Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis bias or consequences exists. As such, views organismal distribution and the ecosystems they make up may be incorrect, undermining countless ecological evolutionary studies. Using 742 million records 374 900 species, we explore global impacts biases related taxonomy, accessibility, ecotype data type across terrestrial marine systems. Pervasive sampling observation exist animals, with only...

This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, radial basis function NN method is utilized tackle the unknown saturations, dead zones, uncertainties. Then, based on backstepping approach, two controllers with update laws are employed stabilize like-position loop subsystem like-posture subsystem, respectively. With introduced laws, uniform ultimate...

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input multioutput affine unknown nonlinear discretetime systems in presence of bounded disturbances. The design has two entities, an action network that is designed to produce optimal signal evaluates performance network. estimates cost-to-go function which tuned recursive equations derived from heuristic dynamic...

In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ system transformation technique to transform the original constrained (in sense restrictions) into an equivalent unconstrained one, whose stability sufficient solve problem. It shown that tracking achieved without violation constraint. More specifically, can shape...

In this paper, we present a novel tracking controller for class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original constrained (in sense signal) control system is transformed into output-feedback problem an unconstrained affine in normal form. As result, stabilization sufficient to ensure constraint satisfaction. It subsequently shown that achieved without violation predefined Therefore, are capable quantifying performance bounds as...

Due to the fast development of distributed energy resources and demand-side response management, agents in electricity markets are becoming more proactive, which boosts peer-to-peer (P2P) market mechanisms. However, our knowledge, none existing works considers clearing both reserve via a P2P mechanism order compensate for uncertainty originating from renewable generation allocate cost induced by fairly. In this article, novel joint is proposed, where each agent can negotiate with neighboring...

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm dual neural network (NN) approximation structure. First, an NN identifier designed to obviate the requirement of complete knowledge system and critic employed value function. Then, law computed based on information from NN, so that actor not needed. In particular, design...

This paper presents a novel state-feedback control scheme for the tracking of class multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, law consisting robust integral neural network (NN) output plus sign error feedback multiplied an adaptive gain is introduced. The NN in learns system online manner, while residual reconstruction errors disturbances are overcome by signal. Since both signal included integral, continuity input...

This paper deals with the power capture control of variable-speed wind energy conversion systems. The objective is to optimize by tracking desired output. Arbitrary steady-state performance achieved in sense that error guaranteed converge any predefined small set. In addition, maximize capture, transient enhanced such convergence rate can be larger than an arbitrary value, which further limits maximum overshoot. First, adaptive controller designed for case where known aerodynamic torque...

In dc microgrids, load power sharing and bus voltage regulation are two common control objectives. this article, a consensus-based algorithm is presented to achieve proportional of weighted geometric mean voltages in microgrids with ZIP (constant impedance, constant current, power) loads simultaneously. By using the virtue Laplacian matrices undirected connected graphs, lemma derived assist stability analysis microgrids. Thus, sufficient condition that stabilizes system established....

Direct current (dc) microgrids have been widely used in many critical applications. Such systems avoid unnecessary ac/dc conversions and can simplify control design. To achieve high-performance of such system, advanced algorithm needs to be designed. This paper presents a novel decentralized output constrained for single-bus dc microgrids. The objectives are realize bus voltage, user-defined load sharing, circulating minimization. Unlike conventional algorithms, the guarantee not only...

This brief presents a novel power control strategy for variable-speed wind turbines equipped with doubly fed induction generators (DFIGs). The objective is to optimize the extracted from while regulating stator reactive meet grid requirements. First, in order power, an adaptive technique designed drive electromagnetic torque follow its reference generated by maximum point tracking algorithm. Subsequently, aiming at satisfying requirements on side, controller proposed manipulate given desired...

In this paper, an output-constrained control algorithm is presented for the consensus of a class unknown nonaffine multiagent systems (MASs) with partially directions. Our contribution includes step forward beyond usual stabilization result to show that outputs agents remain within user-defined time-varying constraints. To achieve new results, error transformation technique established generate equivalent MAS from original one. Stabilization and transformed agent states ensure both...

In modern control systems, most algorithms, especially system-level ones, are implemented in discretetime (DT) with digital controllers and communications. this paper, a distributed DT algorithm is developed to achieve proportional load current sharing average bus voltage regulation DC microgrids. order reduce the communication requirement among controllers, periodic event-triggered (PET) proposed by introducing novel PET condition. Since condition detected periodically, Zeno phenomenon...

The tracking control of a wastewater treatment process (WWTP) is considered. highly nonlinear, with strong coupling, difficult to model mathematically, and the operation subject unknown disturbances. We address this multivariable problem by applying direct heuristic dynamic programming (dHDP)-based reinforcement learning control. goal track desired reference dissolved oxygen (DO) concentration 5th aerobic zone ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...

This article studies the adaptive fuzzy fault tolerant control (FTC) problem for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unknown directions in presence time-varying asymmetric output constraints. Our contribution includes step forward beyond usual FTC results to exhibit that system nonsquare and square MIMO is uniformly bounded against actuator faults by novel methodology without detection unit, as well stay preselected To obtain new results, an equivalent...

With the increasing expansion of wind power installed capacity, deployed farms are becoming an attractive target for malicious entities. The geographical scale farm, remoteness its location, flat logical control network, and fragile protocols make it vulnerable to cyber attacks. False data injection (FDI) attacks on value rotor speed may lead reduced generation efficiency, overload drive-train, even shutdowns potentially costly equipment damage turbines. Therefore, in this article, we...

This study focuses on the adaptive fuzzy predefined-time attitude tracking control problem for rigid spacecraft with inertia uncertainties, external disturbances, and state constraints. In design, logic systems are adopted to approximate unknown nonlinear dynamics, a quadratic-fraction barrier Lyapunov function is introduced ensure that predefined constraints not violated. Compared existing dynamic surface approaches, novel filter an controller presented, such error can converge small region...

With the vigorous development of Industry 4.0, industrial Big Data has turned into core element Industrial Internet Things. As one most fundamental and indispensable components in cyber-physical systems (CPS), intelligent anomaly detection is still an essential challenging issue. However, with network, there may exist unknown types attacks, which are difficult to collect. Facing one-class intrusion scenario that collected training data only includes normal state, broad learning system...

External intrusion incidents pose a severe threat to pipeline security in energy transportation. In response this, distributed optical fiber sensing technology has been widely studied the field of safety monitoring recent years. However, diversity environment along long-distance makes vibration signal complex and changeable, which significantly limits recognition accuracy practical applications, resulting numerous false positives. To address above issues, we transform detection into...

In this paper, we present a novel adaptive consensus algorithm for class of nonlinear multiagent systems with time-varying asymmetric state constraints. As such, our contribution is step forward beyond the usual stabilization result to show that states agents remain within user defined, bound. To prove new results, original system transformed into one. Stabilization and are sufficient ensure networked without violating predefined A single neural network (NN), whose weights tuned online, used...