惯性定向三轴稳定卫星姿态自适应滑模控制

在建立惯性定向三轴稳定卫星非线性耦合姿态运动模型的基础上,设计了姿态三轴稳定控制的自适应滑模变结构控制器。在控制器的设计中,考虑了小卫星惯量参数的不确定性以及外界力矩干扰的情况,利用自适应滑模变结构控制方法在线辨识干扰力矩的极值以及卫星惯量参数,并依此动态调整控制器的输出。基于Lyapunov稳定性原理证明了所设计的控制器能够使系统全局一致最终有界稳定。仿真结果表明了该控制方法的有效性和可行性。     

旋转导向钻井工具稳定平台模糊滑模控制研究

关于稳定平台是旋转导向钻井工具的重要环节.针对稳定平台存在的摩擦问题带来的不确定性,为提高平台系统的控制精度和稳定性,提出利用模糊控制理论与滑模变结构控制相结合的稳定平台控制系统.通过建立带摩擦的稳定平台模型,设计出变结构控制器和自适应模糊系统逐步逼近等效控制,并对切换项进行模糊逼近,以平滑不连续控制.仿真结果表明方法既保证了控制系统对外界扰动和参数不确定性具有较好的鲁棒性,也实现了较高的稳定平台姿态控制精度,使闭环系统具有良好的动态性能.     

非伪控制解决惯性稳定平台的非线性时变干扰

非伪控制是一种基于在线数据的驱动控制方式.本文通过进化策略实现候选控制器的多样性操作,结合滞后算法切换控制器,从而实现系统自适应的PID稳定控制.在惯性稳定平台中,由于非线性时变干扰的存在令被控对象不能提供精确的控制模型,今将非伪控制算法用于惯性稳定平台中,能使惯性稳定平台准确地稳定框架,其方位轴稳定误差小于3.4mrad,俯仰轴稳定误差小于4.36mrad.     

Stewart平台神经网络非奇异终端滑模控制

针对Stewart平台的六自由度（six degrees of freedom, 6-DOF）轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数（radial Basis function, RBF）神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分（proportional integral derivative,PID）控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。     

基于模糊神经网络的滑模控制

研究了一类不确定性非线性系统的滑模变结构控制,提出了一种基于模糊神经网络（Ｆｕｚｚｙ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋｓ）的滑模变结构设计方法,设计了控制器的结构,利用动态反向传播算法实现滑模控制,这种方法与一般变结构控制相比不但具有强的鲁棒性而且还能有效地消除抖动现象,同时在设计中不需要知识系统中不确定性和扰动的上界,另外还运用Ｌｙａｐｕｎｏｖ函数从理论上分析上了系统的稳定性。仿真结果说明了本文所提     

基于神经网络的雷达天线稳定平台控制设计

为了提高稳定平台的抗干扰能力,使雷达天线指向稳定保证成像清晰,该文提出一种神经网络PID控制方案,此方案是利用神经网络的非线性映射能力和自学习自适应能力,找到最优的PID三个控制参数,使稳定平台这种非线性系统具有良好的控制效果。通过MATLAB仿真实验证明,神经网络PID控制方案的超调量仅有1.05%,稳态时间只有0.61 s。远远小于PID控制,而其对扰动的响应幅值仅为PID控制的18.6%,与传统的PID控制相比,神经网络PID控制具有更优秀的抗干扰能力,这种控制方案更加适合在复杂环境下工作的稳定平台,有效保证其精度和稳定性。     

车载惯性平台随动系统的自抗扰控制

针对车载惯性随动平台的扰动特性,提出了随动伺服回路的自抗扰控制算法。该方法不需要精确的系统数学模型,具有强鲁棒性、快速性、高精度、方法简单等特点。仿真和实验结果表明,该方法调转时间短,抗干扰能力强,是一种应用在实际平台系统中的理想的控制器。     

基于神经网络的机器人二阶滑模控制

本文提出了一种基于神经网络与二阶滑模控制融合的控制策略用于非线性机器人控制,设计了一种新颖简易的二阶滑模控制方法,有效地避免了常规变结构控制的抖震问题,并采用神经网络辨识未知的机器人的非线性模型,通过Lyapunov直接法设计网络的权值更新率,确保了系统闭环全局渐近稳定性。最后,通过仿真验证了算法的有效性。     

神经网络滑模变结构控制研究综述

综述了近年来将人工神经网络控制和滑模变结构控制相结合的研究工作．从神经网络在滑模变结构控制中不同的应用方式出发，论述了神经网络在提高和改善滑模变结构系统性能方面的理论和方法，分析了它们各自的特点与相互之间的联系．最后展望了该领域未来的研究方向．     

多关节机器人的神经滑模控制

针对含有建模误差和不确定干扰的多关节机械臂轨迹跟踪控制,提出了一种神经滑模控制方法。该方法采用全局快速终端滑模面保证了系统状态能够在有限时间内到达滑模面和平衡点。采用径向基函数神经网络自适应地补偿系统的建模误差和外界干扰,保证了滑模控制在滑模面的运动。利用李亚普诺夫稳定性判据推导出了控制器的控制律和神经网络的目标函数,通过神经网络的在线学习,削弱了滑模控制的抖振。仿真结果表明了其有效性。     

不确定非线性系统的自适应反推高阶终端滑模控制

针对一类非匹配不确定非线性系统,提出一种神经网络自适应反推高阶终端滑模控制方案.反推设计的前1步利用神经网络逼近未知非线性函数,结合动态面控制设计虚拟控制律,避免传统反推设计存在的计算复杂性问题,并抑制非匹配不确定性的影响;第步结合非奇异终端滑模设计高阶滑模控制律,去除控制抖振,使系统对于匹配和非匹配不确定性均具有鲁棒性.理论分析证明了闭环系统状态半全局一致终结有界,仿真结果表明了所提出方法的有效性.     

基于神经网络滑模的智能车辆横向控制

以智能车辆为研究对象,针对车辆模型存在高度非线性动态特性、参数不确定性以及行驶时受外部干扰较多导致控制精度不高、鲁棒性差等问题,提出了采用径向基函数(RBF)神经网络滑模控制方法.建立2自由度线性车辆模型和自由度非线性整车模型,在传统2自由度车辆控制模型状态方程的基础上推导出新的状态方程并以此设计了相应控制器.利用李雅普诺夫(Lyapunov)稳定性理论推导出神经网络的权,并证明控制系统的稳定性.仿真结果表明:与传统的滑模控制方法相比,该方法控制精度高,有较强的鲁棒性.     

Neural network sliding mode control based on on-line identification for electric vehicle with ultracapacitor-battery hybrid power

In order to deal with three major problems of electric vehicle (EV): the short driving range, the short life of batteries, and the poor ability of start-up, a hybrid power system was designed and applied to the EV. It was composed of an ultracapacitor with high-specific power and long life, four lead-acid batteries, and a bi-directional DC/DC converter. To improve the stability and reliability of the hybrid-power EV, based on establishing the mathematical models of driving and regenerative-braking processes, a novel neural network sliding mode controller (NNSMC) was researched and designed for the EV. The controller comprises a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), and a sliding mode controller (SMC). The BPNN is used to adaptively adjust the switching gain of the SMC on-line so as to avoid the whippings. The RBFNN is used to perform system identification and parameter prediction. The experimental results show that the NNSMC is superior to PID controller at response speed, steady-state tracking error and resisting perturbation whenever driving or braking. Additionally, the hybrid-power EV with NNSMC can improve the ability of start-up, recover more energy, lengthen the life of batteries, and increase the driving range than the EV using batteries as its single power source by about 40%, and than the hybrid-power EV with PID controller by about 4%. Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Hyun Seok Yang. This work was supported by the National Innovation Funding of China (06C26216100555). Jian-Bo Cao received the B.S. degree in Mechanical Engineering from Dalian Jiaotong University in 2003 and the Ph.D. degree in Mechanical Engineering from Xi’an Jiaotong University in 2008. He is currently working at Transportation College, Zhejiang Normal University. His research interests include electric vehicle, hybrid power, and intelligent control. Bing-Gang Cao received the B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Xi’an Jiaotong University in 1976, 1982, and 1992 respectively. He is currently a Professor at School of Mechanical Engineering, Xi’an Jiaotong University, where he is also the Director of Research & Development Center of Electric Vehicle. His research interests include robust control, intelligent control of electric vehicle, noise and vibration control of liquid system, control technology of renewable energy.     

Backstepping sliding mode control with functional tuning based on an instantaneous power approach applied to an underwater vehicle

In this paper, we present a modified backstepping sliding mode control to deal with Euler–Lagrange systems. The controller is applied in an underwater vehicle in order to show the effectiveness of the approach proposed. Instantaneous power data provided by the propulsion system are used to tune the controller in order to guarantee robust performance and energy saving. Thanks to the combination of an internal Proportional Integral and Derivative (PID) controller, it is possible implement high gains to deal with the influence of disturbances and uncertainties. A comparative study among this backstepping sliding mode controller and standard sliding mode controls is presented.     

基于Terminal 滑模的高超声速飞行器姿态控制

针对高超声速飞行器六自由度再入模型,考虑模型参数不确定和外界干扰对再入姿态控制的影响,基于Terminal滑模对再入过程中姿态角的跟踪控制问题进行研究.为了减少外界高频噪声对系统性能的影响,首先,利用多时间尺度技术将姿态模型划分为双环结构;然后,分别针对各环路设计Terminal滑模控制器,并通过Lyapunov理论和奇异摄动理论对系统的稳定性进行证明.仿真结果表明,对于六自由度再入模型,该控制方法能够很好地跟踪再入制导指令.     

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).     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).     

带有干扰观测器的高超声速飞行器滑模控制

针对高超声速飞行器非线性和易受干扰影响的特点,提出了带有扩张状态干扰观测器的连续滑模控制方法.在对飞行器非线性模型做线性化处理的基础上,设计了一种连续时间滑模控制器.该控制器在对不确定性和未知动态保持鲁棒性的基础上,消除了传统滑模中存在的抖振现象.对系统中存在的外加干扰,设计了扩张状态干扰观测器.将外加干扰作为系统的一个状态变量被估计出来,再将估计值用作滑模控制器的补偿量,进而达到消除外干扰的目的.在高超声速飞行器巡航飞行状态的基础上进行了仿真.仿真结果表明,所提出的方案能够满足控制要求.     

集成神经网络与自适应算法的分数阶滑模控制

针对被控对象的参数时变和外部扰动问题,本文融合神经网络的万能逼近能力和自适应控制技术,并结合分数阶微积分理论,提出了基于神经网络和自适应控制算法的分数阶滑模控制策略.本文采用等效控制的方法设计滑模控制律,并利用神经网络的万能逼近能力估测控制律的变化,结合自适应控制算法和分数阶微积分理论抑制传统滑模控制系统的抖震,同时根据Lyapunov稳定性理论分析了系统的稳定性,最后给出了实验结果.实验结果表明,本文提出的基于神经网络和自适应控制算法的分数阶滑模控制系统,能保持滑模控制器对系统外部扰动和参数变化鲁棒性的同时,也能有效地抑制抖震,使得系统获得较高的控制性能.