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In this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.

In the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.

The simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.

The research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.

This paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (D_O) concentration and nitrate nitrogen (S_No) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.

By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.

The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

The purpose of this paper is to present a sliding mode controller design method for a class of uncertain nonlinear systems with uncertainties and to demonstrate a recursive derivative estimation procedure for the derivatives of system outputs.

A recursive derivative estimation procedure for the derivatives of system outputs is demonstrated. Radial basis function (RBF) neural networks are used to approximate the uncertainties and filters are introduced to estimate the derivatives of system outputs step‐by‐step. The adaptive tuning rules of RBF neural network weight matrices are derived by the Lyapunov stability theorem, which guarantees filter errors and network weight errors are bounded and exponentially converge to a neighborhood of the origin globally. The sliding mode controller is designed based on the estimation for the derivatives of system outputs such that the sliding surface converges to zero and the system control input is bounded.

The sliding mode controller can make the system output track the desired output with arbitrarily small tracking error. The filter errors and network weight estimation errors can be made arbitrarily small, and all the system signals are bounded. The proposed method does not need the supper bounds of the unmatched uncertainties and their any order derivatives.

The system output and uncertainties are required to be sufficiently smooth in the proposed method. In practice, this condition is always satisfied generally.

This paper contains very useful advice for researchers on the sliding mode control and the use of neural networks.

The paper presents a new sliding mode controller design method based on recursive derivative estimation of system outputs using neural networks. The paper is aimed at theoretical researchers, especially those who have interest in sliding mode control, neural networks, adaptive techniques, and recursive estimation.

The purpose of this paper is to propose an adaptive output feedback controller using wavelet neural networks with nonlinear parameterization for unknown nonlinear systems with only system output measurement.

An error observer is used to estimate the tracking errors through output measurement information, and the wavelet neural networks are utilized to online approximate an unknown control input by adjusting their internal parameters.

The controller integrates an error observer and wavelet neural networks with nonlinear parameterization into adaptive control design and is derived in accordance with implicit function and mean value theorem. The adjustment mechanism for the parameters of the wavelet neural networks can be derived by means of mean value theorem and Lyapunov theorem, and the stability of the closed‐loop system can be guaranteed.

This paper utilizes the nonlinear parametric wavelet neural networks with estimate state inputs to obtain the adaptive control input for nonaffine systems with only system output measurement, and the nonlinear wavelet parameters can be adjusted efficiently.

The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon.

A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy.

The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy.

The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.

The purpose of this paper is to present an adaptive neuro‐sliding mode control scheme for uncertain nonlinear systems with Lyapunov approach.

The paper focuses on neural network (NN) adaptive control for nonlinear systems in the presence of parametric uncertainties. The plant model structure is represented by a NNs system. The essential idea of the online parametric estimation of the plant model is based on a comparison of the measured state with the estimated one. The proposed adaptive neural controller takes advantages of both the sliding mode control and proportional integral (PI) control. The chattering phenomenon is attenuated and robust performances are ensured. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed‐loop system and obtain good‐tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models.

Simulation results show that the adaptive neuro‐sliding mode control approach works satisfactorily for nonlinear systems in the presence of parametric uncertainties.

The proposed adaptive neuro‐sliding mode control approach is a mixture of classical neural controller with a supervisory controller. The PI controller is used to attenuate the chattering phenomena. Based on the Lyapunov stability theorem, it is rigorously proved that the stability of the whole closed‐loop system is ensured and the tracking performance is achieved.

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system.

In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft.

The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied.

For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage.

In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.