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Before extending the range of services provided, maximizing the usefulness of current procedures within community pharmacy settings is needed, as the scope of pharmacy services is evolving in different dimensions. The present study aims to assess the degree of factors affecting the satisfaction of traditional community pharmacy services using population data collected from patients attending academic and public healthcare centers in Jordan. A validated, pretested, and adapted survey instrument has been utilized to assess the satisfaction of contemporary services delivered by community pharmacists in different dimensions. Linear regression analysis evaluated the predictors associated with higher total satisfaction scores with community pharmacy services. The present study included 642 patients attending healthcare centers. Different dimension scores, such as explanation and consideration, scored similarly, with values ranging from 64.5% - 69.7% of the maximum possible score. The mean total scale score was 67.2% of the total possible scores. Using the linear regression analysis, respondents who were satisfied with their treatment plans were likely to have higher satisfaction with community pharmacy services. The increased number of prescription medications and increased age were associated with lower satisfaction with community pharmacy services. Results indicated that healthcare policymakers might be confident in the services within the community pharmacy setting; however, there is always room for more robust quality control activities. [ABSTRACT FROM AUTHOR]

Decentralized control systems are frequently employed to tackle control challenges in multi-input multi-output (MIMO) systems. Hence, this paper proposes a decentralized PID controller that relies on frequency domain specifications. This controller is designed to minimize the loop interactions and fulfill the design criteria of the MIMO system. The PID parameters are calculated based on gain and phase margin specifications. Additionally, a first-order plus dead time model is derived for each of the decoupled subsystems. Furthermore, robust stability is analysed using disc margin analysis, which overcomes the constraints of traditional margins. Moreover, the paper determines a secure range of uncertainty for various levels of gain and phase uncertainties. The findings suggest that the proposed controller achieves robust stability over a broader range of margins when compared to alternative control methods. [ABSTRACT FROM AUTHOR]

Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks. Harmonic mitigation filters with robust control technique are required to reduce the harmonic effects on power transformer. Traditionally, synchronous reference frame (DQ) is employed to control the shunt active power filter (APF) for mitigation of harmonics in power transformers. DQ control of Shunt APF lacks merits of fast response, delayed operation due to phased lock loop under abnormal grid conditions leading to insufficient harmonic elimination. A developed DQ method based on detecting the positive and negative sequence components is proposed to precisely control the shunt APF for reliable operation of power transformer. This detection technique improves the response time, mitigate the harmonics effecting the operation of transformer and overall power factor. The proposed control system is evaluated under different abnormal operating scenarios and compared with traditional DQ method. The results and analysis confirm the efficacy of the developed DQ method in improving the power transformer performance. [ABSTRACT FROM AUTHOR]

Today, hydrogen fuel cells (HFCs) have become very popular in various applications because of their ability to be a clean energy source. One of the difficulties associated with HFCs is their sluggish response to variations in the load. This paper presents a nested control strategy based on a type-2 neuro-fuzzy controller (T2NFC) to improve the HFC's dynamic response in a hybrid stand-alone power network using HFC and battery. A system model is constructed using Matlab-Simulink. An interleaved converter is used to draw the maximal power from the HFC and reduce the ripple in the HFC's current. Two T2NFCs control DC voltage and battery charge/discharge current. The robustness of the T2NFC is evaluated for input disturbance, output disturbance, and both disturbances. The results show that the proposed control strategy is robust against input and output disturbances. Also, it provides improved dynamic response of the HFC, lower ripple in HFC current, and less overshoot or undershoot in DC voltage both in transient and steady-state. For step reference input, the proposed controller improves settling time of 24.06 % and overshoot of 59.55 % compared to the conventional PI controller. The results verify the effectiveness of the proposed controller under different operating conditions of hybrid stand-alone power network with fuel cell and battery. • The response of HFC is enhanced, leading to faster and more accurate power generation. • The hydrogen fuel cell's current is experienced a significant reduction in ripples. • The hydrogen fuel cell is now being operated at its maximum power point. • A Type-2 Neuro-Fuzzy Controller is developed to provide robust control of the hydrogen fuel cell's output voltage. [ABSTRACT FROM AUTHOR]

We present a proportional–integral–derivative-based controller plus an adaptive slide surface to solve the trajectory tracking control problem for a fully actuated vessel with unknown parameters perturbed by slowly varying external unknown dynamics. The controller design assumes that the vessel moves at low speed and frequency, its physical parameters are unknown, and its state is measurable. The control approach ensures error tracking convergence toward a small vicinity at the origin. We conduct the corresponding stability analysis using the Lyapunov theory and saturation functions. We tested the controller through two numerical experiments—a turning ellipse maneuver and a rest-to-rest maneuver—where the vessel parameters were unknown, and we obtained satisfactory results. [ABSTRACT FROM AUTHOR]

In this article, a new intelligent fault-tolerant control (FTC) is designed to control a flexible-link manipulator with uncertain dead-zone and intermittent actuator faults. Initially, a smooth dead-zone inverse model using a hyperbolic tangent function is introduced to handle dead-zone nonlinearity and suppress input chattering. An adaptive law is proposed to estimate an unknown coupling item, combining the upper bounds of compensation error and floating bias faults, achieving robust adaptive control of the system. A new FTC strategy is subsequently developed to address intermittent actuator faults. Finally, the bounded convergence of system state errors is proven using direct Lyapunov methods, and the effectiveness and superiority of the proposed controller are demonstrated through numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

In this paper, an anti-unwinding adaptive robust backstepping control law is proposed to solve the attitude control problem of rigid spacecraft with external disturbances and inertia uncertainties. A new variable based on hyperbolic sine functions is designed to prevent the unwinding phenomenon, and then a novel robust backstepping controller is developed. Initially, a nonlinear tracking law is designed to obtain the desired angular velocity and to ensure that the attitude maneuvering system could have two equilibrium points. Subsequently, an adaptive robust control law is devised, which incorporates the adaptive estimation method to update the inertial parameters in real-time. Moreover, the bounded external disturbances are taken into account in the design of the control law, and thus the robustness of the attitude control system is improved. Furthermore, the global asymptotic stability of the attitude control system is verified by the Lyapunov-Krasovskii theorem. Finally, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed control law, as well as the anti-unwinding characteristics are perfectly validated during the attitude maneuver of rigid spacecraft. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern real-world control problems call for continuous control domains and robust, sample efficient and explainable control frameworks. We are presenting a framework for recursively composing control skills to solve compositional and progressively complex tasks. The framework promotes reuse of skills, and as a result quick adaptability to new tasks. The decision tree can be observed, providing insight into the agents' behavior. Furthermore, the skills can be transferred, modified or trained independently, which can simplify reward shaping and increase training speeds considerably. This paper is concerned with efficient composition of control algorithms using reinforcement learning and soft attention. Compositional and temporal abstraction is the key to improving learning and planning in reinforcement learning. Our Thompson sampling inspired soft-attention model is demonstrated to efficiently solve the composition problem. [ABSTRACT FROM AUTHOR]

Experimental advanced superconducting tokamak (EAST) fast control power supply (FCPS) is an essential device to realize balance control of plasma vertical displacement during controllable nuclear fusion process. The primary control purpose of EAST FCPS is to quickly output current to track reference current. Change of load inductance parameters due to plasma motion poses higher robustness requirements for fast tracking control of output current. Therefore, an improved gray prediction parameter identification sliding mode control method is proposed to achieve fast tracking and robust control of output current under changes in load inductance parameters. Parameter identification sliding mode control method applied to identify load inductance parameters in real‐time, accurate EAST FCPS output current tracking sliding mode control model is established. High‐order terms are added to discrete exponential convergence reaching law, and a new smooth saturation function is designed to replace traditional sign function to achieve chattering suppression and accelerate system convergence speed. Gray prediction used in current sampling to achieve output current trajectory advanced prediction to further accelerate output current response speed. Four times equal interval time sampling within a fixed switching period and new information priority period by period prediction is proposed to improving gray prediction, achieving digital control delay compensation, and improving prediction accuracy of output current trajectory at abrupt edge. Simulation and experimental verification show that the proposed improved gray prediction parameter identification sliding mode control method has good output current tracking control performance under changes in load parameters. Compared with PI control method currently used in engineering, the proposed control method performs better in output current fast response and overshoot suppression. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the International Journal of Robust & Nonlinear Control on the topic of time-delay systems. It provides a historical overview of functional differential equations and highlights the contributions of Nikolay Nikolayevich Krasovskii and Aristide Halanay to the field. The special issue includes 21 research papers covering stability, robustness, observer theory, predictors, and delay-based approaches to partial differential equation models. Each paper is briefly described, covering topics such as stabilization of uncertain systems, sampled-data control for nonlinear systems, observer theory for differential delay systems, sensor fault estimation, and control approaches for vehicular platoons. The summaries aim to provide library patrons with a quick understanding of the papers' contents and their relevance to their research interests. [Extracted from the article]

Sampled‐data control problems via memoryless state and output feedback are studied, respectively, for a class of strongly nonlinear systems with state and input delays. Under sample and hold, both state and output feedback control schemes are developed by virtue of the emulation method, the adding a power integrator technique, and the recursive design of nonlinear observers. With the aid of Lyapunov–Krasovskii functional theorem, together with the idea of robust control, we prove that the proposed sampled‐data state and output feedback controllers make the hybrid closed‐loop systems with delays and uncertainty globally asymptotically stable, if the input delay and sampling period are limited. The class of time‐delay uncertain systems under consideration goes beyond the Lipschitz or linear growth condition and is genuinely nonlinear in the sense that it contains uncontrollable/unobservable linearization and is not stabilizable, even locally, by any linear or smooth feedback. Applications of the sampled‐data robust control schemes presented in this article are illustrated by examples with simulations. [ABSTRACT FROM AUTHOR]

We consider the problem of enforcing safety in multi‐input systems with distinct input delays via the use of Control Barrier Functions (CBFs). For systems with input delay(s), a popular approach for enforcing safety is the combination of a CBF designed for the delay‐free system with state‐predictors that compensate the input delays. Typically, this comes with the assumption that the system does not violate safety constraints before all input delays have been compensated and the system is fully under control. In this paper, we introduce two control approaches that enforce safety before all input delays have been compensated, whenever it is possible to do so. We do this by utilizing a robust CBF formulation that treats longer‐delayed inputs as known disturbances when determining control effort for shorter delayed inputs. This formulation ensures that, whenever possible, a subset of input channels with shorter delays will be utilized for keeping the system in the admissible safe set before longer input delays have been compensated. The effectiveness of our approaches is demonstrated with two numerical examples. [ABSTRACT FROM AUTHOR]

The permanent magnet linear motor (PMLM) systems are widely applied in various engineering fields. However, the state inequality constraints and uncertainties of parameters and unknown external disturbances need to be further explored. In this article, first, a state transformation method is proposed for converting bounded states to unbounded states. As a result, the output state of system is limited within the expected boundary constraint. Then, a new control strategy is constructed. This strategy can ensure that the controlled system with uncertainties is uniformly bounded and uniformly ultimately bounded. Finally, the effectiveness of the controller and the state transformation method are verified through simulation of PMLM system. The inequality constraints of the output state are satisfied, and the system performance is improved. [ABSTRACT FROM AUTHOR]

We propose a PDE-based accelerated gradient algorithm for optimal feedback controls of McKean–Vlasov dynamics that involve mean-field interactions both in the state and action. The method exploits a forward-backward splitting approach and iteratively refines the approximate controls based on the gradients of smooth costs, the proximal maps of nonsmooth costs, and dynamically updated momentum parameters. At each step, the state dynamics is approximated via a particle system, and the required gradient is evaluated through a coupled system of nonlocal linear PDEs. The latter is solved by finite difference approximation or neural network-based residual approximation, depending on the state dimension. We present exhaustive numerical experiments for low- and high-dimensional mean-field control problems, including sparse stabilization of stochastic Cucker–Smale models, which reveal that our algorithm captures important structures of the optimal feedback control and achieves a robust performance with respect to parameter perturbation. [ABSTRACT FROM AUTHOR]

Robustness plays a vital role in the operation of smart manufacturing information systems (SMISs). This article aims to establish a robust analysis theory, to explain how companies can perform robust analysis of information systems in real time during normal operations and perform robust control over unstable information systems. (1) Using of maximum variance unfolding (MVU) algorithm to fuse secondary index data into primary index data. (2) Using data-driven modeling to establish a robust evolutionary dynamics model of SMISs. (3) Using synergetic and self-organization theory to analyze the dynamic model of the robustness of SMISs. (4) Using dynamic programming and stability theory to conduct robust optimal control. (1) The primary indicator data can maintain the characteristics of the secondary indicator data by using the improved MVU method; (2) Dynamic programming and stability theory used to conduct optimal robust control can give investment strategy to improve the robustness of SMISs; (3) The combination of the synergetic theorem and machine learning can quantitatively describe the SMISs, effectively solve the complex calculation problems caused by the increase of the variable dimension. (1) This paper proposes for the first time the use of maximum variance unfolding (MVU) algorithm to fuse secondary index data into primary index data; (2) for the first time combining complex system theory with machine learning and optimization theory to analyze the robustness of information systems. [ABSTRACT FROM AUTHOR]

In transcranial magnetic stimulation (TMS) and electroencephalography (EEG) experiments, two researchers typically collaborate in the lab. This study addresses the challenge a single researcher faces in managing the TMS experiment's timing while operating the TMS coil. It introduces the Arduino Trigger Generator (ArTGen) to remotely control the timing of TMS experiments using a footswitch pedal. Moreover, a bespoke printed circuit board (PCB) is designed to interface the eegoMylab amplifier with off‐the‐shelf EEG caps. The ArTGen facilitates accurate timing of the TMS stimulator's inter‐pulse intervals (IPIs) through a footswitch pedal, enhancing researchers' control over TMS‐EEG experiments. The PCB interface provides a cost‐effective tool to extend the functionality of the eegoMylab amplifier. The integration of our PCB interface has been validated in a custom TMS‐EEG setup by analyzing TMS‐evoked potentials (TEPs), global mean field power (GMFP), butterfly plots, and event‐related spectral potentials (ERSPs). The PCB reliably preserved EEG signal integrity, ensuring accurate data acquisition. Thorough channel‐wise consistency checks across components confirmed data accuracy. ArTGen's portability and footswitch feature streamline experimental control, aiding TMS‐EEG research and clinical applications. Moreover, our PCB resolves compatibility between the eegoMylab amplifier and the Waveguard EEG cap by extending the amplifier to connect to off‐the‐shelf EEG caps. The ArTGen serves as a robust remote control tool for TMS stimulators, while our PCB interface presents a solution for integrating a customized TMS‐EEG setup. This study addresses the gap in existing TMS‐EEG research by introducing innovative technological enhancements that not only augment experimental flexibility but also streamline procedural workflows. [ABSTRACT FROM AUTHOR]

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems. [ABSTRACT FROM AUTHOR]

According to Logvinovich cavity dependent expansion principle, a cavity developed by cavitator has memory effect, which arises cavity deformation at aft part of a supercavitating vehicle and leads time delay of travel state. This delay affects the dynamic behavior through changing hydrodynamic and moment on the control surface, and even induces instability. This paper focuses on the time delay effect of the underwater supercavitating vehicle, and a simplified dynamic model of underwater supercavitating vehicle with delay was established. First, a planing force was simplified in straight level state, then a simplified model of supercavitating vehicle with delay in longitude plane was obtained. Taking depth, pitch angle, vertical speed and pitch rate as state variant, H ∞ state feedback controller was designed by linear matrix inequality (LMI) and Lyapunov stable theory was exploited to prove the asymptotic stability of the system. Simulation results show that, the controlled system is effectiveness for both simplified model and non-simplified model and it can track reference command signal. [ABSTRACT FROM AUTHOR]

Aircraft icing deteriorates aerodynamic performance and reduces stall angle of attack, the fast convergence rate of tracking error is required to stabilize the aircraft when aircraft icing occurs. The state-of-the-art control methods for icing aircraft mostly assume that the icing of aircraft is instantaneous. Aiming at these issues, a fixed-time angle of attack-constrained control strategy is designed considering dynamic icing process. In order to explore the variation of aerodynamic coefficients in the process of dynamic icing, an ice wind tunnel experiment is implemented, and the relationship between lift coefficient, drag coefficient and pitching moment coefficient with angle of attack and icing intensity is obtained by fitting method. In order to prevent the stalling problem caused by the decrease of the stalling angle of attack in the process of dynamic icing, a method to determine the stalling angle of attack based on deep neural network is proposed. Considering the asymmetric and time-varying angle of attack constraint, a fixed-time convergent angle of attack-constrained robust control method is designed. The ice wind tunnel experiment shows the process of dynamic icing of the airfoil, and the simulation results verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This research presents an advanced control approach for battery management in battery electric utility vehicles (BEUV) operating in indoor logistics environments. The proposed approach utilizes a combination of proportional-integral (PI), fuzzy PI, and interval type 2 fuzzy PI (IT2fuzzyPI) control structures to augment the state space model for battery management. The state space model incorporates the voltage and current of each battery cell as state variables and considers the current demand from the electric motor as an input. By integrating fuzzy logic with PI control and considering uncertainty, the IT2fuzzyPI structure offers improved control recital and system robustness in the occurrence of nonlinearities, uncertainties, and turbulences. The outcomes of the simulation validate the effectiveness of the proposed scheme in managing the battery pack system’s state of charge and controlling the rates of charging and discharging. The IT2fuzzyPI control significantly improves the overall proficiency and longevity of the battery system, making it suitable for battery electric utility vehicles in logistics environments. This research contributes to the field of battery management systems, providing a valuable tool for designing and evaluating high-performance electric vehicles with enhanced control capabilities. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing the control precision and efficiency of a two-degree-of-freedom (2-DOF) space manipulator used for active space debris removal. The unpredictable space environment introduces large uncertainties, which introduces unique challenges beyond the capabilities of a standalone computed torque controller and degrades control performance. To address this problem, a robust controller is developed, integrating traditional techniques such as sliding mode and computed torque control with a Neural Network framework. This synergy leverages both methods' strengths—conventional controls' accuracy and Neural Network's adaptability. The integration of Neural Network-based sliding mode control complements the robustness of computed torque control by actively mitigating uncertainties and disturbances inherent in the space environment. The 2-DOF manipulator's state variables model the system dynamics, necessitating accurate relative motion estimation between the manipulator and debris. The global asymptotic stability of the developed algorithm is demonstrated through the Lyapunov theorem, guaranteeing error convergence to zero. The convergence, stability, precision, tracking errors, and responsiveness of the controller have been analysed and validated by the MATLAB Simulink simulations. The novel approach's performance effectiveness is substantiated by numerical simulations and a comparative analysis with conventional computed torque control. Outcomes highlight the superior precision and efficiency in manipulator tracking the trajectory, validating the integrated controller's potential for successful active space debris removal. • Neural network-based sliding mode computed torque control (NSMCTC) approach ensures precise space manipulator position tracking. • NSMCTC reduces chattering and improves convergence for enhanced trajectory control in dynamic space environments. • Lyapunov stability theorem validates uniform and ultimate boundedness, ensuring global asymptotic stability. • Self-learning capability and low computational complexity distinguish NSMCTC from traditional computed torque control methods. • Potential applications extend to spacecraft manipulator attitude control, offering adaptability for safe debris capture in multidimensional dynamics. [ABSTRACT FROM AUTHOR]

Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust finite-time control scheme is specifically designed for a class of under-actuated nonlinear systems. The proposed scheme integrates a reaching phase-free integral backstepping method with an integral terminal fractional-order sliding mode to ensure finite-time stability at the desired equilibria. The core of the algorithm is built around proportional-integral-based nonlinear virtual control laws that are systematically designed in a backstepping manner. A fractional-order integral terminal sliding mode is introduced in the final step of the design, enhancing the robustness of the overall system. The robust nonlinear control algorithm developed in this study guarantees zero steady-state errors at each step while also providing robustness against matched uncertain disturbances. The stability of the control scheme at each step is rigorously proven using the Lyapunov candidate function to ensure theoretical soundness. To demonstrate the practicality and benefits of the proposed control strategy, simulation results are provided for two systems: a cart–pendulum system and quadcopter UAV. These simulations illustrate the effectiveness of the proposed control scheme in real-world scenarios. Additionally, the results are compared with those from the standard literature to highlight the superior performance and appealing nature of the proposed approach for underactuated nonlinear systems. This comparison underscores the advantages of the proposed method in terms of achieving robust and stable control in complex systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications. [ABSTRACT FROM AUTHOR]

Objective: In the context of rapid Internet development, studying individual happiness holds significant practical and academic value. Methods: This article employs ordinary least squares to examine the impact of the frequency of Internet entertainment usage on happiness using data from the China Family Panel Studies spanning 2014 to 2018. Results: The empirical results demonstrate a significant positive effect of the frequency of Internet entertainment usage on happiness. Specifically, each additional instance of using Internet entertainment is associated with a 4.2 percent increase in happiness. This finding remains robust after controlling for other variables, annual, and provincial fixed effects. To address potential endogeneity issues, the perceived importance of Internet entertainment to residents is used as an instrumental variable. Heterogeneity analysis reveals that the frequency of Internet entertainment usage significantly affects happiness across different genders, ages, education levels, and employment statuses. Conclusion: The implications of this study suggest that governments should focus on the development of Internet entertainment, explore its beneficial potentials further, minimize its drawbacks, and enhance the overall happiness of residents through their engagement with Internet entertainment, thereby allowing the Internet to better serve the populace. [ABSTRACT FROM AUTHOR]

This study introduces the design of a state-feedback controller for Linear Parameter Varying (LPV) systems in scenarios where exogenous parameters are not directly accessible, and the state vector is to be estimated. Instead of considering a static feedback gain, it proposes a method for estimating these parameters and synthesizing a parameter-dependent state-feedback gain that is robust against uncertainties in parameter estimation. The state vector used by the state-feedback controller, and some quantities required by the estimation law, are both obtained by a robust filter synthesized by LMI (Linear Matrix Inequalities). This paper outlines the estimation, filtering, and control laws, detailing the conditions necessary for ensuring convergence and stability. A numerical experiment and a 2 DoF torsional system application show the enhanced dynamic performance of the method when applied to uncertain dynamic systems. The findings highlight the effectiveness of the proposed approach in maintaining system stability and improving performance despite the inherent uncertainties in parameter estimation, offering a significant contribution to the field of robust control for LPV systems. [ABSTRACT FROM AUTHOR]

This paper studies the H∞ control problem for polynomial time‐varying systems. The H∞ control problem has been much less investigated for time‐varying systems in comparison to the time‐invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time‐varying H∞ control problem with the ADP approach. Considering the time as an independent variable for sum‐of‐squares (SOS) optimization problems, an SOS‐based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed‐loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H∞ controller with a smaller disturbance attenuation coefficient, a two‐loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

For addressing the challenges of decreased attitude and trajectory tracking accuracy and a delayed response in the flight control operations of quadcopter unmanned aerial vehicles (UAVs) under the uncertainties of model parameters and external disturbances, this study leverages the advantages of the non-causal declarative modeling language Modelica in system modeling and simulation. In addition, it incorporates the nonlinear Active Disturbance Rejection Control (ADRC) framework for disturbance observation, estimation, and compensation. A state observer is designed to mitigate the impact of external disturbances and model uncertainties through feed-forward compensation, and stability analysis is conducted. Numerical simulations for hover resistance demonstrate that, compared to the cascade proportional integral differential (PID) control strategy, PID-NLADRC reduces the maximum deviation induced by wind disturbances by ∼50% and shortens the disturbance influence time by around 40%. Simulations for different trajectories, such as planar or spatial, smooth or abrupt changes, indicate that under the PID-NLADRC control strategy, the real-time spatial distance deviation mean is reduced by 69.5%, and the peak time is shortened by 75.7%. Validation through multi-objective applications and physical experiments highlights the advantages of PID-NLADRC in terms of positioning accuracy, rapid tracking, and disturbance suppression, aligning well with the fast, precise, and robust flight control requirements of quadcopter UAVs. [ABSTRACT FROM AUTHOR]

Summary: In this study, the problem of preview tracking control (PTC) was considered with regard to the linear parameter periodic systems (LPPSs) subject to previewed signals. First, the difference operator approach was extended to derive an augmented error system (AES). As a result, the PTC problem was transformed into a stabilization problem via state/output feedback. Second, sufficient conditions guaranteeing the closed‐loop stability of the augmented error systems were derived, and the design of a periodic controller with preview actions was proposed through the approach of linear matrix inequalities (LMIs). Finally, a numerical simulation was performed to illustrate the effectiveness of the proposed periodic controller. [ABSTRACT FROM AUTHOR]

This paper focuses on policy selection and scheduling of sensors and attackers in cyber-physical systems (CPSs) with multiple sensors under denial-of-service (DoS) attacks. DoS attacks have caused enormous disruption to the regular operation of CPSs, and it is necessary to assess this damage. The state estimation of the CPSs plays a vital role in providing real-time information about their operational status and ensuring accurate prediction and assessment of their security. For a multi-sensor CPS, this paper is different from utilizing robust control methods to characterize the state of the system against DoS attacks, but rather positively analyzes the optimal policy selection of the sensors and the attackers through dynamic programming ideology. To optimize the strategies of both sides, game theory is employed as a means to study the dynamic interaction that occurs between the sensors and the attackers. During the policy iterative optimization process, the sensors and attackers dynamically learn and adjust strategies by incorporating reinforcement learning. In order to explore more state information, the restriction on the set of states is relaxed, i.e., the transfer of states is not limited compulsorily. Meanwhile, the complexity of the proposed algorithm is decreased by introducing a penalty in the reward function. Finally, simulation results show that the proposed algorithm can effectively optimize policy selection and scheduling for CPSs with multiple sensors. [ABSTRACT FROM AUTHOR]

The compound directional drilling process, including slip and rotation, is the key to realizing long-hole drilling in coal mines. First, a compound directional drilling process control scheme is proposed to realize intelligent control of the directional drilling process. An intelligent drilling process optimization method was designed to improve drilling efficiency. A robust controller for the drilling rate based on gain scheduling was designed to achieve stable control of the drilling rate during sliding deflection drilling. A robust controller based on hybrid sensitivity was designed to achieve stable control of the drilling rate during rotary inclined stabilization drilling. The results of this study can provide a theoretical basis for realizing the intelligent optimization and control of compound directional drilling in coal mines. [ABSTRACT FROM AUTHOR]

Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of $ 133\,{\rm m} $ 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion. [ABSTRACT FROM AUTHOR]

This article presents a robust finite control set predictive scheme for a stand-alone squirrel cage induction generator (SCIG) drive. This technique is considered an alternative to the drive system due to the inclusion of system nonlinearities and fast dynamic response. The control objective in the distributed generation environment is to fix the output voltage to follow the stand-alone requirement. The strategy establishes optimized switching instants for cost function minimization for both source and load converter control and diminished cross-coupling amid active and reactive power during transient scenarios. The scheme is designed to achieve the minimal effect caused by the parameter uncertainties. During source and load changes, this work will also address the maintenance of dc-link voltage, machine, and load variables at the set value, supported by machine and load-end converter control to achieve stand-alone load objectives. In addition, the presented scheme is also tested with random variation of speed to check the efficacy of the control configuration. The drive performance is evaluated by simulation using MATLAB/Simulink environment. Comprehensive real-time findings obtained from a scaled laboratory test bench using dSPACE-1104 are provided to verify the feasibility of the predictive solution. • Distinct design approach of predictive control for SCIG based drive system feeding stand-alone load. • The developed generator side converter strategy controls the active power flow from the SCIG by regulating the dc-link voltage. • The performance of the control scheme is found to be much superior under load transients, and input power variation. • The uncertainty in the values of the machine parameters is also tested and system offer better dynamic performance. • Demonstration of MATLAB Simulation and, then validation with Real-Time results. [ABSTRACT FROM AUTHOR]

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H ∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism. [Display omitted] • A multi-event driven mechanism (MEDM) based joint non-fragile control scheme is derived. • The wind-integrated power system model based on MEDM is established. • The MEDM is integrated into the joint non-fragile controller design under DoS attacks. • The MEDM based joint non-fragile H ∞ automatic generation control strategy is proposed. • It provides stronger robustness and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a tube-based model predictive control strategy for linear systems with bounded disturbances and input delay to ensure input-to-state stability. Firstly, the actual disturbed system is decomposed into a nominal system without disturbances and an error system. For the nominal system, solving an optimization problem, where the delayed control input is set as an optimization variable, yields a nominal control law that enables the nominal state signal to approach to zero. Then, for the error system, the Razumikhin approach is used to identify a robust control invariant set. Using the set invariance theorem, an ancillary control law is developed to confine the error state signal in the invariant set. Combining the two results, we obtain a control law that enables the state signal to remain within a robustly invariant tube. Finally, the effectiveness of the developed control strategy is validated by simulations. • An ancillary control is used to ensure the state errors stay in the invariant set. • A constraint on the delayed input is added to ensure feasibility of the algorithm. • By introducing a state error term, a novel Lyapunov function is proposed. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present two distinct linear Model Predictive Control (MPC) methods for controlling mobile robots in the presence of obstacles while considering the wheel slip. Predictability of the controller enables the robot to automatically choose an alternative path to avoid obstacles. However, environmental conditions and disturbances, including slip, may impact the system model. Therefore, to accurately represent the system, slip angle and slip ratio are factored into the modeling process. Then the kinematic model is linearized using the successive method to reduce computational cost. Next, both Stable MPC (SMPC) and Robust MPC have been designed and implemented on the linearized time‐variant model to control the robot. The superiority of the robust predictive control method over the stable method has been discussed in terms of safety and optimal performance considering wheel slip. Finally, based on experimental tests, it has been found that the robust predictive controller is more effective than stable control when the surface is slippery and there is an obstacle in front of the robot. However, in a case where the wheel slip is neglectable, SMPC can be a better choice in presence of obstacles due to the lower computational cost. [ABSTRACT FROM AUTHOR]

In this research, we analyze the real data in the NAND Flash memory industry using a control chart. There are thousands of electrical measures for each NAND Flash memory chip. We monitor these data through a control chart to ensure that the manufacturing process is in control. For better interpretability, we apply a univariate control chart technique to each variable. However, most existing control charts, such as the EWMA chart, do not include between-subgroup variations but only within-subgroup variations. They often obtain too narrow control limits for some variables, which leads too many subgroups to fall outside the control limits. To overcome this issue, we apply a control chart under a mixed-effects modeling framework to include both within-subgroup and between-subgroup variations. Additionally, the EWMA chart assumes that all the items are normally distributed; however, we frequently encounter that a normal assumption is violated. To overcome this limitation, we apply a robust approach based on a nonparametric sign chart. Furthermore, we introduce a p-value combination method to increase the statistical power for the gradual change detection of a statistical process. Our study show that the proposed control chart can efficiently monitor the real data in the NAND Flash memory industry. [ABSTRACT FROM AUTHOR]

Considering the advantages of economic benefit and cost reduction by using rank-one control, we investigated the problem of robust regional eigenvalue assignment using rank-one control for undamped gyroscopic systems. Based on the orthogonality relation, we presented a method for solving partial eigenvalue assignment problems to reassign partial undesired eigenvalues accurately. Since it is difficult to achieve robust control by assigning desired eigenvalues to precise positions with rank-one control, we assigned eigenvalues within specified regions to provide the necessary freedom. According to the sensitivity analysis theories, we derived the sensitivity of closed-loop eigenvalues to parameter perturbations to measure robustness and proposed a numerical algorithm for solving robust regional eigenvalue assignment problems so that the closed-loop eigenvalues were insensitive to parameter perturbations. Numerical experiments demonstrated the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H ∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H ∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H ∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the uncertainty, i.e., the discrepancy between a nominal model and the true dynamics, with pre-computable estimation error bounds, and a robust Riemannian energy condition for computing the control signal. Under certain conditions, the controller guarantees exponential trajectory convergence during the learning transients, while learning can improve robustness and facilitate better trajectory planning. Simulation results validate the efficacy of the proposed control architecture. [ABSTRACT FROM AUTHOR]

This paper investigates state-of-the-art modeling and control techniques for the robust line-of-sight pointing control of an optical payload on-board a stratospheric balloon, to meet stringent pointing requirements in the context of astronomy missions. Previous experience shows that the pointing performance of such systems is essentially limited by the rejection of the natural pendulum-like oscillations of the flight chain. This observation justifies the need for a model that accurately predicts such flight conditions that cannot be replicated in laboratory, and for a systematic methodology addressing the line-of-sight controller design to reject these flexible dynamics excited by wind disturbances. Moreover, it is sought to ensure robust stability and performance to the parametric uncertainties inherent to balloon-borne systems, such as complex balloon's properties or release of ballast throughout the flight, especially since experimental validation is limited. In this paper, a first dynamical model of the complete system is proposed, based on Lagrangian mechanics; the comparison with flight data show that the frequency content of the platform's motion is accurately predicted. Then, a multibody approach is applied to derive a second model taking into account the parametric uncertainties with the Linear Fractional Transformation representation. Finally, the control of the line-of-sight is tackled as a robust, structured H 2 / H ∞ problem that improves the performance with regard to traditional PID controller. A μ -sensitivity analysis is also proposed to identify the most sensitive parameters and verify the performance. [ABSTRACT FROM AUTHOR]

The superior performance of stocks trading near their 52-week-high prices has been attributed to underreaction stemming from investors' anchoring bias. The author tests and finds robust supporting evidence for another source of underreaction—investor inattention to continuous information. Using an intuitive and readily implementable measure of information discreteness, he documents that the returns to the 52-week-high strategy are significantly higher among stocks with past continuous information as compared to stocks with past discrete information. The result is evident in risk-adjusted returns, in non-January months, among large and small stocks, across alternative holding and sample periods, and robust to controls for common return predictors in cross-sectional regressions. Further corroborating evidence is obtained from a strategy based on the 52-week-low price that earns significant returns among stocks with past continuous information but insignificant returns among stocks with past discrete information. Investors can achieve substantial performance gains by implementing the 52-week-high and 52-week-low strategies among stocks with past continuous information. [ABSTRACT FROM AUTHOR]

This paper aims to address the problem of event‐based adaptive robust control for a class of nonaffine nonlinear systems against actuator faults and unknown deception attacks. Firstly, different from the widely used relative threshold triggering mechanism, an improved event‐triggered mechanism with the time‐varying function threshold is devised for effectively saving more communication costs between controller and actuator. Then, based on a class of modified filter‐based coordinate transformations and the improved event‐triggered mechanism, the intermediate virtual control laws are devised to construct actual controllers, which not only guarantees robust stabilization of the system but also relaxes the universal assumptions on control coefficients and attack weights. Besides, the design challenges resulting from the presence of coupling terms in the nonaffine nonlinear system are successfully overcome by virtue of mean value theorem and the property of fuzzy basis function. Theoretical analysis proves that all signals of the closed‐loop system are semiglobally uniformly ultimately bounded (SUUB) and the Zeno behavior can be averted. Finally, the effectiveness of the proposed strategy is verified via comprehensive simulation results. [ABSTRACT FROM AUTHOR]

There exist multiple uncertainties in the belt conveyor system. To reduce the system errors and save the control energy, the uncertainties need to be described and compensated accurately. In this paper, the fuzzy set theory is applied to establish the fuzzy dynamical model. Then, an exponential control is designed and a noncooperative‐game based optimization approach is proposed to solve out the Nash equilibrium. The novel control with optimal parameters including the adaptive tension force are applied to render the system uniform boundedness and uniform ultimate boundedness. Finally, the simulations are performed for verification. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of perturbation suppression and tracking control of nonlinear fractional order systems with time‐varying delay. To simplify the control process for such systems, the Takagi–Sugeno fuzzy model is used to describe the nonlinear fractional order delayed systems. Additionally, a modified repetitive controller is applied to develop an output feedback technique for the proposed systems. To ensure system stability, the study establishes new delay‐dependent stabilization conditions using a novel Lyapunov–Krasovskii functional (LKF). These stability conditions are expressed in formation of linear matrix inequalities (LMIs) by adopting a frequency‐distributed model transformation and some slack matrices. The proposed method demonstrates that the system output can track the desired input with ignorable error, while effectively rejecting disturbances. Finally, the efficacy of the proposed method is evaluated through simulations of the modified Chua circuit and the hydraulic turbine governing system (HTGS). [ABSTRACT FROM AUTHOR]

This paper aims at the study of the general decay synchronization of state and spatial diffusion coupled delayed reaction-diffusion memristive neural networks (CDRDMNNs). At first, we present the general decay synchronization concept for the considered network based on ψ-type stability, then we go deeply into the establishment of general decay synchronization criterion of state CDRDMNNs by excogitating a proper controller and utilizing an opportune Lyapunov functional, thereby gain a sufficient condition for attaining general decay synchronization of state CDRDMNNs. Subsequently, we devote ourselves to investigating the general decay synchronization of spatial diffusion CDRDMNNs and derive an adequate condition for realizing the general decay synchronization of this type of network. Next, some sufficient conditions which ensure robust general decay synchronization of state CDRDMNNs and spatial diffusion CDRDMNNs with uncertain parameters are concluded by utilizing various types of inequality techniques. Ultimately, the efficacy of the acquired general decay synchronization results are certified via numerical simulations of two examples. [ABSTRACT FROM AUTHOR]