多关节机器人的模糊神经滑模控制

针对含有建模误差和不确定干扰的多关节机器人轨迹跟踪控制,提出了一种模糊神经滑模控制方法.该方法采用全局快速终端滑模面,保证了系统能够从任意初始状态在有限时间内到达滑模面和平衡点.采用模糊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.文中利用李亚普诺夫稳定性判据推导出了控制器的控制律和模糊神经网络的目标函数,通过模糊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.     

基于神经网络补偿的机器人滑模变结构控制

针对机器人控制系统中存在的建模误差和不确定性干扰,提出了基于神经网络补偿的滑模变结构控制。该方法采用双幂次快速终端滑模控制使得系统能在有限时间内快速达到滑模面和平衡点,采用径向基函数神经网络自适应地补偿建模误差和不确定干扰,并通过李雅普诺夫直接法设计权值更新率,确保了系统的全局稳定性,有效抑制了抖震。对两关节机器人的仿真结果表明了该方法的有效性。     

基于径向基函数神经网络的多关节机器人滑模控制器

针对具有不确定性的多关节机器人系统,提出了一种径向基函数神经滑模控制方法;该控制方案采用全局滑模面,将神经网络的非线性映射能力与滑模控制的特点相结合,利用径向基神经网络自适应学习系统不确定性的未知上界,消弱了由滑模控制产生的抖动,同时保证了系统的鲁棒性;基于李亚普诺夫定理给出了系统稳定性的充分条件;仿真结果表明,该方法具有良好的轨迹跟踪和速度跟踪性能,提高了对于建模误差和不确定干扰等因素的鲁棒性。     

基于干扰观测器的一类不确定非线性系统自适应二阶动态terminal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,以其拥有良好的跟踪性能以及强鲁棒性为目标,提出基于回归扰动模糊神经网络干扰观测器(recurrent perturbation fuzzy neural networks disturbance observer,RPFNNDO)的鲁棒自适应二阶动态terminal滑模控制策略.将回归网络、模糊神经网络和sine-cosine扰动函数各自优势相结合,给出一种回归扰动模糊神经网络结构,提出RPFNNDO设计方法,保证干扰估计准确性;构造基于带有指数函数滑模面的二阶快速terminal滑模面,给出其控制器设计过程,避免了滑模到达阶段、传统滑模的抖振问题,采用具有指数收敛的鲁棒项抑制干扰估计误差对系统跟踪性能的影响,利用Lyapunov理论证明闭环系统的稳定性;将该方法应用于混沌陀螺系统同步控制仿真实验,结果表明所提方法的有效性.     

控制增益符号未知的不确定非线性系统鲁棒自适应控制

针对一类控制增益函数及符号均未知的不确定非线性系统,基于反推滑模设计方法,提出一种鲁棒自适应神经网络控制方案.结合Nussbaum增益设计技术和神经网络逼近能力,取消了控制增益函数及符号已知的条件,应用积分型Lyapunov函数避免了控制器奇异性问题,并通过引入神经网络逼近误差和不确定干扰上界的自适应补偿项消除了建模误差和不确定干扰的影响.理论分析证明了闭环系统所有信号半全局一致终结有界,仿真结果验证了该方法的有效性.     

基于Backstepping的倒立摆鲁棒跟踪控制

针对内部参数不确定及存在外部干扰的非线性倒立摆系统,提出了基于Backstepping方法的滑模变结构控制律,并且采用RBF神经网络逼近系统不确定非线性函数,同时引入滑模误差对其神经网络权值进行在线自适应调整,使神经网络的逼近速度加快,改善了动态性能.该控制律能保证倒立撰轨迹跟踪误差的快速收敛性以及对外部扰动和内部参数不确定的不敏感性,最后给出的仿真实例证明了该理论分析结果的正确性,控制效果良好.     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

无刷直流电机的神经滑模变结构控制

针对工业控制中对无刷直流电机位置控制的高精度要求,研究了滑模变结构控制和神经网络相结合的控制方法.为了消除滑模变结构控制方法中存在的抖振缺点,提出了一种神经滑模控制方法.方法首先设计了一个二阶时变滑模面,使系统的初始状态就在滑模面上,可以增强系统的鲁棒性.然后,通过径向基函数神经网络学习电机的负载、干扰等参数,使滑模控制的切换控制项能随着负载参数的变化而变化,削弱了滑模变结构控制的抖振.对上述方法进行仿真,结果证明了上述方法的有效性,为无刷直流电机优化控制提供了有效手段.     

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.     

多关节机器人的自适应模糊滑模控制

针对多关节机械臂轨迹跟踪控制,提出了一种基于全局快速终端滑模面的自适应模糊滑模控制方法。该方法通过设计合适的自适应律,采用模糊自适应控制调节滑模控制的切换控制增益,实现了对建模误差和不确定干扰的自动跟踪,削弱了抖振。系统不需要对建模误差和干扰进行预估计,并且通过对控制器结构的简化,降低了模糊控制器的维数,减少了计算量。利用李亚普诺夫定理证明了控制系统的稳定性,仿真结果表明了其有效性。     

漂浮基空间机器人自适应RBF 网络终端滑模控制

主要研究漂浮基空间机器人对工作空间连续轨迹跟踪控制问题．针对系统动力学模型中非线性项未知，以及参数不确定性和外界扰动无法估计的情况，提出了基于自适应RBF网络终端滑模控制方法．该方法结合了非线性滑动流形与径向基函数特性，利用自适应RBF网络在线学习系统中的不确定性，使得无需精确的动力学模型亦能保证系统在有限时间内快速稳定．根据Lyapunov方法设计的自适应增益保证闭环控制系统具有全局稳定性，并且有效抑制抖振现象．针对6关节空间机器人的轨迹跟踪控制仿真表明，提出的自适应RBF网络终端滑模控制方法能够基于不完整动力学模型实现高精度轨迹跟踪，且误差在有限时间内快速收敛，系统抖振也得到了有效抑制．     

Distributed consensus control for multi‐agent systems using terminal sliding mode and Chebyshev neural networks

This paper investigates the problem of consensus tracking control for second‐order multi‐agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication ?ow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite‐time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural‐network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering‐free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time‐varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite‐time stability in both the reaching phase and the sliding phase is guaranteed by a Lyapunov‐based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane‐based sliding mode controller. Copyright © 2011 John Wiley & Sons, Ltd.     

Decentralized sliding‐mode control for attitude synchronization in spacecraft formation

This paper addresses attitude synchronization and tracking problems in spacecraft formation in the presence of model uncertainties and external disturbances. A decentralized adaptive sliding mode control law is proposed using undirected interspacecraft communication topology and analyzed based on algebraic graph theory. A multispacecraft sliding manifold is derived, on which each spacecraft approaches desired time‐varying attitude and angular velocity while maintaining attitude synchronization with the other spacecraft in the formation. A control law is then developed to ensure convergence to the sliding manifold. The stability of the resulting closed‐loop system is proved by application of Barbalat's Lemma. Simulation results demonstrate the effectiveness of the proposed attitude synchronization and tracking methodology. Copyright © 2012 John Wiley & Sons, Ltd.     

基于线性化反馈的径向神经滑模控制器设计

针对非线性系统,采用径向神经网络逼近及自适应控制方法,利用线性化反馈技术,设计一种自适应神经滑模控制器。滑模变结构控制具有独特的鲁棒性能以及对匹配不确定性和外干扰的完全自适应等特点,但容易出现系统抖振问题,将神经网络应用于滑模变结构控制系统的设计中,系统抖振得到抑制。仿真结果也表明将神经网络与滑模控制相结合的方法是行之有效的。     

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.     

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.      

基于改进型非线性干扰观测器的爬壁机器人轨迹跟踪

针对爬壁机器人建模不准确及容易受外部扰动的影响造成位置及姿态误差的问题,提出了一种基于改进型非线性干扰观测器的轨迹跟踪控制方案.首先通过反演控制设计了一个运动学控制器为机器人动力学控制提供参考质心速度与角速度.其次应用改进型非线性扰动观测器作为前馈控制对建模误差及外部扰动进行估计,并保证扰动误差以指数形式收敛.最后针对引入干扰观测器的动力学模型设计了滑模控制器.该方案对外界干扰进行了快速补偿,并通过Lyapunov定理证明了其稳定性.仿真结果表明该控制方法对于克服建模误差及外界干扰具有较好的效果.     

Continuous Finite‐Time Sliding Mode Control for Uncertain Nonlinear Systems with Applications to DC‐DC Buck Converters

This paper investigates the continuous finite‐time control problem of high‐order uncertain nonlinear systems with mismatched disturbances through the terminal sliding mode control method. By constructing a novel dynamic terminal sliding manifold based on the disturbance estimations of high‐order sliding mode observers, a continuous finite‐time terminal sliding mode control method is developed to counteract mismatched disturbances. To avoid discontinuous control action, the switching terms of a dynamic terminal sliding manifold are designed to appear only in the derivative term of the control variable. To validate its effectiveness, the proposed control method is applied to a DC‐DC buck converter system. The experimental results show the proposed method exhibits better control performance than a chattering free controller, such as mismatched disturbances rejection and smaller steady‐state fluctuations.     

A Robust Control Law with Estimated Perturbation Compensation for Robot Manipulators

By considering the dynamic response of a robot manipulator as characterized by the sliding function, a technique is proposed to estimate the perturbation in the robot control system. Perturbation compensation is then incorporated in the design of a robust control law to cancel the effects of system parametric uncertainties and external disturbances. A normalized power rate component is introduced to replace the discontinuous control that is usually associated with variable structure system. The suggested robust control law ensures that the robot control system reaches an user specified neighbourhood of the sliding manifold in finite time and with prescribed transient behaviour. Explicit estimates of the bounds on modelling errors and external disturbances are not required while signal measurement uncertainties can be accommodated. Furthermore, using the equivalent control concept, a very simple expression is derived to estimate the system perturbation signal. Detailed computer simulation results are presented to demonstrate the effectiveness of the proposed control law.