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.

Adaptive Backstepping Control for Uncertain Systems with Compound Nonlinear Characteristics

An adaptive backstepping multi-sliding mode approximation variable structure control scheme is proposed for a class of uncertain nonlinear systems.An actuator model with compound nonlinear characteristics is established based on the model decomposition method.The unmodeled dynamic term of the radial basis function neural network approximation system is presented.The Nussbaum gain design technique is utilized to overcome the problem that the control gain is unknown.The adaptive law estimation is used to estimate the upper boundary of neural network approximation and uncertain interference.The adaptive approximate variable structure control effectively weakens the control signal chattering while enhancing the robustness of the controller.Based on the Lyapunov stability theory,the stability of the entire control system is proved.The main advantage of the designed controller is that the compound nonlinear characteristics are considered and solved.Finally,simulation results are given to show the validity of the control scheme.

Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.

Adaptive L_2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L 2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set. Moreover, the generalized matching conditions are also relaxed in the proposed L 2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.

Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network（NN） parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.

Adaptive Backstepping Sliding Mode Control for Nonlinear Systems with Input Saturation

An adaptive backstepping sliding mode control is proposed for a class of uncertain nonlinear systems with input saturation.A command filtered approach is used to prevent input saturation from destroying the adaptive capabilities of neural networks (NNs).The control law and adaptive updating laws of NNs are derived in the sense of Lyapunov function,so the stability can be guaranteed even under the input saturation.The proposed control law is robust against the disturbance,and it can also eliminate the impact of input saturation.Simulation results indicate that the proposed controller has a good performance.

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.

基于GA-BP网络的数控机床动态误差预测研究

动态误差是高速高精度数控机床的重要误差源,针对实际加工过程中动态误差对工件精度影响较大的问题,提出了一种基于遗传算法优化的反向传播(GA-BP)神经网络以预测动态误差。首先,为了提高神经网络对动态误差的预测精度,从线性特征与非线性特征两方面对动态误差影响因素进行了深入分析,确定了神经网络输入输出参数;然后,采用了遗传算法对BP神经网络进行了优化,建立了动态误差模型,获得了最优网络学习参数,从而实现了对动态跟随误差的精准预测;之后,采用三次样条插值的方法对理想轨迹与实际轨迹之间的轮廓误差进行了计算,有效提高了轮廓误差估算精度;最后,采用了五轴数控机床进行了实验,对模型的有效性进行了验证。研究结果表明:所建神经网络模型可以精准预测机床反向越冲特性对轮廓误差的影响,各轴的动态误差预测精度为±3μm,复杂轨迹轮廓误差预测精度为±1.5μm。实验结果验证了所建模型的可靠性,为后续机床动态误差建模与控制研究提供了一定的参考价值。

基于Rayleigh-BP模型的压电驱动系统迟滞建模与前馈控制

针对可调谐半导体激光器压电驱动系统的迟滞非线性,提出了一种基于Rayleigh-BP模型的建模及控制方法。利用空间扩展法建立了Rayleigh-BP率相关迟滞模型,该模型实现了对压电驱动系统的率相关迟滞非线性的精准预测;利用逆向算法求解了Rayleigh模型的逆模型,并将该模型与BP神经网络结合,设计了前馈控制器对系统进行补偿;对前馈控制方法进行了仿真与实验验证。结果表明,建立的Rayleigh-BP模型具有较高的精度,在10 Hz时均方根误差仅为0.0469μm。前馈控制方法可以明显提高系统输出的线性度,在40 Hz时仿真结果均方根误差为0.0274μm,线性相关系数R 2为0.99992;在30 Hz时实验结果均方根误差为0.0506μm,线性相关系数R 2达到了0.99955,极大降低了迟滞现象。

基于BP网络的非线性广义预测学习控制器

本文提出了一种基于BP神经网络的非线性广义预测学习控制器，它由一个BP网络构成．在整个学习与控制过程中，首先根据被控对象的输出与BP网络的学习输出之间的误差来修改网络的权值，以逐步建立被控对象的合理的多步预报模型;然后，根据网络的多步预报输出序列与设定值序列的偏差修改控制律．学习过程与控制过程交替进行．仿真结果证实了该控制器的有效性，为实现非线性系统的控制提供了一条可行途径．

无模型机械臂BP神经网络状态观测及反演跟踪控制

针对摩擦阻尼及模型参数不确定的情况,运用反演控制设计策略,针对多连杆机械臂提出了一种基于神经网络观测器的无模型轨迹跟踪控制方法。运用带有修正项的自适应BP神经网络观测器对不可测状态量进行观测,同时对系统模型进行在线逼近。在此基础上设计了基于观测状态和逼近模型的反演跟踪控制器,Lyapunov稳定性理论证明了该控制器能够保证跟踪误差的有界和闭环系统中所有信号的有界。跟踪给定轨迹的仿真实验证明了该方法的有效性。

一类非线性系统基于Backstepping的自适应鲁棒神经网络控制

针对一类未知非线性系统提出了一种基于Backstepping的自适应神经网络控制方法,放松了满足匹配条件,要求神经网络逼近误差的边界已知等一些限制性的假设。扩展了自适应backstepping和自适应神经控制的适用范围,整个闭环系统表明是最终一致有界的。

基于ELM的一类不确定性纯反馈非线性系统的Backstepping自适应控制

针对一类不确定性纯反馈非线性动力学系统,在中值定理、Backstepping控制的基础上,提出一种基于极限学习机(ELM)的自适应神经控制方法。ELM随机确定单隐层前馈网络(SLFNs)的隐含层参数,仅需调整网络的输出权值,能以极快的学习速度获得良好的推广性。在每一步的Backstepping设计中,应用ELM网络对子系统的未知非线性项进行在线逼近,通过Lyapunov稳定性分析设计的权值参数自适应调节律,可以保证闭环非线性系统所有信号半全局最终一致有界,系统的输出收敛于期望轨迹的很小邻域内。将所设计的控制方法应用于化工过程中的连续搅拌反应釜(CSTR)非线性系统实例中,仿真结果表明了控制方法的有效性。

采用非线性模块的BP神经网络PID水位预测控制

因非线性时变控制系统造成的滞后,压水堆核电厂蒸汽发生器(SG)水位控制会引起的较大惯性或耦合,对此提出一种基于非线性模块的BP神经网络PID控制预测模块。采用BP神经网络模型作为非线性预测模块,调用SG水位PID控制运行数据作为非线性预测模块的训练样本;提取BP神经网络模型的输出数据作为BP神经网络PID的输入数据,多重优化BP神经网络的权值及阈值,缩小修正差值逼近系统整定值;构建SG水位非线性模块的预测数值与系统运行数据之间的非线性映射关系,修正带有预测判断方向的阈值,实现在线动态调整输出PID各参数的最优值。实验结果表明,预测模块可有效缩短系统的稳定阶跃响应周期,具有较高的计算精度。

一类非线性系统基于Backstepping的自适应稳定控制

针对一类未知高阶非线性系统 ,提出了一种基于 Backstepping和神经网络的自适应稳定控制方法。利用 RBF神经网络逼近未知非线性函数 ,不需要满足匹配条件 ,基于 Backstepping方法调节网络权值。在控制律中引入非线性衰减项和 σ-修正项保证了网络权值的稳定性 ,阻止了参数漂移。通过 Lyapunov直接方法 ,证明了整个闭环系统的最终一致有界性。该方法扩展了自适应Backstepping和自适应 NN控制的应用范围 ,适于并行计算 。

一类输入受限的不确定非线性系统自适应Backstepping变结构控制

针对一类输入受限的不确定非线性系统,提出了一种自适应Backstepping变结构控制器设计方法。建立了受未知非线性特征约束的执行器故障模型,可以描述系统存在死区、齿隙、饱和、滞回等输入受限情形以及可能发生的执行器失效、卡死等故障情形。设计径向基函数神经网络补偿未建模动态项,引入一阶低通滤波器避免了Backstepping控制中的计算复杂性问题。自适应近似变结构控制能够有效削弱控制信号抖振。理论分析和仿真实验结果证明,提出的自适应鲁棒控制律能够在输入受限的情况下自适应地调节控制输入,使得闭环系统稳定且满足控制性能要求。

一类含有滞回故障非线性系统Backstepping变结构控制

为解决执行器发生未知故障情况下不确定非线性系统的控制问题,采用一种自适应Backstepping变结构控制方法,建立了包括滞回非线性和失效、卡死等故障类型的非线性执行器模型.通过径向基函数(radial basis function,RBF)神经网络逼近系统中的未知非线性函数项,神经网络参数根据自适应律实时调整,保证了逼近效果.结合动态面控制,避免了Backstepping控制中的计算复杂性问题.引入的自适应补偿项消除了系统建模误差和不确定干扰的影响,理论分析证明了闭环系统半全局一致最终有界,仿真结果验证了该方法的有效性.

Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

In this study,a humanoid prototype of 2-DOF(degrees of freedom)lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton.To improve the detection accuracy of the humanrobot interaction torque,a BPNN(backpropagation neural networks)is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor.Meanwhile,the backstepping controller is designed to realize the exoskeleton's passive position control,which means that the person passively adapts to the exoskeleton.On the other hand,a variable admittance controller is used to implement the exoskeleton's active followup control,which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance.To improve the wearable comfortable effect,serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters.Finally,the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.