Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control （SMC）. Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.

Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.

Backstepping sliding mode control for uncertain strict-feedback nonlinear systems using neural-network-based adaptive gain scheduling

A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.

An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems

A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.

ON THE STABILITY OF CELLULAR NEURAL NETWORKS WITH FEEDBACK MODE

Cellular Neural Networks (CNN) with feedback mode and M×N cells are equivalent to a network which possesses 2M×N cells, a neighborhood with mirror-like structure, space-variant templates and without feedback as well as without input templates. The stability of the CNN with feedback mode and transformations with the neighborhood of mirror-like structure are discussed.

Signal prediction based on empirical mode decomposition and artificial neural networks

In view of the usefulness of Empirical Mode Decomposition （EMD）, Artificial Neural Networks （ ANN）, and Most Relevant Matching Extension （MRME） methods in dealing with nonlinear signals, we pro- pose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.

Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak

Prediction of disruptions caused by locked modes using the Back-Propagation （BP） neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation （RMPs） locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

This paper presents a robust sliding mode controller for a class of unknown nonlinear discrete-time systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional theory into the sliding-mode technique is used and a neural-network based sliding mode control scheme is proposed. Because of the novality of Chebyshev Neural Networks (CNNs), that it requires much less computation time as compare to multi layer neural network (MLNN), is preferred to approximate the unknown system functions. By means of linear matrix inequalities, a sufficient condition is derived to ensure the asymptotic stability such that the sliding mode dynamics is restricted to the defined sliding surface. The proposed sliding mode control technique guarantees the system state trajectory to the designed sliding surface. Finally, simulation results illustrate the main characteristics and performance of the proposed approach.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

In this paper,a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network.The sensor fault and the system input uncertainty are assumed to be unknown but bounded.The radial basis function (RBF) neural network is used to approximate the sensor fault.Based on the output of the RBF neural network,the sliding mode observer is presented.Using the Lyapunov method,a criterion for stability is given in terms of matrix inequality.Finally,an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

FUZZY NEURAL NETWORK CONTROL FOR VIBRATION WAVEFORM SYSTEM OF MOLD

Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.

Decentralized direct adaptive neural network control for a class of interconnected systems

The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized di- rect adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local informa- tion. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

Changes in the default mode network in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users

The default mode network is associated with senior cognitive functions in humans. In this study, we performed independent component analysis of blood oxygenation signals from 14 heroin users and 13 matched normal controls in the resting state through functional MRI scans. Results showed that the default mode network was significantly activated in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users, and an enhanced activation signal was observed in the right inferior parietal Iobule （P 〈 0.05, corrected for false discovery rate）. Experimental findings indicate that the default mode network is altered in heroin users.

An linear matrix inequality approach to global synchronisation of non-parameter perturbations of multi-delay Hopfield neural network

In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications.

老采空区地表沉降预测合理监测模式分析

为研究老采空区沉降监测数据时间间隔对预测精度的影响,本文利用某老采空区地表沉降监测点实测沉降数据,在等时间间隔、非等时间间隔两种情况下建立两种方案、六种沉降预测模式,采用长短期记忆神经网络(LSTM)预测模型对老采空区地表沉降进行预测,以平均绝对误差(MAE)和平均绝对百分比误差(MAPE)作为评价指标,分析不同时间间隔监测数据对预测精度的影响。结果表明,在总监测时长不变的情况下,预测精度随平均监测间隔时长的增长呈先增高后降低的趋势,即并非监测间隔越短,预测精度越高,而是在相应监测间隔范围内存在预测精度最优值。研究成果可为老采空区监测方案设计及沉降预测模式提供借鉴和指导。

An Optimized Damage Identification Method of Beam Using Wavelet and Neural Network

An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。

基于深度迁移学习的车辆悬架高频异常振动故障诊断

在车辆悬架故障诊断过程中,深度学习故障诊断模型在面对少量样本数据时模型训练效果不佳,导致诊断模型的接收者操作特性曲线(receiver operating characteristic,ROC)的曲线下面积(area under curve,AUC)较小的问题,利用经验模态分解(empirical mode decomposition,EMD)方法,对采集的车辆悬架高频振动信号进行分解处理,根据每个经验模态分量(intrinsic mode functions,IMF)的能量,提取高频异常振动故障特征,构建了基于深度迁移学习的诊断模型;以深度卷积神经网络算法为基础,对小样本特征矢量信息进行故障知识迁移处理,通过参数微调更新权值,优化故障诊断模型。实验结果表明:优化后模型的AUC值为0.89,模型故障诊断结果具有较高准确性。