基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function（径向基函数）神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

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.     

滑动与打滑条件下的轮式移动机器人自抗扰跟踪控制

针对具有未知的滑动与打滑的轮式移动机器人(WMR),提出了一种基于自抗扰思想的跟踪控制策略.首先建立了滑动与打滑条件下的轮式移动机器人动力学模型.其次,由反步法设计运动学控制器,基于模型设计线性扩张观测器和动力学控制器,并给出了控制器稳定性分析.最后与积分滑模控制进行了仿真对比,结果表明该控制方法的误差收敛速度更快.观测器能够精确估计滑动与打滑及动力学不确定性对机器人的扰动,提高了轮式移动机器人轨迹跟踪的鲁棒性.     

基于介电弹性体驱动器的软体机器人建模与跟踪控制

针对基于介电弹性体驱动器的软体机器人的跟踪控制问题,本文提出一种自适应鲁棒控制策略.根据虚功原理建立介电弹性体驱动器的动力学模型,模型中弹性势能部分采用Gent模型进行描述.考虑到介电弹性体驱动器的精确模型参数难以获取,使用基于径向基神经网络的逼近器对模型中的未知项进行估计.同时,考虑到介电弹性体驱动器形变量的变化率难以被测量,设计状态观测器对系统未知状态量进行观测.根据逼近器的估计结果和状态观测器的观测结果,设计滑模控制器实现介电弹性体驱动器的跟踪控制目标.最后,通过数值仿真实验验证所提控制策略的有效性.     

具有非匹配扰动的非线性变参数系统模型参考跟踪控制

本文研究了一类含有非匹配扰动的非线性变参数系统的跟踪控制问题.首先,设计非线性扰动观测器用于估计系统所受到的未知扰动.其次,在前馈–反馈跟踪控制器中引入扰动补偿控制项,提出一种基于扰动观测器的跟踪控制策略.利用依赖于状态和时变参数的线性矩阵不等式,导出保证闭环系统输入–状态稳定的充分条件,进而运用平方和凸优化技术解析地构造出扰动观测器和跟踪控制器.通过理论证明,所设计的控制策略能够实现非线性变参数系统输出对参考模型输出的跟踪,消除输出通道中非匹配扰动的影响.最后,由数值仿真例子验证了所提方法的有效性.     

具有电液执行器的主动悬架神经网滑模控制

提出了一种新型兼顾乘坐舒适性和操纵稳定性的主动悬架自适应神经网络滑模控制器。首先建立了考虑电液执行器动力学特性的非线性悬架模型。由于执行器具有未知时变的非线性,该控制器被分为两个回路。在内环,采用自适应径向基函数（Radial Basis Function,RBF）神经网络使具有控制器对不确定性的鲁棒性,并根据李雅普诺夫稳定性理论得到的更新律在线调整自校正加权向量。在外环,结合了天棚地棚混合控制策略设计了一个模型参考滑模控制器（Model Reference Sliding Mode Control,MRSMC）来跟踪混合参考模型的状态。此外,还为估计不可测信号设计了基于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的状态观测器。仿真结果表明,即使存在不确定性,控制器也可以精确地跟踪所受的力。仿真结果表明,该方法在存在未知因素时能够有效地提高悬架性能。     

具有未知负载扰动的水井钻机电液伺服系统无模型自适应控制

针对电液伺服系统在水井钻机推进工况下存在的参数不确定以及未知负载扰动突变等非线性因素,提出了基于径向基(RBF)神经网络扰动观测器的无模型自适应控制方法.首先,通过改进的无模型自适应控制动态线性化方法,将被控系统线性化为与输入输出相关的增量形式,并将未知负载扰动合并到一个非线性项中;然后,设计了径向基神经网络扰动观测器对含有未知负载扰动的非线性项进行估计,作为对未知扰动的补偿;最后,设计了时变参数估计律,通过在线调整伪偏导数,给出了电液伺服系统的控制更新律.仿真结果表明,所设计的控制器能够对未知负载扰动突变进行补偿,并能确保跟踪误差有界收敛.     

基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制

针对速度不可测的三自由度欠驱动船舶轨迹跟踪控制问题,考虑船舶存在模型参数不确定项以及外界环境干扰未知情况,提出一种基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制策略.该策略构造扩张观测器估计船舶速度向量,利用神经网络算法逼近模型参数不确定项,然后采用动态面控制技术避免对虚拟控制律直接求导,简化控制律计算过程,并引入低频增益学习技术消除外界扰动导致控制信号产生高频振荡,最后选取李雅普诺夫函数证明该控制律能够保证船舶跟踪闭环系统中所有误差信号一致最终有界.仿真结果表明,本文所设计控制器对船舶模型参数不确定项及外界环境干扰具有较强的鲁棒性,能够实现对船舶轨迹的有效跟踪.     

基于神经网络的鲁棒自适应控制

考虑摩擦及外界干扰的情况下，针对具有不确定性参数的机器人系统，提出一种基于神经网络动态补偿的鲁棒自适应控制策略，采用神经网络在线补偿控制器以克服系统的外部扰动，未建模动力学部分等非参数不定性带来的影响，从而提高了系统的动态性能和稳态精度，并对闭环系统稳定性进行了证明，仿真结果表明，所提方法具有良好的跟踪性能和较强的鲁棒性。     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。     

Robust adaptive neural tracking control for a class of electrically driven robots with time delays

This article addresses the problem of designing the robust tracking control for a class of uncertain electrically driven robots with time delays. The unknown time-delay uncertainty is assumed to be bounded by a function of all the state variables. By suitably choosing the Lyapunov–Krasovskii functionals, a novel adaptive/robust neural tracking control scheme is developed for the first time such that all the states and signals of the closed-loop time-delay robot system are bounded and the tracking error is shown to be uniformly ultimately bounded. By suitably designing the embedded current signal, the effect of time-delay uncertainty in the mechanical dynamics does not require to be incorporated into the current tracking error dynamics, and so the Lyapunov–Krasovskii functionals can be easily constructed in the stability analysis. Compared with the previous investigations of controlling robots the control scheme developed here can be extended to handle a broader class of electrically driven robots perturbed simultaneously by plant uncertainties, time-varying perturbations, and time-delay uncertainties. Finally, simulation examples are made to demonstrate the effectiveness of the proposed control algorithm.     

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

基于改进的FNNs移动机械臂输出反馈跟踪控制

针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制.     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

基于自适应滑模的欠驱动无人艇轨迹跟踪控制算法

针对欠驱动水面无人艇在航行过程中存在的海洋环境干扰、数学模型参数不确定、执行器故障等问题,提出了一种基于扰动观测器与神经网络技术的自适应滑模轨迹跟踪策略。在无人艇三自由度模型的基础上,结合视线制导率,提出了一种新的轨迹跟踪制导策略。采用自适应滑模控制技术设计了欠驱动无人艇轨迹跟踪控制器,有效地抑制了执行器衰减故障对无人艇控制系统的影响;同时运用了非线性扰动观测器和自适应径向基函数神经网络分别对无人艇受到的外界干扰和模型参数不确定性进行补偿和拟合,提高了控制系统的抗干扰能力。基于Lyapunov定理证明了所设计的控制系统的稳定性,并在MATLAB中进行了仿真测试。仿真结果表明,所提出的轨迹跟踪控制算法可以在较为复杂的环境下实现对欠驱动无人艇的精准控制;相较于对比算法,位置的平均跟踪误差减小了80%以上,具备较高的稳定性和鲁棒性。     

Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.     

Adaptive neural network output feedback control for flexible multi-link robotic manipulators

In this paper, a novel approach for adaptive control of flexible multi-link robots in the joint space is presented. The approach is valid for a class of highly uncertain systems with arbitrary but bounded dimension. The problem of trajectory tracking is solved through developing a stable inversion for robot dynamics using only joint angles measurement; then a linear dynamic compensator is utilised to stabilise the tracking error for the nominal system. Furthermore, a high gain observer is designed to provide an estimate for error dynamics. A linear in parameter neural network based adaptive signal is used to approximate and eliminate the effect of uncertainties due to link flexibilities and vibration modes on tracking performance, where the adaptation rule for the neural network weights is derived based on Lyapunov function. The stability and the ultimate boundedness of the error signals and closed-loop system is demonstrated through the Lyapunov stability theory. Computer simulations of the proposed robust controller are carried to validate on a two-link flexible planar manipulator.