Adaptive Neural Network Control for Fixed-Wing UAV With Disturbance Observer Under Switching Disturbance.

The adaptive neural network (NN) control for the fixed-wing unmanned aerial vehicle (FUAV) under the unmodeled dynamics and the time-varying switching disturbance (TVSD) is investigated in this article. To better describe the TVSD induced by the change in the flight area of the FUAV, a switching augmented model (SAM) based on the known information about the TVSD is proposed first. The parameter adaptation technique is used to estimate the related TVSD.

Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed.

Flexible Performance-Based Control for Nonlinear Systems Under Strong External Disturbances.

Addressing external disturbances has been a critical issue for control design to ensure reliable operation of systems. This article investigates the tracking control problem for the uncertain nonlinear systems with the strong external disturbance and the prescribed performance. The flexible performance-based control scheme is developed by introducing an external disturbance criterion into the prescribed performance.

Event-Triggered Fractional-Order Tracking Control for an Uncertain Nonlinear System With Output Saturation and Disturbances.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances.

Event-Triggering-Learning-Based ADP Control for Post-Stall Pitching Maneuver of Aircraft.

In this article, an improved event-triggering-learning (ETL)-based adaptive dynamic programming (ADP) method for the post-stall pitching maneuver of aircraft is proposed to achieve the robust optimal control and reduce the computational cost. First, a feedforward control with the nonlinear disturbance observer (NDO) technique is designed to attenuate the adverse effects caused by the unsteady aerodynamic disturbances. Subsequently, the ADP method with a critic neural network which is constructed to approximate the value function in the Hamilton-Jacobi-Bellman equation is employed to conduct the optimal control of aircraft.

Flight and Vibration Control of Flexible Air-Breathing Hypersonic Vehicles Under Actuator Faults.

The issue of modeling and fault-tolerant control (FTC) design for a class of flexible air-breathing hypersonic vehicles (FAHVs) with actuator faults is investigated in this article. Different from previous research, the shear deformation of the fuselage is considered, and an ordinary differential equations-partial differential equations (ODEs-PDEs) coupled model is established for the FAHVs. A feedback control is proposed to ensure flight stable and an adaptive FTC method is designed to deal with actuator faults while suppressing the system's vibrations.

Adaptive NN Tracking Control for Uncertain MIMO Nonlinear System With Time-Varying State Constraints and Disturbances.

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers.

Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems.

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The multigradient recursive RL algorithm is used to avoid the local optimal problem that may exist in the gradient descent scheme. Different from the existing event-triggered control results, a new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability.

Adaptive Neural Safe Tracking Control Design for a Class of Uncertain Nonlinear Systems With Output Constraints and Disturbances.

In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it.

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

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.

Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information.

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.

Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems.

This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl-Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm.

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm.

An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems.

This article proposes a new implicit function-based adaptive control scheme for the discrete-time neural-network systems in a general noncanonical form. Feedback linearization for such systems leads to the output dynamics nonlinear dependence on the system states, the control input, and uncertain parameters, which leads to the nonlinear parametrization problem, the implicit relative degree problem, and the difficulty to specify an analytical adaptive controller. To address these problems, we first develop a new adaptive parameter estimation strategy to deal with all uncertain parameters, especially, those of nonlinearly parameterized forms, in the output dynamics.

Distributed Fault Estimation and Fault-Tolerant Control of Interconnected Systems.

This article studies the distributed fault estimation (DFE) and fault-tolerant control for continuous-time interconnected systems. Using associated information among subsystems to design the DFE observer can improve the accuracy of fault estimation of the interconnected systems. Based on the static output feedback (SOF), the global outputs of the interconnected systems are used to construct a distributed fault-tolerant control (DFTC).

Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed.

Relative Degrees and Implicit Function-Based Control of Discrete-Time Noncanonical Form Neural Network Systems.

This paper studies the relative degrees of discrete-time neural network systems in a general noncanonical form, and develops a new feedback control scheme for such systems, based on implicit function theory and feedback linearization. After time-advance operation on output of such systems, the output dynamics nonlinearly depends on the control input. To address this issue, we use implicit function theory to define the relative degrees, and to establish a normal form.

Kcne4 deletion sex-specifically predisposes to cardiac arrhythmia via testosterone-dependent impairment of RISK/SAFE pathway induction in aged mice.

Sudden cardiac death (SCD) is associated with both electrical and ischemic substrates, and is a major cause of ischemic heart disease mortality worldwide. Male sex predisposes to SCD but the underlying mechanisms are incompletely understood. KCNE4, a cardiac arrhythmia-associated potassium channel β-subunit, is upregulated by 5α-dihydrotestosterone (DHT).

Robust control of post-stall pitching maneuver based on finite-time observer.

This article presents a robust finite-time maneuver control scheme for the longitudinal attitude dynamic of the aircraft with unsteady aerodynamic disturbances and input saturation. To efficiently eliminate the influence of unsteady aerodynamic disturbances, nonlinear finite-time observers are developed. Despite the existence of the nonlinearity and the coupling between aircraft states and unsteady aerodynamic disturbances, the proposed observers can still precisely estimate the unmeasurable unsteady aerodynamic disturbances in finite time.

Remote ischemic preconditioning differentially attenuates post-ischemic cardiac arrhythmia in streptozotocin-induced diabetic versus nondiabetic rats.

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).

Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer.

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented.

Remote Liver Ischemic Preconditioning Protects against Sudden Cardiac Death via an ERK/GSK-3β-Dependent Mechanism.

Background: 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.

Methods/results: Male Sprague Dawley rats were randomized to sham-operated, control, or RLIPC groups.

Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone.

In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations.

Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics.