Search

The control problem of unmanned aerial vehicle (UAV) suspended transportation systems (STSs) has become a popular topic in the field of both theoretical investigation and engineering applications. With the increasing complexities of the mission environment, it is difficult to accomplish a transportation task with a single UAV, and using multiple UAVs to collaboratively suspend and transport loads has become increasingly significant. Motivated by this, this work presents an event-triggered formation optimization-based control scheme for a multi-quadrotor STS, which can reduce the power consumption of the UAV formation by dynamically adjusting the UAV formation while achieving load trajectory tracking. In the developed scheme, the STS is decoupled into the suspended load and the multiple UAV subsystems based on the principle of timescale separation. It allows to design independently the virtual force command of the load and the actual control law, which are designed by using the sliding mode-like control method. Afterward, the formation optimization algorithm is presented. The obtained virtual force command of the suspended load would provide the receding horizon formation for the UAVs to minimize the sum of cable forces and the power consumption, where the constraints of cable tension, cable break, and collision avoidance are considered. Furthermore, the event-triggering mechanism is appointed for the formation optimization algorithm, and it reduces the solving frequency of the optimization algorithm and the computational burden of the system. Both the stability of the closed-loop system and the boundedness of the number of event triggers in finite time are confirmed by using the Lyapunov function method. Numerical simulations verify that the developed method can effectively reduce the energy consumption of multiple UAVs and reliably accomplish the trajectory tracking of the suspended load. Therefore, this work offers certain theoretical significance and practical application value for promoting the development of multiple UAV STSs. In addition, this work on the formation control of the STS expands the application scope of UAV suspension and enhances its transportation efficiency. In terms of cooperation suspension, how to generate and adjust the UAV formation to pass through narrow space still maintains profound research significance. Future work may consider the attitude loop of the suspended load, which plays an irreplaceable role in performing transportation tasks. Meanwhile, using a distributed communication protocol rather than a centralized one could distribute computational burdens to each UAV, which would better enhance engineering practicability.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

This paper investigates the path planning method of unmanned aerial vehicle (UAV) in three-dimensional map. Firstly, in order to keep a safe distance between UAV and obstacles, the obstacle grid in the map is expanded. By using the data structure of octree, the octree map is constructed, and the search nodes is significantly reduced. Then, the lazy theta∗ algorithm, including neighbor node search, line-of-sight algorithm and heuristics weight adjustment is improved. In the process of node search, UAV constraint conditions are considered to ensure the planned path is actually flyable. The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes. Heuristic weight adjustment strategy is employed to control the precision and speed of search. Finally, the simulation results show that the improved lazy theta∗ algorithm is suitable for path planning of UAV in complex environment with multi-constraints. The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes.

Game theory can be applied to the air combat decision-making problem of multiple unmanned combat air vehicles (UCAVs). However, it is difficult to have satisfactory decision-making results completely relying on air combat situation information, because there is a lot of time-sensitive information in a complex air combat environment. In this paper, a constraint strategy game approach is developed to generate intelligent decision-making for multiple UCAVs in complex air combat environment with air combat situation information and time-sensitive information. Initially, a constraint strategy game is employed to model attack-defense decision-making problem in complex air combat environment. Then, an algorithm is proposed for solving the constraint strategy game based on linear programming and linear inequality (CSG-LL). Finally, an example is given to illustrate the effectiveness of the proposed approach.

Abstract In this paper, a control scheme based on state prediction is proposed for a class of nonlinear systems subject to input delay and external disturbance. Firstly, as for constant input delay, an improved control scheme that combining state prediction with dynamic surface control (DSC) technique is proposed, which can compensate for the input delay completely. Secondly, by considering the constant input delay and external disturbance, an effective controller based on state prediction, DSC technique and disturbance observer is presented to tackle the disturbed input‐delayed nonlinear systems. Thirdly, as for time‐varying input delay and external disturbance, a control scheme that includes state prediction, DSC technique, input saturation and sliding mode disturbance observer is further proposed to tackle the tracking control for the addressed nonlinear systems. Besides, some theorems are given to ensure that all signals in the closed‐loop systems can be semi‐globally ultimately uniformly bounded (UUB) under the proposed control schemes. Finally, by utilising the dynamic model of quadrotor unmanned aerial vehicle (UAV), some simulations are presented to illustrate the effectiveness of our proposed results.

This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.

Carbon nanotubes (CNTs) possess remarkable nonlinear optical properties; a particular application is to function as a mode locker used in ultrafast fiber lasers to produce ultrashort optical pulses. Various types of CNT saturable absorbers (SAs) and ultrafast fiber lasers have been demonstrated. In this review, typical fabrication process and development of CNT SAs are discussed and we highlight the recent investigation and progress of state-of-the-art ultrafast fiber lasers covering GHz, bidirectional ultrafast fiber lasers, vectorial mode fiber lasers, comb systems, and mode-locking dynamics. Our perspectives of ultrafast fiber lasers based on CNT SAs are given finally.

In this paper, the flight control of an unmanned aerial vehicle (UAV), quadrotor, in the presence of high-order disturbances is presented. Since during flight, UAV faces enormous, and various kinds of disturbance, the effect of such disturbances becomes vital for consideration during the development of disturbance observer (DO) based tracking control scheme. To obtain the desired tracking performance, standard sliding mode control (SMC) method is utilized while for disturbance estimation, DO based on Simpson's approximation is developed and incorporated with SMC. Furthermore, in this paper, both matched and mismatched disturbances are considered. Hence, a matrix associated with disturbances is invoked in the system model. To show the effective and desired control performance of developed disturbance observer-based control (DOBC) scheme, extensive simulations are conducted, followed by presenting the results in the paper.

In this paper, the problem of deep-stall recovery will be studied for the aircraft without longitudinal static stability. A new longitudinal short-period deep-stall model is established in the presence of time-varying distributed delays. Based on bifurcation analysis method, the effects of actuator fault on deep stall are analyzed in depth. Considering the system uncertainty, actuator fault, input saturation and unsteady disturbance, a finite-time prescribed performance deep-stall recovery law is designed according to the bifurcation analysis results. Further, to avoid the singularity problem in traditional finite-time control, an improved finite-time control law is developed. Stability of the closed-loop system is proved by common Lyapunov functional method. And simulations are given to illustrate the validity of the proposed deep-stall recovery control scheme.

In human-computer gaming scenarios, the autonomous decision-making problem of an unmanned combat air vehicle (UCAV) is a complex sequential decision-making problem involving multiple decision-makers. In this paper, an autonomous maneuver decision-making method for UCAV that considers the partially observable states of Human (the adversary) is proposed, building on a game-theoretic approach. The maneuver decision-making process within the current time horizon is modeled as a game of Human and UCAV, which significantly reduces the computational complexity of the entire decision-making process. In each established game decision-making model, an improved maneuver library that contains all possible maneuvers (called the continuous maneuver library) is designed, and each of these maneuvers corresponds to a mixed strategy of the established game. In addition, the unobservable states of Human are predicted via the Nash equilibrium strategy of the previous decision-making stage. Finally, the effectiveness of the proposed method is verified by some adversarial experiments.

In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external disturbances. Auxiliary systems are built to handle the input saturation. The neural networks are applied to approximate the uncertain terms. The control scheme combining integral backstepping and sliding mode control is developed in position and attitude subsystems, respectively. In the closed-loop system, the boundedness of the signals is proved by means of the Lyapunov theory. The simulation demonstrates that the approach has good robustness and tracking performance.

To intercept a maneuvering target in a two-dimensional plane, the inverse optimal guidance law based on zero effort miss (ZEM) and disturbance observer (DO) is studied in this paper. Firstly, the relative kinematics equation is simplified to obtain the missile-target ZEM and its dynamics. In order to enhance the robustness of the inverse optimal guidance law, the integral of the ZEM is introduced as a new state to form an augmented system with the original system based on the idea of proportional integral (PI) control. Then, the target maneuver acceleration is assumed as the unknown external disturbance of the guidance augmented system, which is estimated by the DO. Based on the estimated value of DO and the backstepping method, the inverse optimal guidance law is designed to reduce the adverse effect of the disturbance on the guidance system. Finally, simulations are designed to verify the effectiveness of the inverse optimal guidance method based on DO.

This paper addresses the automatic carrier landing problem in the presence of deck motion, carrier airwake disturbance, wind shears, wind gusts, and atmospheric turbulences. By transforming the 6-DOF aircraft model into an affine dynamic with angle of attack controlled by thrust, the equations associated to the resultant disturbances are deduced; then, a deck motion prediction block (based on a recursive-least squares algorithm) and a tracking differentiator-based deck motion compensation block are designed. After obtaining the aircraft reference trajectory, the backstepping control method is employed to design a novel automatic carrier landing system with three functional parts: a guidance control system, an attitude control system, and an approach power compensation system. The design of the attitude subsystem involves the flight path control, the control of the attitude angles, and the control of the angular rates. To obtain convergence performance for the closed-loop system, the backstepping technique is combined with sliding mode-based command differentiators for the computation of the virtual commands and extended state observers for the estimation of the disturbances. The global stability of the closed-loop architecture is analyzed by using the Lyapunov theory. Finally, simulation results verify the effectiveness of the proposed carrier landing system, the aircraft reference trajectory being accurately tracked.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

BackgroundThe management of diabetes-related complications accounts for a large share of total carbon dioxide equivalent (CO2e) emissions. We assessed whether improving diabetes control in people with type 2 diabetes reduces CO2e emissions, compared with those with unchanging glycemic control.MethodsUsing the IQVIA Core Diabetes Model, we estimated the impact of maintaining glycated hemoglobin (HbA1c) at 7% (53 mmol/mol) or reducing it by 1% (11 mmol/mol) on total CO2e/patient and CO2e/life-year (LY). Two different cohorts were investigated: those on first-line medical therapy (cohort 1) and those on third-line therapy (cohort 2). CO2e was estimated using cost inputs converted to carbon inputs using the UK National Health Service’s carbon intensity factor. The model was run over a 50-year time horizon, discounting total costs and quality adjusted life years (QALYs) up to 5% and CO2e at 0%.ResultsMaintaining HbA1c at 7% (53 mmol/mol) reduced total CO2e/patient by 18% (1546 kgCO2e/patient) vs 13% (937 kgCO2e/patient) in cohorts 1 and 2, respectively, and led to a reduction in CO2e/LY gain of 15%–20%. Reducing HbA1c by 1% (11 mmol/mol) caused a 12% (cohort 1) and 9% (cohort 2) reduction in CO2e/patient with a CO2e/LY gain reduction of 11%–14%.ConclusionsWhen comparing people with untreated diabetes, maintaining glycemic control at 7% (53 mmol/mol) on a single agent or improving HbA1c by 1% (11 mmol/mol) by the addition of more glucose-lowering treatment was associated with a reduction in carbon emissions.

This paper develops a composite tracking controller for a two-link manipulator. Because a completely accurate dynamic model for a manipulator is unavailable in practice and because a manipulator generally suffers from all types of unknown external disturbances from its working environment, a high-order disturbance observer (HODO) is introduced. Both the internal and external disturbances are regarded as a total compound disturbance to be estimated by the HODO. To achieve excellent tracking performance of a self-designed manipulator platform for any bounded initial state, a sliding-mode controller is proposed with disturbance estimation compensation. The bounded stability of the closed-loop control system is analyzed with the Lyapunov method. Finally, simulation results and experimental results are provided to demonstrate the effectiveness of the proposed tracking controller.

Abstract Background Sudden cardiac death (SCD), a leading cause of global mortality, most commonly arises from a substrate of cardiac ischemia, but requires an additional trigger. Diabetes mellitus (DM) predisposes to SCD even after adjusting for other DM-linked cardiovascular pathology such as coronary artery disease. We previously showed that remote liver ischemia preconditioning (RLIPC) is highly protective against cardiac ischemia reperfusion injury (IRI) linked ventricular arrhythmias and myocardial infarction, via induction of the cardioprotective RISK pathway, and specifically, inhibitory phosphorylation of GSK-3β (Ser 9). Methods We evaluated the impact of acute streptozotocin-induced DM on coronary artery ligation IRI-linked ventricular arrhythmogenesis and RLIPC therapy in rats. Results Post-IRI arrhythmia induction was similar in nondiabetic and DM rats, but, unexpectedly, DM rats exhibited lower incidence of SCD during reperfusion (41 vs. 100%), suggesting uncontrolled hyperglycemia does not acutely predispose to SCD. RLIPC was highly effective in both nondiabetic and DM rats at reducing incidence and duration of, and increasing latency to, all classes of ventricular tachyarrhythmias. In contrast, atrioventricular block (AVB) was highly responsive to RLIPC in nondiabetic rats (incidence reduced from 72 to 18%) but unresponsive in DM rats. RISK pathway induction was similar in nondiabetic and DM rats, thus not explaining the DM-specific resistance of AVB to therapy. Conclusions Our findings uncover important acute DM-specific differences in responsiveness to remote preconditioning for ventricular tachyarrhythmias versus AVB, which may have clinical significance given that AVB is a malignant arrhythmia twofold more common in human diabetics than nondiabetics, and correlated to plasma glucose levels >10 mmol/L.

Carbon nanotubes (CNTs) possess both remarkable optical properties and high potential for integration in various photonic devices. We overview, here, recent progress in CNT applications in fibre optics putting particular emphasis on fibre lasers. We discuss fabrication and characterisation of different CNTs, development of CNT-based saturable absorbers (CNT-SA), their integration and operation in fibre laser cavities putting emphasis on state-of-the-art fibre lasers, mode locked using CNT-SA. We discuss new design concepts of high-performance ultrafast operation fibre lasers covering ytterbium (Yb), bismuth (Bi), erbium (Er), thulium (Tm) and holmium (Ho)-doped fibre lasers.

In this paper, an adaptive impedance control combined with disturbance observer (DOB) is developed for a general class of uncertain robot manipulators in discrete time. The impedance control is applied to realize the interaction force control of robot manipulators in unknown, time-varying environments. The optimal reference trajectory is produced by impedance control, and the impedance parameters are achieved using Q-learning technique, which is implemented based on trajectory tracking errors. The position control with DOB of robot manipulators is implemented to track the virtual desired trajectory, and the DOB is designed to compensate for unknown compounded disturbance function by bounding both tracking error inputs and compounded disturbance inputs in a permitted control region, of which the compounded disturbance function is taken into account of all uncertain terms and external disturbances. The appropriate DOB parameters are selected applying linear matrix inequalities (LMIs) method. Both the impedance control and the bounded DOB control can well guarantee semiglobal uniform boundness of the closed-loop robot systems based on Lyapunov analysis and Schur complement theory. Simulation results are performed to test and verify effectiveness of the investigated combining adaptive impedance control with DOB.

A backstepping-based robust adaptive fault tolerant control (FTC) scheme is proposed for the altitude and attitude dynamic model of the medium-scale unmanned autonomous helicopter (UAH) subjected to external disturbance and actuator fault. The external disturbance and actuator fault are integrated into the medium-scale UAH altitude and attitude dynamic model. Since the actuator fault is uncertain in occurring time instant, the adaptive control approach is adopted to compensate for its negative effect. Meanwhile, the upper bound of external disturbance is estimated and the estimation is employed to design the controller. Based on the backstepping control technique, a robust adaptive FTC law is developed, which guarantees the desired system stability as well as the satisfactory tracking property. Simulation studies verify the effectiveness of the designed controller.

This paper studies the problem of stabilization control for a class of continuous-time fractional systems subject to external constant disturbances based on a disturbance observer. To tackle unknown disturbances, a fractional disturbance observer is introduced for the fractional system. A stabilization controller incorporating the disturbance observer is then designed for the fractional system. Under the developed control scheme and linear program method, the sufficient conditions are proposed to guarantee that the fractional systems with the closed-loop positivity are asymptotically stable. Numerical simulation results further demonstrate the effectiveness of the results.

The fault tolerant control problem is investigated for a class of uncertain networked control systems (NCSs) subject to actuator faults and actuator saturation in this paper. System parameter uncertainties are considered and a sector condition method is employed to deal with the saturation problem. Based on the network transmission environment, the NCSs can be modeled as a class of saturated discrete-time systems with time-varying delays and actuator faults. By using Lyapunov-Krasovskii (L-K) stability theory, a sufficient condition for the fault tolerant controller design of NCSs is derived in the form of linear matrix inequalities. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

In this article, a sliding mode control scheme is proposed for a quadrotor in the presence of an exogenous disturbance. A nonlinear sliding mode surface is constructed based on the estimate output of a disturbance observer to reject the effect of the unknown disturbance in the quadrotor. The desired control performance is achieved by bringing the state from unstable state to stable ones. To show the effectiveness of the developed control scheme, simulation results are provided for illustration of the designed controller based on disturbance observer.

In this paper, an adaptive sliding mode tracking control scheme is developed for the medium-scale unmanned autonomous helicopter with system uncertainties and external unknown disturbances. A simplified mathematical model is established, which is divided into position subsystem and attitude subsystem. The uncertainty term of the system is handled by the inherent approximation ability of the neural network. The sliding model control scheme under the backstepping frame is developed for tackling disturbances. The stability of the simplified system is proved by using the Lyapunov theory, and the tracking errors are guaranteed to be uniformly bounded. Numerical simulation results show that the proposed control strategy is effective.

In this article, a prescribed performance-based adaptive neural network control scheme is proposed for an uncertain small-scale unmanned helicopter system subject to input saturations and output constraints. The radial basis function neural networks are employed to approximate system uncertainties. A nonlinear disturbance observer is developed to tackle input saturation. Meanwhile, the prescribed performance function is adopted to deal with output constraint. The closed-loop system stability is rigorously proved using Lyapunov synthesis. Finally, simulation results for unmanned helicopter system are presented to demonstrate the effectiveness of developed tracking control scheme using disturbance observer and radial basis function neural network.

In this article, an adaptive neural tracking controller is designed for near-space vehicles with stochastic disturbances and unknown parametric uncertainties. Based on the great nonlinear function approximation capability of neural networks, the unknown system uncertainties are tackled using the radial basis function neural networks. Furthermore, on the basis of stochastic Lyapunov stability theory, an adaptive tracking control scheme is developed for near-space vehicle which can guarantee the closed-loop system stability. Under the developed adaptive neural control scheme, all closed-loop system signals are bounded in the sense of probability, and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are provided to illustrate the proposed adaptive neural control scheme that can guarantee the satisfactory tracking performance for the attitude motion of the near-space vehicle with stochastic disturbances.

In this article, a sliding mode control scheme for the autopilot system of a ship is presented, which also incorporates actuator saturation. Under the designed controller, the actuator doesn’t enter its saturation mode. Meanwhile, the stability of the closed-loop system can be guaranteed for the ship with input saturation. Fuzzy logic is used to handle gain to avoid the chattering effect, and it is compared with saturation function. The control system for the mathematical model of a scale replica of an “Esso Osaka” tanker is simulated in time domain for the effectiveness of the controller. The simulation results show that the proposed controller is more efficient as compared to the traditional and saturation function methods.

The nano quadrotor is a nonlinear multi-input and multi-output system with strong coupling, which causes difficulties in control law design. In order to achieve a favorable performance, an extended state observer–based nonlinear cascade proportional–integral–derivative controller is proposed in this article. First, the nano quadrotor platform is built, and the dynamic model is established. Second, a novel and practical measuring method is given to obtain model parameters. Then, based on the active disturbance rejection control method, the design procedure of the extended state observer–based nonlinear cascade proportional–integral–derivative controller is presented. In the developed controller, a tracking differentiator is involved to extract the signals of gyroscope, and extended state observer is used to estimate the disturbance. To obtain a better performance of tracking differentiator and extended state observer, a systematic parameter-tuning method is studied. Finally, simulation results are given to demonstrate the efficiency of the proposed controller.

Preconditioning stimuli conducted in remote organs can protect the heart against subsequent ischemic injury, but effects on arrhythmogenesis and sudden cardiac death (SCD) are unclear. Here, we investigated the effect of remote liver ischemia preconditioning (RLIPC) on ischemia/reperfusion (I/R)-induced cardiac arrhythmia and sudden cardiac death (SCD) in vivo, and determined the potential role of ERK/GSK-3βsignaling.Male Sprague Dawley rats were randomized to sham-operated, control, or RLIPC groups. RLIPC was induced by alternating four 5-minute cycles of liver ischemia with 5-minute intermittent reperfusions. To investigate I/R-induced arrhythmogenesis, hearts in each group were subsequently subjected to 5-minute left main coronary artery ligation followed by 20-minute reperfusion. RLIPC reduced post-I/R ventricular arrhythmias, and decreased the incidence of SCD >threefold. RLIPC increased phosphorylation of cardiac ERK1/2, and GSK-3β Ser9 but not Tyr216 post-I/R injury. Inhibition of either GSK-3β (with SB216763) or ERK1/2 (with U0126) abolished RLIPC-induced antiarrhythmic activity and GSK-3β Ser9 and ERK1/2 phosphorylation, leaving GSK-3β Tyr216 phosphorylation unchanged.RLIPC exerts a powerful antiarrhythmic effect and reduces predisposition to post-IR SCD. The underlying mechanism of RLIPC cardioprotection against I/R-induced early arrhythmogenesis may involve ERK1/2/GSK-3β Ser9-dependent pathways.

A bounded controller is proposed for a class of uncertain discrete time-delay systems with nonlinearity and disturbance based on state estimator and disturbance observer technique. A state estimator is developed to estimate the unmeasured system state vector. Suppose that the disturbance is generated by an exogenous system; a disturbance observer is designed to estimate the unknown disturbance. The parameters of the state estimator and the disturbance observer are calculated by solving linear matrix inequalities (LMIs). By applying the outputs of the state estimator and the disturbance observer, the sufficient condition for the existence of the bounded controller is derived based on an appropriate Lyapunov function candidate. Under the developed bounded controller, the stability of the closed-loop system can be guaranteed. Simulation examples are provided to show the effectiveness of the proposed bounded control scheme.

Wing rock is a highly nonlinear phenomenon when the aircraft suffers undesired roll-dominated oscillatory at high angle of attack (AOA). Considering the strong nonlinear and unsteady aerodynamic characteristics, an uncertain multi-input and multi-output (MIMO) nonlinear wing rock model is studied, and system uncertainties, unsteady aerodynamic disturbances and externaldisturbancesareconsideredinthedesignofwingrockcontrollaw. Tohandletheproblemof multipledisturbances,arobustcontrolschemeisproposedbasedontheextendedstateobserver(ESO) and the radial basis function neural network (RBFNN) technique. Considering that the effectiveness of actuators are greatly decreased at high AOA, the input saturation problem is also handled by constructing a corresponding auxiliary system. Based on the improved ESO and the auxiliary system, a robust backstepping control law is proposed for the wing rock control. In addition, the dynamic surface control (DSC) technique is introduced to avoid the tedious computations of time derivatives for the virtual control laws in the backstepping method. The stability of the closed-loop system is guaranteed via rigorously Lyapunov analysis. Finally, simulation results are presented to illustrate the effectiveness of the ESO and the proposed wing rock control approach.

A robust control scheme using composite nonlinear feedback (CNF) technology is proposed to improve tracking control performance for the uncertain linear system with input saturation and unknown external disturbances. A disturbance observer is presented to estimate the unknown disturbance generated by a linear exogenous system. The designed gain matrix of the disturbance observer is determined by solving linear matrix inequalities (LMIs). Based on the output of the designed disturbance observer, a robust CNF controller including a linear feedback control item and a nonlinear item is developed to follow the desired tracking signals. The linear feedback controller is designed using LMIs and the stability of the closed-loop system is proved via rigorous Lyapunov analysis. Finally, the extensive simulation results are presented to illustrate the effectiveness of the proposed control scheme.

In this paper, robust adaptive tracking control is proposed for the underwater robot in the presence of parametric uncertainties and unknown external disturbances. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using parameter adaptation, backstepping control and variable structure based techniques, the robust adaptive tracking control is presented for underwater robots to handle the uncertainties, saturation and dead-zone characteristics of actuators. Actuator nonlinearities comprising of dead-zone and saturation are explicitly considered in the tracking control design. Under the proposed tracking control, semi-global uniform boundedness of the closed-loop signals is guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed robust adaptive tracking control.

Abstract Background Chronic kidney disease (CKD) is more prevalent in Taiwan than in most countries. This population-based cohort study evaluated the dementia risk associated with CKD. Methods Using claims data of 1,000,000 insured residents covered in the universal health insurance of Taiwan, we selected 37049 adults with CKD newly diagnosed from 2000–2006 as the CKD cohort. We also randomly selected 74098 persons free from CKD and other kidney diseases, frequency matched with age, sex and the date of CKD diagnosed. Incidence and hazard ratios (HRs) of dementia were evaluated by the end of 2009. Results Subjects in the CKD cohort were more prevalent with comorbidities than those in the non-CKD cohort (p Conclusions Patients with CKD could have an elevated dementia risk. CKD patients with comorbidity deserve attention to prevent dementia.

Abstract Optical integration is essential for practical application, but it remains unexplored for nanoscale devices. A newly designed nanocomposite based on ZnO semiconductor nanowires and Tb(OH)3/SiO2 core/shell nanospheres has been synthesized and studied. The unique sea urchin-type morphology, bright and sharply visible emission bands of lanthanide, and large aspect ratio of ZnO crystalline nanotips make this novel composite an excellent signal receiver, waveguide, and emitter. The multifunctional composite of ZnO nanotips and Tb(OH)3/SiO2 nanoparticles therefore can serve as an integrated nanophotonics hub. Moreover, the composite of ZnO nanotips deposited on a Tb(OH)3/SiO2 photonic crystal can act as a directional light fountain, in which the confined radiation from Tb ions inside the photonic crystal can be well guided and escape through the ZnO nanotips. Therefore, the output emission arising from Tb ions is truly directional, and its intensity can be greatly enhanced. With highly enhanced lasing emissions in ZnO-Tb(OH)3/SiO2 as well as SnO2-Tb(OH)3/SiO2 nanocomposites, we demonstrate that our approach is extremely beneficial for the creation of low threshold and high-power nanolaser.

Abstract Background Application of superparamagnetic iron oxide nanoparticles (SPIOs) as the contrast agent has improved the quality of magnetic resonance (MR) imaging. Low efficiency of loading the commercially available iron oxide nanoparticles into cells and the cytotoxicity of previously formulated complexes limit their usage as the image probe. Here, we formulated new cationic lipid nanoparticles containing SPIOs feasible for in vivo imaging. Methods Hydrophobic SPIOs were incorporated into cationic lipid 1,2-dioleoyl-3-(trimethylammonium) propane (DOTAP) and polyethylene-glycol-2000-1,2-distearyl-3-sn-phosphatidylethanolamine (PEG-DSPE) based micelles by self-assembly procedure to form lipid-coated SPIOs (L-SPIOs). Trace amount of Rhodamine-dioleoyl-phosphatidylethanolamine (Rhodamine-DOPE) was added as a fluorescent indicator. Particle size and zeta potential of L-SPIOs were determined by Dynamic Light Scattering (DLS) and Laser Doppler Velocimetry (LDV), respectively. HeLa, PC-3 and Neuro-2a cells were tested for loading efficiency and cytotoxicity of L-SPIOs using fluorescent microscopy, Prussian blue staining and flow cytometry. L-SPIO-loaded CT-26 cells were tested for in vivo MR imaging. Results The novel formulation generates L-SPIOs particle with the average size of 46 nm. We showed efficient cellular uptake of these L-SPIOs with cationic surface charge into HeLa, PC-3 and Neuro-2a cells. The L-SPIO-loaded cells exhibited similar growth potential as compared to unloaded cells, and could be sorted by a magnet stand over ten-day duration. Furthermore, when SPIO-loaded CT-26 tumor cells were injected into Balb/c mice, the growth status of these tumor cells could be monitored using optical and MR images. Conclusion We have developed a novel cationic lipid-based nanoparticle of SPIOs with high loading efficiency, low cytotoxicity and long-term imaging signals. The results suggested these newly formulated non-toxic lipid-coated magnetic nanoparticles as a versatile image probe for cell tracking.

Studies have associated the human respiratory syncytial virus which causes seasonal childhood acute bronchitis and bronchiolitis (CABs) with climate change and air pollution. We investigated this association using the insurance claims data of 3,965,560 children aged ≤ 12 years from Taiwan from 2006−2016. The monthly average incident CABs increased with increasing PM2.5 levels and exhibited an inverse association with temperature. The incidence was 1.6-fold greater in January than in July (13.7/100 versus 8.81/100), declined during winter breaks (February) and summer breaks (June−August). The highest incidence was 698 cases/day at <20 °C with PM2.5 > 37.0 μg/m3, with an adjusted relative risk (aRR) of 1.01 (95% confidence interval [CI] = 0.97−1.04) compared to 568 cases/day at <20 °C with PM2.5 < 15.0 μg/m3 (reference). The incidence at ≥30 °C decreased to 536 cases/day (aRR = 0.95, 95% CI = 0.85−1.06) with PM2.5 > 37.0 μg/m3 and decreased further to 392 cases/day (aRR = 0.61, 95% CI = 0.58−0.65) when PM2.5 was <15.0 μg/m3. In conclusion, CABs infections in children were associated with lowered ambient temperatures and elevated PM2.5 concentrations, and the high PM2.5 levels coincided with low temperature levels. The role of temperature should be considered in the studies of association between PM2.5 and CABs.

Background: Studies comparing mental disorder risks between women with breast cancer and cervical cancer are lacking. This study compared risks of developing anxiety and depression between women with breast cancer (BC cohort) and women with cervical cancer (CC cohort) using insurance claims data of Taiwan., Methods: From the 2000 to 2016 data, we identified a BC cohort and BC controls (N = 96,862) and a CC cohort and CC controls (N = 26,703), matched by propensity scores. Incident mental disorders and the Cox method estimated the related cancer cohort to control cohort hazard ratios (HRs), and 95% confidence intervals (CIs) were estimated by the end of 2016., Results: Compared to the CC cohort, the BC cohort had slightly higher incident anxiety (15.9 versus 15.5 per 1,000 person-years) and depression (6.92 vs. 6.28 per 1,000 person-years). These mental disorders were higher in respective cancer cohorts than controls. The BC cohort to BC control adjusted HRs of anxiety and depression were 1.29 (95% CI = 1.25-1.33) and 1.78 (95% CI = 1.69-1.87), respectively. The corresponding adjusted HRs for the CC cohort were 1.12 (95% CI = 1.06-1.18) and 1.29 (95% CI = 1.18-1.41). The combined incidence rates of both disorders were 1.4-fold greater in the BC cohort than in BC controls (22.8 vs. 15.8 per 1,000 person-years), and 1.2-fold greater in the CC cohort than in the CC controls (21.7 vs. 18.3 per 1,000 person-years)., Conclusion: Women with breast cancer or cervical cancer are at an elevated likelihood of developing anxiety and depression disorders. These incident disorders are slightly higher in those with breast cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Sung, Mou, Liao, Wang and Shieh.)

Background: Evidences have shown that the stroke risk associated with long-term exposure to particulate matter with an aerodynamic diameter of ≤2.5 μm (PM 2.5 ) varies among people in North America, Europe and Asia, but studies in Asia rarely evaluated the association by stroke type. We examined whether long-term exposure to PM 2.5 is associated with developing all strokes, ischemic stroke and hemorrhagic stroke., Methods: The retrospective cohort study consisted of 1,362,284 adults identified from beneficiaries of a universal health insurance program in 2011. We obtained data on air pollutants and meteorological measurements from air quality monitoring stations across Taiwan in 2010-2015. Annual mean levels of all environmental measurements in residing areas were calculated and assigned to cohort members. We used Cox proportional hazards models to estimate hazard ratio (HR) and 95% confidence interval (CI) of developing stroke associated with 1-year mean levels of PM 2.5 at baseline in 2010, and yearly mean levels from 2010 to 2015 as the time-varying exposure, adjusting for age, sex, income and urbanization level., Results: During a median follow-up time of 6.0 years, 12,942 persons developed strokes, 9919 (76.6%) were ischemic. The adjusted HRs (95% CIs) per interquartile range increase in baseline 1-year mean PM 2.5 were 1.03 (1.00-1.06) for all stroke, 1.06 (1.02-1.09) for ischemic stroke, and 0.95 (0.89-1.10) for hemorrhagic stroke. The concentration-response curves estimated in the models with and without additional adjustments for other environmental measurements showed a positively linear association between baseline 1-year mean PM 2.5 and ischemic stroke at concentrations greater than 30 μg/m 3 , under which no evidence of association was observed. There was an indication of an inverse association between PM 2.5 and hemorrhagic stroke, but the association no longer existed after controlling for nitrogen dioxide or ozone. We found similar shape of the concentration-response association in the Cox regression models with time-varying PM 2.5 exposures., Conclusion: Long-term exposure to PM 2.5 might be associated with increased risk of developing ischemic stroke. The association with high PM 2.5 concentrations remained significant after adjustment for other environmental factors., (© 2022. The Author(s).)

Purpose: This retrospective cohort study was conducted to determine the glaucoma risk associated with metabolic disease (MetD) using insurance claims data of Taiwan., Methods: From the database, we identified patients with newly diagnosed hypertension, diabetes and/or hyperlipidemia from the years 2000 to 2002 as the MetD cohort (N = 42,036) and an age-gender-diagnosis-date matched control cohort without MetD with a two-fold sample size than that of the MetD cohort. Both cohorts were followed until the development of glaucoma, death, or withdrawal, until 31 December 2013. The incidence of glaucoma, and the Cox method estimated hazard ratio (HR) of glaucoma were calculated. Results showed that the incidence of glaucoma was two-fold higher in the MetD cohort than in the controls (1.99 versus 0.99 per 1000 person-years), with an adjusted HR of 1.66 (95% CI: 1.50-1.85). The glaucoma incidence was higher in patients with diabetes than those with hypertension and hyperlipidemia (2.38 versus 1.95 and 1.72 per 1000 person-years, respectively). The incidence increased to 5.67 per 1000 person-years in patients with all three comorbidities, with an aHR of 4.95 (95% CI: 2.35-10.40). We also found higher incidence rates of primary open-angle glaucoma and primary angle-closure glaucoma with aHRs of 2.03 and 1.44, respectively. It was concluded that glaucoma risk increased with the number of MetD. Health providers need to monitor patients with MetD to prevent glaucoma.

Background: Osteoarthritis (OA) is more prevalent in women with age. Comorbidities are prevalent in OA patients. In this study, we conducted a follow-up study to evaluate whether women with OA are at an increased risk of ischemic stroke using insurance claims data of Taiwan., Methods: We identified 13,520 women with OA aged 20-99 newly diagnosed in 2000-2006 and 27,033 women without OA for comparison, frequency matched by age and diagnosis date. Women with baseline history of hypertension and other disorders associated with stroke were excluded for this study. Incident ischemic stroke was assessed by the end of 2013. A nested case-control analysis was used to identify factors associated with the stroke in the OA cohort., Results: The incidence rate of ischemic stroke in the OA cohort was 1.5-fold greater than that in comparisons (1.93 versus 1.26 per 1,000 person-years), with an adjusted hazard ratio of 1.34 (95% confidence interval [CI], 1.09-1.66). The nested case-control analysis showed that stroke cases were twice as likely to develop hypertension during the follow-up period than controls without stroke. The ischemic stroke risk was significantly associated with hypertension (odds ratio [OR] 1.84; 95% CI, 1.37-2.46) and atrial fibrillation (OR 2.25; 95% CI, 1.24-4.09). Ischemic stroke was not associated with the use of non-steroidal anti-inflammatory drugs or aspirin., Conclusion: Women with OA are at an elevated risk of ischemic stroke. A close monitoring of hypertension, atrial fibrillation, and other stroke related comorbidities is required for stroke prevention for OA patients.

Cancer is increasing in rate globally and is leading cause of death among no communicable chronic diseases (NCDs) after cardiovascular disease (CVD). Most of the research focuses on the risk of occupational injury, job stress, mental illness, substance abuse and workplace safety in physicians and nurses. However, fewer studies have investigated the risk of cancer in pharmacists. We compared the matched general population to investigate the risk of cancer in pharmacists in Taiwan. Data were obtained from the Health and Welfare Data Science Center of the Ministry of Health and Welfare in Taiwan. We established a pharmacist group that included 11,568 pharmacists and selected a 4-fold comparison ( n = 46,272) for the non-clinics comparison group, frequency being matched by age, gender and Charlson Comorbidity Index (CCI) score. The pharmacists had a lower but non-significant risk of all cancer (Adjusted hazard ratio [aHR] = 0.96; 95% confidence interval [CI] = 0.85-1.07) compared with the general population. Female pharmacists had a higher risk of cancer than male pharmacists ([aHR] = 1.23; 95% CI = 1.06-1.43). Pharmacists had higher risks of breast cancer in females (aHR = 1.68; 95% CI = 1.35-2.08) and of prostate cancer in males (aHR = 2.18; 95% CI = 1.35-2.08) when compared with the general population. Occupational risk factors could play a role, but they were not evaluated. These epidemiological findings require additional studies to clarify cancer risk mechanisms in pharmacists.