Introduction to Novel Adaptive

This article proposes a novel adaptive super-twisting sliding mode control scheme with a time-delay estimation technique (ASTSMC-TDE) to control the yaw angle of a single ducted-fan unmanned aerial vehicle system.

In this article, we propose a novel adaptive event-triggered strategy and apply to the multi-area power system to deal with the load frequency control (LFC) problem with network-induced delay and stochastic sensor faults based on the discrete-time sliding mode control (DSMC) technique.

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic.

To achieve resilient secondary voltage restoration, a novel adaptive fault tolerant control scheme is proposed.

A novel adaptive terminal sliding mode control (NATSMC) method is proposed to improve the speed control performance of permanent magnet synchronous motor (PMSM) under external disturbance.

Novel adaptive distributed robust event-triggered controllers without/with finite-time extended state observer (FTESO) are developed based on adaptive terminal sliding mode (TSM) control method to save control update times and reduce chattering times simultaneously.

In the first scheme, a novel adaptive fault-tolerant distributed fixed-time control protocol is developed.

The novel adaptive finite-time and fixed-time control schemes are implemented, respectively, which can not only ensure that the stochastic chaos synchronization of PMSMs can be achieved in a fast rate, but also determine the control gains successfully(not necessary to set them in advance).

Third, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control law based on TSMDO is proposed to ensure the convergence of attitude tracking errors to zero.

In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads.

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed.

This paper is concerned with the tracking control problem for a class of switched nonaffine stochastic nonlinear systems in completely nonaffine form and nonlower-triangular structure, with unknown backlash-like hysteresis involved, and a novel adaptive neural tracking control scheme, based on backstepping design, is proposed.

In this work a novel adaptive neuro-fuzzy inference system model has been developed for the prediction of the intrinsic mechanical properties of various cellulosic natural fibers to enhance their selection for better green composite materials.

To securely store this information in the cloud, we introduce a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) for data protection to improve and determine the degree of security like breaches, data integrity, etc.

A novel adaptive event-triggered scheme (AETS) that considers the effect of the energy-constrained, nonperiodic DoS attacks on the communication network is proposed to reduce the usage of system resources and adapt the variation of the plant state, and the model of closed-loop control system is established in the framework of time-delay systems and switched systems.

In order to reduce the unnecessary transmissions, a novel adaptive event-triggered scheme is employed to determine whether the sensor measurements should be transmitted or not.

In this paper, we provide a novel adaptive neural network backstepping control scheme for a special variable stiffness actuator (VSA) based on lever mechanisms with saturation inputs, output constraints and disturbances is presented here.

This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty.

By using the dynamic surface control (DSC) and the backstepping technique, we establish a novel adaptive fuzzy finite-time pure-feedback switching control method.

Therefore, in this article, a novel adaptive switching dynamic surface control (DSC) strategy is first presented for fractional-order nonlinear systems in the nonstrict feedback form with unknown dead zones and arbitrary switchings.

Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems.

Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur.

This paper presents a novel adaptive multiple-model predictive control (MPC) scheme for Four-Wheel Independent Drive (4WID) autonomous vehicles with a holistic structure.

A novel adaptive prescribed performance model predictive control (APPMPC) scheme for inductively coupled plasma (ICP) generation system is developed in this paper.

This paper studies a novel adaptive fractional-order non-singular terminal sliding mode (FO-NTSM) control strategy for omnidirectional mobile robot manipulator (OMRM) with unknown parameters and external disturbances.

In this research, a novel adaptive non-singular terminal sliding mode control (ANTSMC) is proposed for finite-time attitude and altitude tracking of aircraft system considering the bounded disturbances.

In this study, automatic mining of high-quality rule set is handled as a multi-objective optimization problem due to the nature of the necessities and novel adaptive multi-objective intelligent search and optimization algorithms based on Grey Wolf Optimizer are proposed for this task.

To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed.

In this article, a novel adaptive multiobjective particle swarm optimization (MOPSO) is proposed on the basis of an evolutionary state estimation mechanism, which is used to detect the evolutionary environment whether in exploitation or exploration state.

Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined.

In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced.

This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation.

Then, based on the traditional event-triggered mechanism, we propose a novel adaptive event-triggered mechanism (AETM) in this paper, in which the threshold parameters can be adjusted dynamically according to the tracking performance.

A novel adaptive fault tolerant mechanism is presented to overcome the difficulty arising from the actuator faults and mismatched parameters, where the parameters estimators are generated from discrete state governed by the state dependent event-triggered mechanism.

Second, a novel adaptive fault-tolerant controller based on the fuzzy neural network (FNN) and nonsingular fast terminal sliding-mode (NFTSM) control scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures.

In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electro-hydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously.

In this paper, a novel adaptive sliding mode controller (ASMC) for the steer-by-wire (SbW) system on road vehicles is proposed.

Moreover, a novel adaptive-based sliding mode controller is presented to steer the system state onto the sliding manifold.

In this paper, a novel adaptive fuzzy neural network (FNN) control scheme is proposed for the trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of output constraints and input nonlinearities.

In order to exclude the requirements, an adaptive fuzzy neural network is integrated; yielding a novel adaptive fuzzy neural TOFTSMC (AFN-TOFTSMC).

Afterwards, a novel adaptive network fuzzy inference system (ANFIS)-based optimization method is introduced which aims at online tuning of virtual inductances (VI) in the islanded microgrids.

Based on finite/fixed-time control theories and in the context of command filtered control, two novel adaptive control laws are developed by which the vehicle can track the desired heading within settling time with all signals of the closed-loop system are uniformly bounded.

In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered.

In this work, we develop novel adaptive hybrid discontinuous Galerkin algorithms for second-order elliptic problems.

Different from traditional dynamic programming (DP) methods, this new theory serves as a fundamental tool to analyze the robustness of DP algorithms, and in par- ticular, to develop novel adaptive optimal control and reinforcement learning methods.

This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts.

This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts.

This paper also discusses a novel adaptive control mechanism employing a Kalman filter for the mobile inverted pendulum system (MIPS).

This algorithm is combined with PFC to obtain a novel adaptive control algorithm entitled Adaptive Predictive Functional Control (APFC).

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic.

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints.

A novel adaptive fuzzy fault-tolerant event-triggered controller is derived to guarantee the boundedness of closed-loop signals.

To address this challenge, a novel adaptive fuzzy proportional-integral (AFPI) control scheme is investigated in this paper.

Then, we design a novel adaptive controller, and under the action of this controller, synchronization criteria for the fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays are obtained by Lyapunov's stability theory and the differential equation theory with discontinuous right side.

In this study, a novel adaptive controller of the Omnidirectional Mobile Robot (OMR) is proposed for a certain class of unknown nonlinear dynamical systems.

In the first scheme, a novel adaptive fault-tolerant distributed fixed-time control protocol is developed.

To achieve resilient secondary voltage restoration, a novel adaptive fault tolerant control scheme is proposed.

The main objective of this article is to solve the trajectory following problem for lower limb exoskeleton robot by using a novel adaptive robust control method.

In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced.

Then, based on the traditional event-triggered mechanism, we propose a novel adaptive event-triggered mechanism (AETM) in this paper, in which the threshold parameters can be adjusted dynamically according to the tracking performance.

Therefore, a novel adaptive event-triggered H∞ control for singular systems with extended state observer is proposed.

A novel adaptive multi-dimensional Taylor network control scheme is provided via backstepping technique.

The network consists of two major parts: a novel adaptive multi-target weighting network (AMWNet) for DR lesions segmentation and a reverse data recovery network (RRN) to simulate the cycle perception in visual hierarchy.

A novel adaptive law that updates all the parameters of the FDO, including the control representative values, the center vectors, and the width vectors, is developed.

In addition, the novel adaptive law in AFAC can further shorten the duration of saturation and improve the convergent speed of tracking error via adjusting gain in AFAC according to saturation of actuator.

In this paper, a novel adaptive sliding mode controller (ASMC) for the steer-by-wire (SbW) system on road vehicles is proposed.

Thereafter, a novel adaptive sliding mode control was designed, to which all parameters describing involved uncertainties and disturbances were estimated by the adaptive update technique.

Recognizing that many scholars seek to assess performance relative to a control condition, we also develop and implement a novel adaptive algorithm that seeks to maximize the precision with which the largest treatment effect is estimated.

In this paper, we propose a novel adaptive algorithm for TT decomposition of streaming tensors whose slices are serially acquired over time.

We then introduce a novel adaptive sampling scheme that breaks the ``additive error barrier'' of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones.

In this study, we present a novel adaptive sampling method in the process of surrogate aerodynamic model construction based on the hyper-volume iteration (HVI) strategy.

In this paper, a novel adaptive backstepping nonsingular fast terminal sliding mode controller is investigated for the chaotic dynamic behavior of a third-order single machine infinite bus power system.

A novel adaptive backstepping control design based on tuning function is given, which successfully eliminates the effect of uncertainties.

Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems.

Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks.

The evolution of resistance to pesticides in agricultural systems provides an opportunity to study the fitness costs and benefits of novel adaptive traits.

This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts.

This article proposes an optimized direct instantaneous torque control (DITC) scheme for switched reluctance motor (SRM) drives with a novel adaptive dynamic commutation strategy, which can effectively reduce torque ripple and improve system efficiency.

Third, a novel adaptive dynamic surface control method based on Caputo fractional derivative definitions and fractional order filters is developed to overcome the “explosion of complexity” problem in the traditional backstepping design process and to determine the parameter update laws and control signals, concurrently.

To achieve high performance of spacecraft formation flying, a novel adaptive iterative learning disturbance observer based on adaptive notch filter is designed to estimate and compensate unknown multi-frequency disturbances.

In this letter, we propose a novel adaptive iterative stabilization method for physical human-(tele)robot interaction, named Discrete Windowed-Energy Variable Structure Passivity Signature Control (DWE-VSPSC).

Novel adaptive distributed robust event-triggered controllers without/with finite-time extended state observer (FTESO) are developed based on adaptive terminal sliding mode (TSM) control method to save control update times and reduce chattering times simultaneously.

In this article, a novel adaptive distributed secondary voltage controller for islanded microgrids (MGs) is proposed.

Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations.

In this brief, a novel adaptive filtering algorithm for block-sparse system identification called, Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed.

The issues of actuator fault reconstruction, sensor fault reconstruction, and state estimation are considered in this article for Markovian jump systems, using a novel adaptive observer approach.

This paper proposes a novel adaptive observer technique for estimating the state and disturbance of uncertain nonlinear systems.

The novel adaptive finite-time and fixed-time control schemes are implemented, respectively, which can not only ensure that the stochastic chaos synchronization of PMSMs can be achieved in a fast rate, but also determine the control gains successfully(not necessary to set them in advance).

In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered.

Besides, a novel adaptive tracking method for optimal regenerative torque limitation which does not need online motor parameters estimation is proposed.

A novel adaptive tracking control strategy without singularity is proposed for quadrotor systems subject to uncertain dynamic parameters.

In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries.

In this paper, along with a unified power management strategy for intra and inter microgrid power sharing in a hybrid AC-DC MMG system, a novel adaptive droop control algorithm is presented that dynamically changes droop coefficients of the VSCs to ensure maximum utilization of available resources in each microgrid under different operating modes.

We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks.

In this study, a novel adaptive learning deep belief network (ALDBN) with a series of growing and pruning algorithms is proposed to dynamically adjust its structure when ALDBN is utilized for extracting features.

In this work a novel adaptive neuro-fuzzy inference system model has been developed for the prediction of the intrinsic mechanical properties of various cellulosic natural fibers to enhance their selection for better green composite materials.

To securely store this information in the cloud, we introduce a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) for data protection to improve and determine the degree of security like breaches, data integrity, etc.

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems.