimodal interaction graph to explicitly represent the fine-grained semantic correspondences between text and visual objects. Based on this graph, we implement deep-level feature fusion between modalities utilizing graph attention networks. Compared with existing methods, our approach is the first to extend graph deep learning throughout the MNER task. Extensive experiments on the Twitter multimodal datasets validate the effectiveness of our GNN-MNER. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

The role of pumped storage in global energy structure transformation is becoming increasingly prominent. This article introduces a flexible excitation system based on fully controlled device converters into pumped storage units (PSUs). It can address the issues of insufficient excitation capacity and limited stability associated with traditional thyristor excitation systems. The study focuses on linear active disturbance rejection control (LADRC) for the flexible excitation control system of pumped storage units and utilizes intelligent optimization algorithms to optimize the controller parameters. This addresses the inherent problem of traditional PID controllers, which are unable to alleviate the trade-off between response speed and overshoot. At the same time, the robustness and anti-interference of the control system are improved, effectively enhancing the performance of the pumped storage flexible excitation control system. Simulation verifies the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper expands a recently proposed peak current-mode (PCM) control method for a power factor correction (PFC) boost converter to include the totem-pole converter and solves the controller's compatibility problem with the totem-pole converter by proposing three input current sensing methods. Using MATLAB/Simulink 2023b, simulation experiments on a 2 kW totem-pole converter utilizing the PFC PCM controller were carried out to assess the performance of the controller with the proposed sensing methods. The findings indicate that under steady-state conditions, all three proposed sensing methods performed input current shaping successfully and yielded nearly identical THD% of about 4.4% in the input current waveform. However, it is noteworthy that method 2, referred to as the memory method, exhibited a sluggish and less robust transient response in comparison to the swift and resilient responses observed with method 1 and method 3. Additionally, the third proposed method, which involves a single current sensor positioned across the input inductor, emerged as the optimal and cost-effective sensing solution. This method achieved the same desirable attributes of fast and robust control while utilizing only a single current sensor, a notable advantage over method 1, which employs two current sensors. [ABSTRACT FROM AUTHOR]

Multistability is central to biological systems. It plays a crucial role in adaptation, evolvability, and differentiation. The presence of positive feedback loops can enable multistability. The simplest of such feedback loops are (a) a mutual inhibition (MI) loop, (b) a mutual activation (MA) loop, and (c) self-activation. While it is established that all three motifs can give rise to bistability, the characteristic differences in the bistability exhibited by each of these motifs is relatively less understood. Here, we use dynamical simulations across a large ensemble of parameter sets and initial conditions to study the bistability characteristics of these motifs. Furthermore, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling amongst them. Our analysis revealed that MI-based architectures offer discrete and robust control over gene expression, multistability, and coordinated expression among multiple genes, as compared to MA-based architectures. We then devised a combination of MI and MA architectures to improve coordination and multistability. Such designs help enhance our understanding of the control structures involved in robust cell-fate decisions and provide a way to achieve controlled decision-making in synthetic systems. [ABSTRACT FROM AUTHOR]

The chain rule is a standard tool in differential calculus to find derivatives of composite functions. Faà di Bruno's formula is a generalization of the chain rule and states a method to find high-order derivatives. In this contribution, we propose an algorithm based on Faà di Bruno's formula and Bell polynomials (Bell in Ann Math 29:38–46, 1927; Parks and Krantz in A primer of real analytic functions, 2012) to compute the structure of derivatives of function compositions. The application of our method is showcased using trajectory planning for the heat equation (Laroche et al. in Int J Robust Nonlinear Control 10(8):629–643, 2000). [ABSTRACT FROM AUTHOR]

This paper devotes to the design of all feasible output feedback controllers for the robust output tracking of Boolean control networks with disturbances (DBCNs). First, the output feedback–based robust control invariant set (RCIS) is defined, and a calculation method to find all output feedback–based RCISs of a given set is derived via the truth matrix technique. Second, a class of complete family of robust reachable sets (CFRRSs) is developed; the solvability of robust output tracking problem by output feedback control is verified. Furthermore, a constructive algorithm of all feasible output feedback controllers is presented. Finally, the study of a network about Escherichia coli lactose operon shows the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Proportional pressure-reducing valve (PPRV) is widely used in brake circuits and pilot supply lines for directional control valves. The performance of PPRV can be degraded because of the effect of magnetic hysteresis, frictions, and other disturbances, which worsens the accuracy of the entire hydraulic system. This paper presents a high-performance pressure controller of a PPRV considering the model nonlinearities. A nonlinear phenomenological model based on the Hammerstein modeling method is first developed to describe the dynamics of a PPRV. A pressure-dependent damping ratio is carried out to handle the asymmetric nonlinearity at the starting process of pressure adjustments, and a series of experiments are conducted to verify the effectiveness of the model. A second-order adaptive robust controller is developed based on the Lyapunov theorem to guarantee the tracking performance in the presence of parameter uncertainties and uncertain nonlinearities. Comparative experiments show that the proposed second-order adaptive robust control algorithm together with the phenomenological model achieves a significant reduction in pressure-tracking error, compared with the one without the proposed plant model, or other existing controllers such as the feedforward proportional–integral–derivative (PID) controller and the first-order adaptive robust controller. [ABSTRACT FROM AUTHOR]

This article investigates the problem of monitoring wildfires using a quadcopter uncrewed aerial vehicle (UAV) subject to external perturbations and nonlinear uncertainties. A finite‐time control scheme is designed for the quadcopter UAV to monitor the elliptical propagation of a wildfire. The control scheme is composed of an improved nonsingular fast terminal sliding mode control (NFTSMC) and a higher order sliding mode observer (HOSMO). The HOSMO is constructed first to estimate external perturbations, nonlinear uncertainties, and incomplete system states resulting from the system's uncertainties. Then, a novel NFTSMC is developed that utilizes the estimated system states. The Lyapunov theorem is used to demonstrate that the quadcopter UAV can elliptically monitor a wildfire, and the tracking error can rapidly converge to zero within a finite time. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The focus of this paper is the design of open-loop control profiles for linear differentially flat systems whose model parameters are uncertain and are represented probabilistically. To account for the time-invariant model parameter uncertainties, polynomial chaos is used to derive a surrogate model which permits easy evaluation of the mean and variance of the uncertain states. A chance constrained optimisation problem is posed to minimise the terminal error of the stochastic states for a prescribed risk of not satisfying the terminal state bounds. To permit posing a convex optimisation problem, the cost function which is the residual energy is approximated by a hypercube circumscribing the hypersphere which bounds the terminal residual error. This relaxation permits posing a convex optimisation problem to arrive at the robust control profile. The proposed technique is illustrated on two examples including the benchmark spring-mass-dashpot system undergoing a rest-to-rest maneuver and a UAV undergoing a translation in a prescribed time. Parametric studies are conducted where the impact of varying the order of the polynomial chaos expansion and the degree of the parameterised control profiles are used to conclude that a 2–3 degree polynomial chaos expansion is sufficient to capture the time evolution of the mean and variance of the states which are required for the chance constrained optimisation problems. As expected, the design results in improved performance as the number of free variables used to parameterise the control profiles increases. [ABSTRACT FROM AUTHOR]