基于RBFNN的风电机组变桨距反推滑模控制

在风力发电变桨距优化控制问题的研究中,针对具有不确定性的非线性风电机组,设计了基于径向基函数神经网络(RBFNN)的风电机组变桨距反推滑模控制器.首先应用精确反馈线性化理论将原非线性系统模型进行全局线性化处理,再应用RBFNN对不确定项进行逼近,结合滑模控制和反推法,设计反推滑模控制器(BSMC),保证了高风速下风机的稳定性,抑制了不确定项对系统的影响,避免了传统反推法存在的计算复杂问题.通过与传统滑模控制器(SMC)进行仿真对比,结果表明,RBFNN-BSMC能够很好地稳定风电机组的输出功率,具有较强的鲁棒性.     

磁悬浮轴承系统的模糊滑模变结构控制研究

研究主动磁悬浮轴承转子位置高性能控制问题.针对主动磁悬浮轴承因受模型摄动与外界干扰等呈现出的复杂非线性导致传统滑模控制抖振严重的问题,为提高系统转子位置控制精度以及鲁棒性,提出用模糊滑模变结构控制来提高磁悬浮轴承系统控制精度与鲁棒性.算法采用等效滑模控制器来准确跟踪磁悬浮轴承转子位置,用模糊控制改善与消除滑模抖振问题.通过对单自由度主动磁悬浮轴承的建模进行仿真测试,结果表明,所设计的模糊滑模控制器能够在外界干扰下有效提高控制系统精度,精确跟踪轴承转子位置,并且控制性能比传统滑模控制好,具有实际应用价值.     

基于BP神经网络的汽包建模及其控制器设计

针对锅炉汽包系统的强耦合性和非线性及传统的PID控制方法存在控制精度低、调节时间长等问题,提出了利用基于数据的建模方法,对汽包系统进行误差反向传播(BP)神经网络建模,并对神经网络模型进行泛化能力测试,然后利用基于BP神经网络的PID控制方法设计汽包液位优化控制器.实验仿真结果表明,基于BP神经网络建立的汽包模型具有较好的泛化能力,神经网络PID优化控制器在控制精度高、收敛速度快和鲁棒性强等方面都优于传统PID控制器.     

一种基于自适应2阶终端滑模控制算法的理论和实验研究

针对一类非线性多变量被控对象的控制问题,设计了一种基于自适应2阶终端滑模的自主控制方法．与传统滑模控制方法相比,该控制方案保留了其结构简单、鲁棒性强的优点,并有效削弱了传统滑模控制引发的抖振问题．利用基于李亚普诺夫的分析方法进行闭环系统的稳定性分析,确保了闭环系统的稳定性以及控制误差有限时间收敛．通过在3自由度控制实验平台上进行姿态镇定和跟踪实验,表明本文所提出的控制方法具有良好的姿态控制效果.     

CMAC-PID在柴油机调速系统中的应用

研究柴油机优化控制问题,柴油机调速控制要求快速、准确.针对柴油机调速系统的非线性、时变等特点,导致稳定时间长,传统PID控制不理想.为了提高自动调节供油量,达到控制准确度,提出一种小脑模型神经网络(CMAC)与PID并行控制的柴油机调速系统(CMAC-PID).采用现代控制理论与经典相结合的方法对柴油机调速系统进行优化设计,利用传统PID来实现柴油机调速属于反馈控制,保证了系统的稳定性,并且能够抑制扰动,结合CMAC神经网络对控制器进行前馈控制,确保系统的控制响应速度,减小超调量,提高控制精度.仿真结果表明,采用CMAC-PID算法控制精度更高,超调小,稳定时间短,鲁棒性强,能优化柴油机调速系统性能,为设计提供了参考.     

基于自适应RBF 网络补偿的智能车辆循迹控制

针对智能车辆这一复杂非线性时变系统的循迹控制问题,提出一种基于Lyapunov函数方法的RBF神经网络自适应补偿控制策略.首先建立了车辆循迹控制的动力学名义模型;然后利用RBF神经网络对车辆循迹控制名义模型的不精确部分进行自适应补偿;最后应用Lyapunov稳定性理论推导出RBF网络权值的训练规则并证明了控制系统的稳定性.仿真结果表明,该方法提高了循迹控制的精度,具有较高的可行性和实用性.     

深空环境下三星库仑编队重构控制研究

本文研究了深空环境下三星库仑编队构型重构控制问题.首先考虑外界环境干扰作用(主要以太阳光压为主)和德拜效应影响,推导出精确的三星库仑编队动力学方程.针对库仑编队动力学特性和太阳光压对于编队任务控制精度的影响,设计基于BP神经网络的PID控制方法.PID控制结构简单,稳定性好,BP神经网络具有超强的自主学习和非线性逼近干扰能力,二者有机结合,通过BP神经网络输出最优的PID控制参数组合,改变卫星所带电荷从而改变卫星之间库仑力大小,使编队渐近稳定并按期望距离和构型飞行.仿真结果表明基于BP神经网络PID控制性能明显优于传统PID控制,大大提高了编队控制精度和系统对于外界干扰的鲁棒性.     

无刷直流电机调速系统神经网络自适应滑模变结构控制

为了提高无刷直流电机调速驱动系统的性能,提出神经网络自适应滑模变结构控制策略。推导无刷直流电机端电压与转速之间的微分方程,运用滑模变结构控制理论,通过调节端电压来实现转速控制;为了有效抑制系统在滑模切换面上的抖振采用自适应算法调整滑模增益的大小;从实际应用的角度出发,利用神经网络对非线性函数的任意精度拟合性,设计径向基函数神经网络估计器对控制量中广义扰动进行动态估计。仿真和实验结果表明采用本文提出的方法控制无刷直流电机,超调量小,速度响应快,控制精度高,且系统对各种干扰和参数摄振具有较强的鲁棒性,动、静态性能均优于PID控制。     

基于LuGre 摩擦模型的机械臂模糊神经网络控制

针对未知摩擦非线性会使机械臂控制精度难以提高的缺陷,建立基于动态LuGre摩擦的机械臂模型.在系统参数未知和机械臂负载变化的情况下,设计一种自适应模糊神经网络控制器,采用基函数中心和宽度均自适应变化的模糊神经网络补偿器,实现对系统中包括LuGre摩擦在内的非线性环节的逼近,并利用滑模控制项减小逼近误差.通过Lyapunov方法证明了闭环系统的稳定性,并通过仿真结果验证了所提出控制方法的有效性.     

非仿射系统的自学习滑模抗扰控制

针对一类单输入单输出(single-input single-output,SISO)非仿射非线性系统的控制问题,提出了一种自学习滑模抗扰控制方法.该方法用非线性光滑函数设计扩张状态观测器,实现SISO非仿射非线性系统内部不确定性和外部扰动的扩张状态估计,并将扩张状态观测器(extended state observer,ESO)与自学习滑模控制技术融为一体,实现SISO非仿射非线性系统的自学习滑模抗扰控制.该方法不依赖受控对象的数学模型,可以快速跟踪任意给定的参考信号.数值仿真试验表明了该方法响应速度快、控制精度高,具有很强的抗扰动能力,因而是一种鲁棒稳定性很强的控制方法,在SISO非仿射非线性系统控制领域具有重要作用.     

Adaptive neural integral sliding‐mode control for tracking control of fully actuated uncertain surface vessels

This paper develops a novel adaptive neural integral sliding‐mode control to enhance the tracking performance of fully actuated uncertain surface vessels. The proposed method is built based on an integrating between the benefits of the approximation capability of neural network (NN) and the high robustness and precision of the integral sliding‐mode control (ISMC). In this paper, the design of NN, which is used to approximate the unknown dynamics, is simplified such that just only one simple adaptive rule is needed. The ISMC, which can eliminate the reaching phase and offer higher tracking performance compared to the conventional sliding‐mode control, is designed such that the system robust against the approximation error and stabilize the whole system. The design procedure of the proposed controller is constructed according to the backstepping control technique so that the stability of the closed‐loop system is guaranteed based on Lyapunov criteria. The proposed method is then tested on a simulated vessel system using computer simulation and compared with other state‐of‐the‐art methods. The comparison results demonstrate the superior performance of the proposed approach.     

Robust Fuzzy Output Sliding Control without the Requirement of State Measurement

A robust fuzzy output sliding control for nonlinear robotic arms is proposed in this paper. The proposed method not only retains the advantages of the conventional sliding mode control such as robustness against parameter variations and external disturbances, but also uses measurable output signals to define the sliding surface function. A fuzzy controller is developed to modify the control law to avoid state measurement. Control system stability is proved by using the Lyapunov stability theorem. The system robustness is guaranteed. Simulations results demonstrate the validity and effectiveness of the proposed method for uncertain nonlinear robotic arms.     

电液伺服位置系统的变结构自适应鲁棒控制

该文建立了电液伺服位置系统的带有时变参数和非线性特性的三阶模型。在此基础上。基于滑模控制理论,对其设计了一种具有参数自适应能力的自适应滑模变结构控制器。从初始状态到达滑模面这段运动时间内和在滑模面上运动时,依赖于一个时间函数使系统在两个不同的控制律之间进行切换,以满足不同运动阶段的要求。此外在应用变结构控制的同时,通过参数自适应来消除系统不确定性对控制性能的影响,进而增加了系统的鲁棒性。然后基于李亚普诺夫稳定性理论证明了所设计系统的渐近稳定性。最后将此方法应用于冷轧机电液伺服位置系统进行仿真,结果表明这种针对不同运动阶段的特点所设计的控制器满足了变结构控制的可达条件,达到了减小系统到达滑模面的时间和削弱抖振的目的。与传统的变结构控制对比。该文所设计的控制器在减小响应时间、抑止超调和提高鲁棒性方面都具有先进性。     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

Practical adaptive fractional‐order nonsingular terminal sliding mode control for a cable‐driven manipulator

For the high precise tracking control purpose of a cable‐driven manipulator under lumped uncertainties, a novel adaptive fractional‐order nonsingular terminal sliding mode control scheme based on time delay estimation (TDE) is proposed and investigated in this paper. The proposed control scheme mainly has three elements, ie, a TDE element applied to properly compensate the lumped unknown dynamics of the system resulting in a fascinating model‐free feature; a fractional‐order nonsingular terminal sliding mode (FONTSM) surface element used to ensure high precision in the steady phase; and a combined reaching law with adaptive technique adopted to obtain fast convergence and high precision and chatter reduction under complex lumped disturbance. Stability of the closed‐loop control system is analyzed with the Lyapunov stability theory. Comparative simulations and experiments were performed to demonstrate the effectiveness of our proposed control scheme using 2‐DOF (degree of freedom) of a cable‐driven manipulator named Polaris‐I. Corresponding results show that our proposed method can ensure faster convergence, higher precision, and better robustness against complex lumped disturbance than the existing TDE‐based FONTSM and continuous FONTSM control schemes.     

Vehicle Dynamics Modeling and Electronic Stability Program/ Active Front Steering Sliding Mode Integrated Control

Chassis integrated control can significantly improve vehicle handling stability and comfort. Because of the complexity of the problem, it has attracted significant research attention. We built a vehicle nonlinear dynamic model with multi‐degree freedom, including body movement, wheel movement, and electronically controlled hydraulic power steering system. We compared the magic formula tire model, Dugoff tire model, brush tire model, and LuGre dynamic friction tire model and steady model. The precision of the model was verified by a comparison between simulation results and the real vehicle test results. Then, based on the vehicle dynamics model, an AFS (active front steering) controller was designed based on sliding mode variable structure control, and an AFS and ESP (electronic stability program) integrated coordination controller was proposed. Finally, based on the nonlinear tire model and multi‐DOF (degree of freedom) vehicle model, sinusoidal and step steering angle input simulation analysis was proposed on different road friction coefficients. The results show that the vehicle has better response characteristics with coordinated control strategy when compared with AFS and ESP only control. The evidence suggests that the proposed integrated control system in this paper can improve vehicle stability and safety.     

Second-order terminal sliding mode control for hypersonic vehicle in cruising flight with sliding mode disturbance observer

This paper focuses on the design of nonlinear robust controller and disturbance observer for the longitudinal dynamics of a hypersonic vehicle (HSV) in the presence of parameter uncertainties and external disturbances. First, by combining terminal sliding mode control (TSMC) and second-order sliding mode control (SOSMC) approach, the secondorder terminal sliding control (2TSMC) is proposed for the velocity and altitude tracking control of the HSV. The 2TSMC possesses the merits of both TSMC and SOSMC, which can provide fast convergence, continuous control law and hightracking precision. Then, in order to increase the robustness of the control system and improve the control performance, the sliding mode disturbance observer (SMDO) is presented. The closed-loop stability is analyzed using the Lyapunov technique. Finally, simulation results illustrate the effectiveness of the proposed method, as well as the improved overall performance over the conventional sliding mode control (SMC).     

基于自组织小波小脑模型关节控制器的不确定非线性系统鲁棒自适应终端滑模控制

针对一类不确定非线性系统的跟踪控制问题,在考虑建模误差、参数不确定和外部干扰情况下,以良好的跟踪性能及强鲁棒性为目标,提出基于自组织小脑模型(self-organizing wavelet cerebellar model articulation controller,SOWCMAC)的鲁棒自适应积分末端(terminal)滑模控制策略.首先,将小脑模型、自组织神经网络和小波函数各自优势相结合,给出一种SOWCMAC,以保证干扰估计方法具有快速学习能力和更好的泛化能力.其次,设计两种改进的terminal滑模面构造方法,并分别给出各自的收敛时间.然后,基于SOWCMAC和改进的积分terminal滑模面,给出不确定非线性系统鲁棒自适应非奇异terminal控制器的设计过程,其中通过构造自适应鲁棒项抑制干扰估计误差对系统跟踪性能的影响,并利用Lyapunov理论证明闭环系统的稳定性.最后,将该方法应用于近空间飞行器姿态的控制仿真实验,结果表明所提出方法有效性.     

基于主动滑模控制的一类不确定混沌系统的同步

讨论了一类不确定混沌系统的同步问题。基于主动控制思想,提出了一种新的主动滑模控制策略,使得从任意初始条件出发的不确定混沌系统在有限时间内趋近滑模面;通过一种新颖的虚拟反馈控制,得到了设计鲁棒滑模面的一个充分条件,较好地实现了响应系统与驱动系统的完全同步,确保了不确定混沌系统同步的鲁棒稳定性。该控制器适用于一般的混沌系统。以Lü混沌系统为例进行了仿真验证,仿真结果表明,该控制方法可以实现较快的混沌同步,且同步的鲁棒稳定性良好。     

Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

In cold season, wet snow ice accretion on overhead transmission lines increases wind load effects which in turn increases line tension. This increased line tension causes undesirable effects in power systems. This paper discusses the design of an observer-based boundary sliding mode control (BSMC) for 3 DOF overhead transmission line de-icing robot manipulator (OTDIRM). A robust radial basis functional neural network (RBFNN) observer-based neural network (NN) controller is developed for the motion control of OTDIRM, which is a combination of BSMC, NN approximation and adaptation law. The RBFNN-based adaptive observer is designed to estimate the positions and velocities. The weights of both NN observer and NN approximator are tuned off-line using particle swarm optimization. Using Lyapunov analysis the closed loop tracking error was verified for a 3 DOF OTDIRM. Finally, the robustness of the proposed neural network-based adaptive observer boundary sliding mode control (NNAOBSMC) was verified against the input disturbances and uncertainties.