High-speed Nonsingular Terminal Switched Sliding Mode Control of Robot Manipulators

This paper proposes a high-speed nonsingular terminal switched sliding mode control (HNT-SSMC) strategy for robot manipulators. The proposed approach enhances the control system performance by switching among appropriate sliding mode controllers according to different control demands in different regions of the state space. It is shown that the highspeed nonsingular terminal switched sliding mode (HNT-SSM) which is the representation of different control demands and enforced by the HNT-SSMC has the property of global highspeed convergence compared with the nonsingular fast terminal sliding mode (NFTSM), and provides the global non-singularity. The simulation study of an application example is carried out to validate the effectiveness of the proposed strategy.      

机器人的非奇异终端神经滑模轨迹跟踪控制

针对具有不确定性的机器人系统,为提高系统的稳态跟踪精度,提出一种非奇异终端神经滑模轨迹跟踪控制方案.控制器采用改进的非奇异终端滑模面,并基于径向基函数神经网络自适应调整控制律的切换项,不但克服了在设计中需要知道系统不确定性的上界的限制,而且平滑了控制信号.可应用Lyapunov稳定性理论证明了系统的渐近稳定性和跟踪误差的渐近收敛性.仿真结果验证了控制方法不仅能够保证机器人系统轨迹跟踪控制的快速性和鲁棒性,而且有效地削弱了抖振,可见方案是可行且有效的.     

Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators

International Journal of Control, Automation and Systems - This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks (RBFNNs) for industrial robot...     

非奇异快速的终端滑模控制方法

针对已有终端滑模控制的奇异和收敛缓慢问题,提出一种非奇异快速终端滑模函数,并利用李亚普诺夫方法证明其有限时间收敛特性．在此基础上,结合带负指数项的吸引子来设计控制律,使控制输入实现时间连续,并保证滑模面全局存在．理论分析表明,通过合理选择控制参数,可使系统避免“收敛停滞”,且对有界模型误差和外部干扰具有较好鲁棒性．     

机械臂自适应反演超螺旋全局终端滑模控制

针对机械臂系统存在的系统参数摄动、非线性摩擦及外部干扰等不确定问题,提出一种自适应反演超螺旋全局终端滑模轨迹跟踪控制方法。该方法基于反演法、Lyapunov理论和全局快速终端滑模理论设计控制器,保证系统稳定性及全局收敛性,增强系统的鲁棒性。为解决系统集总扰动上界未知的问题,采用自适应技术设计切换控制律,抵消不确定性的影响,同时引入超螺旋算法抑制滑模控制固有的抖振现象。最后,通过理论分析和仿真算例验证了该控制器的有效性与可行性。     

Finite-time Control for Image-based Visual Servoing of a Quadrotor Using Nonsingular Fast Terminal Sliding Mode

International Journal of Control, Automation and Systems - This paper investigates finite-time control for image-based visual servoing (IBVS) of a quadrotor subjects to image dynamics uncertainties...     

一类不确定系统的自适应非奇异Terminal滑模控制

提出针对一类不确定系统的自适应非奇异Terminal滑模控制。确定含非线性项的滑模面,通过构造李亚普洛夫函数的方式确定控制器结构,设计自适应增益控制律实时估计不确定参数,并考虑外扰动的影响,证明控制系统的稳定性和鲁棒性。仿真结果验证自适应非奇异Terminal滑模控制器能够使不确定系统状态变量快速收敛。     

A Robust Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In this paper, a robust adaptive terminal sliding mode controller is developed for n-link rigid robotic manipulators with uncertain dynamics. An MIMO terminal sliding mode is defined for the error dynamics of a closed loop robot control system, and an adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties in the Lyapunov sense. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated and a finite time error convergence in the terminal sliding mode can be guaranteed. Also, a useful bounded property of the derivative of the inertial matrix is explored, the convergence rate of the terminal sliding variable vector is investigated, and an experiment using a five bar robotic manipulator is carried out in support of the proposed control scheme.     

基于神经网络的机器人轨迹跟踪控制

针对机器人模型未知情况，讨论了用神经网络和反馈控制实现机械手的跟踪控制。提出一种基于参考误差的投影算法来训练网络权值，训练后网络输出能逼近期望的前馈力矩，并从理论上证明跟踪误差的收敛性。仿真结果表明方案具有较好的跟踪性能和较强的抗干扰能力。     

机械臂卷积神经网络滑模轨迹跟踪控制

针对工业技术的发展对于多关节机械臂的精度与快速控制高要求,提出了一种机械臂卷积神经网络滑模轨迹跟踪控制方法。分析机械臂动力学方程,提取其中的不确定部分,针对不确定部分,构建深度卷积神经网络对其进行补偿,将补偿部分引入到滑模控制律中,通过改进后的滑模控制实现对机械臂轨迹跟踪的精确控制,并通过构建Lyapunov函数论证了系统的稳定性。仿真结果显示该方法能够满足轨迹跟踪要求,且减小了抖振现象。通过与其余三种典型控制方法的对比,测试结果表明,该方法加快了轨迹跟踪误差的收敛,且跟踪精度有了明显的提高。     

基于神经网络的不确定机器人自适应滑模控制

提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合,利用RBF网络自适应学习系统不确定性的未知上界,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。仿真结果表明了该方案的有效性。     

Adaptive Nonsingular Terminal Sliding Mode Control Design for Near Space Hypersonic Vehicles

This paper presents an adaptive nonsingular terminal sliding mode approach for the attitude control of near space hypersonic vehicles (NSHV) in the presence of parameter uncertainties and external disturbances. Firstly, a novel nonsingular terminal sliding surface is developed and its finitetime convergence is analyzed. Then, an adaptive nonsingular terminal sliding mode control law is proposed, which is chattering free. In the proposed approach, all parameter uncertainties and external disturbances are lumped into one term, which is estimated by an adaptive uncertainty estimation for eliminating the boundary requirement needed in the conventional control design. Subsequently, stability of the closed-loop system is proven based on Lyapunov theory. Finally, the proposed approach is applied to the attitude control design for NSHV. Simulation results show that the proposed approach attains a satisfactory performance in the presence of parameter uncertainties and external disturbances.      

非奇异终端滑模控制系统相轨迹和暂态分析

对于采用非奇异终端滑模控制的二阶系统,考虑到不同的初始状态,研究了其相轨迹的分布规律,给出了临界曲面的概念和解析表达. 基于相轨迹的分布,考虑了系统初始状态与到达滑模面时间的关系,探讨了到达时间最大值的估计算法,并给出了非奇异终端滑模控制系统的收敛时间的数学表达. 通过仿真验证了相轨迹的分布规律和临界曲面的正确性,以及根据系统初值估计到达时间的有效性.     

PHANTOM Omni机器人的自适应模糊滑模控制

针对PHANTOM Omni机器人的位置轨迹跟踪问题,采用了一种基于模糊逻辑的自适应模糊滑模控制方案。利用滑模控制中的切换函数作为输入,根据模糊系统的逼近能力设计控制器,并基于李雅谱诺夫方法设计自适应律对控制器所需参数进行实时调节。仿真中将其与传统的滑模控制进行了比较,仿真结果表明：自适应模糊滑模控制能使PHANTOM Omni机器人更好地实现期望的位置轨迹跟踪并有效地减轻抖振现象,从而证明了该方法在PHANTOM Omni机器人上实施的可行性。     

Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.     

单连杆柔性臂负载自适应模糊滑模控制

针对单连杆柔性臂,提出了负载自适应模糊滑模控制与最优控制相结合的混合控制方法。首先,采用奇异摄动将系统分为慢变和快变两个子系统。然后,对慢变子系统采用负载自适应模糊滑模控制,快变子系统采用最优控制。最后,仿真结果表明,该方法不仅能实现柔性臂轨迹的快速、准确跟踪,有效地抑制弹性振动,并且对负载的变化具有强的鲁棒性。     

液压驱动伺服机器人的模糊滑模控制研究

针对液压驱动四足机器人伺服系统非线性和不确定性严重的问题,提出了一种快速响应、鲁棒性好、控制精度高的模糊滑模控制器,并进行了仿真研究.首先,建立了液压驱动伺服机器人的液压动力机构非线性数学模型,利用Lyapunov方法设计了滑模控制器;其次,构造了一个模糊边界层宽度调节器,削弱滑模控制的抖振;最后,分析了参考力、液压参数、供油压力及负载刚度变化对系统输出的影响.仿真结果表明,该控制器对液压伺服力系统非线性和参数变化具有较好的控制效果.该方法用于四足液压驱动伺服机器人的控制是可行的、有效的.     

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