A Fast Convergence Super‐Twisting Observer Design for an Autonomous Airship

Airships are often under the risk of external disturbances, for example, the wind. The accurate external disturbance of the airship are hard to acquire. Typically, the super‐twisting observer can track the states for mechanical systems and could be used as an external disturbance tracker. However, the convergence rate of the traditional super‐twisting observer is very slow and the observed states often oscillate heavily. We modified the traditional super‐twisting observer by adding more variables to the sliding surface, and adding parameters to the newly added variables. Simulation results illustrates that the new proposed observer is able to reduce the convergence time and reduce overshoot greatly, improving the performance of the observer.     

A Robust Backstepping Sensorless Control for Interior Permanent Magnet Synchronous Motor Using a Super‐Twisting Based Torque Observer

In order to achieve high‐performance speed regulation for sensorless interior permanent magnet synchronous motors (IPMSMS), a robust backstepping sensorless control is presented in this paper. Firstly, instead of a real mechanical sensor, a robust terminal sliding mode observer is used to provide the rotor position. Then, a new super‐twisting algorithm (STA) based observer is designed to obtain estimates of load torque and speed. The proposed observer ensures finite‐time convergence, maintains robust to uncertainties, and eliminates the common assumption of constant or piece‐wise constant load torque. Finally, a sensorless scheme is designed to realize speed control despite parameter uncertainties, by combining the robust backstepping control with sliding mode actions and the presented sliding mode observers. The stability of the observer and controller are verified by using Lyapunov's second method to determine the design gains. Simulation results show the effectiveness of the proposed approach.     

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

In this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

Output feedback stabilization using super‐twisting control and high‐gain observer

An output feedback controller is designed for a class of uncertain nonlinear systems with relative degree higher than one. A super‐twisting sliding mode state feedback controller is designed and implemented using a high‐gain observer. It is proved that the controller achieves practical stabilization and the ultimate bound can be reduced by decreasing a design parameter. The performance of the controller is illustrated by simulation.     

基于扰动观测器的机械臂自适应反演滑模控制

机械臂的动力学模型通常包含一定的结构不确定性,并受到外界未知干扰的影响。针对现有模型的不确定性特点,提出了一种基于非线性扰动观测器的自适应反演滑模控制方法,解决机械臂的轨迹跟踪控制问题。对于外界干扰,利用非线性扰动观测器进行观测补偿,无需上界先验知识;对于结构不确定性,引入反演滑模控制,同时设计自适应律,保证闭环系统的稳定性并增强系统的动态适应性。仿真结果证明,所提出的方法可以有效克服系统不确定性,降低控制输入信号的抖振,最终实现期望轨迹的快速精确跟踪。     

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。     

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.     

Augmented Sliding Mode Control for Flexible Link Manipulators

A method of sliding mode control (SMC) is proposed for the control of flexible, nonlinear, and structural systems. The method departs from standard sliding mode control by dispensing with generalized accelerations during the control law design. Global, asymptotic stability of rigid body motion is maintained if knowledge on the bounds of the neglected terms exists. Furthermore, this method provides damping for the measured flexible body modes. This paper investigates an augmented SMC technique for slewing flexible manipulators. A conventional sliding surface uses a first order system including a combination of error and error rate terms. The augmented sliding surface includes an enhanced term that helps to reject flexible degrees-of-freedom. The algorithms are theoretically developed and experimentally tested on a slewing single flexible link robot. The test apparatus is instrumented with a strain gauge at the root and an accelerometer attached at the tip. A DC motor and encoder are used to servo the link from an initial position to a final position. A standard cubic polynomial is employed to generate the reference trajectories. The augmented SMC algorithm showed improved performance by reducing the flexible link tip oscillations.     

水平欠驱动机械臂的反步自适应滑模控制

针对二自由度水平欠驱动机械臂系统,提出了基于分层滑模控制思想的反步自适应滑模控制方法．该方法能够在不对系统状态模型进行复杂坐标变换,并且没有约束方程限制的前提下实现对欠驱动系统的反馈滑模控制．仿真结果表明了该方法的有效性,而且优化后的控制器具有较好的适应性和控制效果．     

基于滑模预测控制的海底采矿车轨迹跟踪算法

海底采矿车多工作于稀软底质,其面临的外部扰动较大,难以快速收敛跟踪误差,精准地跟踪预设轨迹。为此,本文提出了一种海底采矿车的滑模预测控制（sliding model predictive control,SMPC）轨迹跟踪算法。基于海底采矿车的运动学模型,首先设计滑模控制率实现轨迹跟踪误差快速收敛,其次利用少预测时域的线性时变模型预测控制算法（linear time varying model predictive control,LTV-MPC）优化该滑模控制率。而后,通过证明滑模控制率收敛和模型预测控制稳定,保证了闭环控制系统的稳定性。RecurDyn&Simulink联合仿真结果表明,与单一的滑模控制（sliding mode control,SMC）和线性时变模型预测控制算法相比,所提出的SMPC轨迹跟踪算法提高了轨迹跟踪精度,且算法具有较好的实时性。     

机械臂轨迹跟踪控制研究进展

综述了近年来刚性机械臂轨迹跟踪控制研究领域的最新进展.根据应用于机械臂的不同控制算法进行分类,从自适应PID控制、神经网络自适应控制、模糊自适应控制、滑模变结构控制和鲁棒自适应控制5种主要控制方法进行阐述.重点从关节空间出发,论述了各种控制算法在提高机械臂轨迹跟踪性能方面的各自优缺点,并分析了它们之间的相互联系.对机械...     

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...     

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators. First, a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems. Then an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed, so that the priori knowledge of the lumped disturbance is not required. Further, a new non-singular fast termin...     

Design of first- and second-order sliding mode observers for induction motors using a stator-flux model

We extend current research in the area of ‘sensorless’ control of induction motors by presenting two observers based on first- and second-order sliding mode control theories for the simultaneous estimation of flux and speed. We base the observers on the stator-flux model of the motor instead of the usual rotor-flux model mainly because of the uncertain rotor resistance that plays a significant role in the latter. By designing the observers as if they are sliding mode controllers, we lend the properties of parameter insensitive closed-loop dynamics and finite time convergence to the stator flux and speed estimation schemes. We also present simulation and experimental results to validate the operation of the observers.     

自主水下航行器的鲁棒自适应姿态控制算法

针对自主水下航行器（Autonomous Underwater Vehicle，AUV）在自动巡航任务中的姿态控制问题，提出了一种神经网络与滑模控制相结合的鲁棒自适应姿态控制算法。采用了RBF神经网络对AUV数学模型中的不确定项进行逼近，抑制了未建模动态和参数摄动的影响，进而基于反步法和滑模控制设计了姿态控制律，其中引入鲁棒项以克服外界干扰和神经网络逼近误差，并通过Lyapunov定理证明了控制系统的稳定性。将所设计的控制算法应用在AUV的姿态控制系统中进行数值仿真，验证了该控制算法的有效性和鲁棒性。     

Trajectory Tracking Control of an Underactuated Underwater Vehicle Redundant Manipulator System

The purpose of this study is to control the position of an underactuated underwater vehicle manipulator system (U‐UVMS). It is possible to control the end‐effector using a regular 6‐DOF manipulator despite the undesired displacements of the underactuated vehicle within a certain range. However, in this study an 8‐DOF redundant manipulator is used in order to increase the positioning accuracy of the end‐effector. The redundancy is resolved according to the criterion of minimal vehicle and joint motions. The underactuated underwater vehicle redundant manipulator system is modeled including the hydrodynamic forces for the manipulator in addition to those for the autonomous underwater vehicle (AUV). The shadowing effects of the bodies on each other are also taken into account when computing the hydrodynamic forces. The Newton‐Euler formulation is used to derive the system equations of motion including the thruster dynamics. In order to establish the end‐effector trajectory tracking control of the system, an inverse dynamics control law is formulated. The effectiveness of the control law even in the presence of parameter uncertainties and disturbing ocean currents is illustrated by simulations.     

面向水下阀门开闭的水下机械手轨迹规划仿真

为了实现水下机械手自主旋转水下阀门自动控制优化设计,研究了水下机械手的轨迹规划.水下机械手自主旋转水下阀门过程分为接近、旋转两部分.在机械手接近阀门的规划轨迹中,引入梯度投影法(GPM)优化关节角,并提出了一种新的配置GPM优化因子方案;在机械手旋转启闭阀门轨迹规划中,使用五次多项式完成关节空间轨迹优化并形成末端的圆形轨迹,结合几何法和欧拉角法求解运动学逆解;在Matlab中完成仿真,证明了GPM比加权最小范数法更好的关节角优化效果,实现较高的末端轨迹精度;验证了圆形轨迹和逆运动学理论的合理性.     

柔性机械手的鲁棒控制器设计

针对柔性机械手的动力学方程具有非最小相位的特点,运用重新定义的柔性机械手系统的输出,通过输入输出线性化,将系统分解为输入输出子系统和零动态子系统．考虑到柔性机械手系统存在的不确定性,设计终端滑模控制器,使输入输出子系统在有限时间内收敛到零．最优组合输出系数采用混沌遗传算法优化,以保证零动态子系统在平衡点附近渐近稳定,从而保证整个系统渐近稳定．仿真结果证明了设计方法的有效性．     

Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators

A general mobile modular manipulator can be defined as a m-wheeled holonomic/nonholonomic mobile platform combining with a n-degree of freedom modular manipulator. This paper presents a sliding mode adaptive neural-network controller for trajectory following of nonholonomic mobile modular manipulators in task space. Dynamic model for the entire mobile modular manipulator is established in consideration of nonholonomic constraints and the interactive motions between the mobile platform and the onboard modular manipulator. Multilayered perceptrons (MLP) are used as estimators to approximate the dynamic model of the mobile modular manipulator. Sliding mode control and direct adaptive technique are combined together to suppress bounded disturbances and modeling errors caused by parameter uncertainties. Simulations are performed to demonstrate that the dynamic modeling method is valid and the controller design algorithm is effective.