考虑输出约束的机械臂自适应神经网络鲁棒控制

在工业机械臂系统的跟踪控制过程中,由于其结构和工作环境复杂,导致难以建立精确的系统模型,针对此问题提出了基于多层前馈神经网络的自适应鲁棒控制器.通过神经网络在线估计机械臂系统动力学模型,并在控制器中进行补偿,同时设计了一个在线更新的鲁棒项克服神经网络的重构误差;考虑机械臂实际系统的输出约束,采用障碍李雅普诺夫函数设计控制律并证明系统的稳定性从而使系统满足约束条件.仿真实验结果表明:在约束条件下所提出的控制器能够实现系统的一致最终有界稳定,且跟踪性能良好,并具有很好的抗干扰和自适应能力.     

非完整移动机械臂的鲁棒跟踪控制

针对非完整移动机械臂惯性参数的不确定性,采用滑模控制为其设计了输出跟踪控制器。首先给出了包括驱动电机动态特性的非完整移动机械臂的简化动态模型,然后通过微分同胚和输入变换将其分解为4个低阶子系统,并给出了其输出跟踪的滑模控制器设计方法。仿真实验表明,所设计的鲁棒控制器能很好地跟踪给定轨迹。     

周期时变系统的鲁棒自适应重复控制

针对周期时变系统,提出一种鲁棒自适应重复控制方法.该方法利用周期学习律估计周期时变参数,并结合鲁棒自适应方法处理非周期不确定性.与现有重复控制不同的是,在控制器设计中引入了新变量—周期数,利用周期系统的重复特性,使界的逼近误差随周期数的增加而逐渐减少,保证了系统的全局渐近稳定性.同时将该方法应用于一类非线性参数化系统,使系统在非参数化扰动的情形下,输出误差仍能收敛于0,倒立摆模型的仿真验证了此结果.该设计方法适用于消除神经网络逼近误差对重复控制系统的影响,理论证明了基于神经网络的鲁棒自适应重复控制系统中所有变量的有界性和输出误差的渐近收敛性,关于机械臂模型的仿真结果验证了受控系统具有良好的跟踪性能.     

轮式移动机械臂的鲁棒跟踪控制研究

移动机械臂系统一般由移动平台和机器臂组成,它既具有机器臂的操作灵活性,又具有移动机器人的可移动性,因此其应用范围要比单个系统宽得多。这篇文章研究了由非完整移动平台和完整机械臂构成的移动机械臂系统的鲁棒跟踪控制问题,基于误差动态方程和耗散不等式引理设计了一种鲁棒跟踪控制器,该控制器在出现外界干扰时能使系统渐近跟踪给定信号。使用Matlab6.5对系统进行了仿真研究,仿真结果表明所提出的鲁棒控制算法是正确有效的。     

基于自适应动态规划的非线性鲁棒近似最优跟踪控制

为克服现有近似最优跟踪控制方法只能跟踪连续可微参考输入的局限,本文针对一类具有未知动态的连续时间非线性时不变仿射系统,提出了一种新的基于自适应动态规划的鲁棒近似最优跟踪控制方法.首先采用递归神经网络建立系统模型,然后建立评价神经网络对最优性能指标进行估计,从而得到最优性能指标偏导数的估计值,进而得到近似最优跟踪控制器,最后利用系统输出与参考输入之间的跟踪误差设计鲁棒项对神经网络建模误差进行补偿.分别针对两个非线性系统进行仿真实验,仿真结果表明了所提方法的有效性和优越性.     

Delta算子系统动态输出反馈D-稳定鲁棒协方差控制

研究Delta算子描述的线性不确定系统基于动态输出反馈的D-稳定鲁棒协方差控制问题.设计动态输出反馈控制器,使Delta算子不确定系统鲁棒D-稳定,且稳态输出协方差矩阵具有给定上界.利用线性矩阵不等式(LMI)方法,给出D-稳定鲁棒协方差控制器存在的充分条件.在此基础上,提出相应控制器的设计算法.数值算例表明了该设计方法的可行性.     

基于一阶不确定项估计律的机械臂鲁棒控制研究

针对多自由度机械臂控制系统的模型参数误差、关节摩擦力以及外部输入扰动等不确定项,设计了一类一阶误差估计律;结合基于机构动力学名义模型的输入输出反馈线性化控制算法,对六自由度刚性机械臂的时变轨迹跟踪控制进行了研究,理论上证明了设计的鲁棒控制器是全局渐进稳定的.仿真结果表明该控制策略对系统的各类不确定项具有很好的鲁棒性,能够实现高精度的轨迹跟踪控制.     

基于RBF神经网络的机械臂自适应控制方法

针对机械臂受内部摩擦和时变扰动等不确定性因素的影响,其轨迹跟踪控制系统的跟踪精度会下降,且影响系统的稳定性,提出一种基于径向基函数神经网络的自适应控制方法。首先,利用RBF神经网络采用离线训练和在线学习的方式对机械臂的动力学模型进行辨识;其次针对机械臂控制系统中的摩擦,设计RBF神经网络自适应控制算法对其进行逼近得到补偿控制量。针对时变扰动和神经网络逼近误差设计鲁棒项,以克服众多不确定性因素带来的影响,同时通过构造李亚普诺夫函数对所设计的控制系统进行稳定性分析;最后,仿真实验结果证明提出的控制方法具有较高的跟踪精度、抗干扰能力和较强的鲁棒性。     

一类非线性不确定系统的鲁棒H∞控制

利用Hamilton-Jacobi不等式讨论了一类含不确定项的非线性控制系统的鲁棒H∞ 控制问题,分别得到了状态反馈控制器和动态输出反馈控制器,使得相应的闭环系统具有鲁 棒H∞特性的充分条件,并且给出了它们的具体设计.     

随机时滞系统的神经网络输出反馈动态面控制

针对一类具有未知控制方向的随机时滞系统设计自适应神经输出反馈控制器.首先,利用状态观测器估计不可测量的系统状态;其次,选择合适的Lyapunov-Krasovskii函数消除未知延迟项对系统的影响,利用Nussbaum-type函数处理系统的未知控制方向问题,通过神经网络逼近未知的非线性函数,以及用动态表面控制(DSC)解决控制器设计中出现的复杂性问题;最后,通过Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,可以保证闭环系统中所有信号在二阶或四阶矩意义下一致最终有界,跟踪误差能收敛到零值小的领域内.仿真实例验证了所提出方法的有效性.     

Wavelet neural networks robust control of farm transmission line deicing robot manipulators

This paper shows some new results for the wavelet neural networks robust control of farm transmission line deicing robot manipulators (FTLDRM). A wavelet neural network is used to approximate the unknown model uncertainties and disturbances, and an adaptive robust compensator is given to compensate the lumped uncertainty. Based on the stability and neural network approximation theory, several sufficient conditions are provided which guarantee the convergence of the feedback error system. Both simulation and experimental results are given to show the superior performance of the proposed intelligent control method.     

Adaptive compliant force–motion control of coordinated non-holonomic mobile manipulators interacting with unknown non-rigid environments

Most studies on the coordination control of multiple mobile manipulators system assume exact knowledge of system dynamics and deal only with motion control. However, actual applications may involve the tasks in which multiple coordinated mobile manipulators system interacts with rigid or non-rigid working surfaces. In this paper, we consider multiple mobile manipulators grasping a rigid object in contact with a deformable working surface, whose geometric and real physical parameters are unknown but boundedness of physical parameters is known. The contact forces are nonlinear and difficult to model. A neuro-adaptive control for coordinated mobile manipulators is proposed for robust force/motion tracking. The control law is based on the philosophy of the parallel approach, in which the control problem is divided into three subspaces and the adaptive techniques are employed to deal with the uncertain environmental constraints, disturbances, and unknown robotic and object dynamics. The proposed adaptive force–motion controller guarantees the tracking errors of motion and force trajectories converge to zero. Simulation examples are presented to verify the effectiveness of the proposed control.     

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.     

漂浮基空间机器人的径向基神经网络鲁棒自适应控制

针对一类同时具有参数及非参数不确定性的自由漂浮空间机器人系统的轨迹跟踪问题,采用了一种RBF神经网络的自适应鲁棒补偿控制策略.对于系统的参数不确定性,通过对径向基神经网络来自适应学习并补偿,逼近误差通过滑模控制器消除,神经网络权重的自适应修正规则基于Lyapunov函数方法得到;而非参数不确定通过鲁棒控制器来实时自适应...     

一类不确定非完整移动机械臂的鲁棒镇定

讨论了一类带有未知惯性参数,未建模动态及外界干扰的非完整动力学系统的鲁棒镇定问题．基于滑模控制思想及非完整运动学系统的镇定策略,给出了该类系统的鲁棒镇定方法．将其用于一类不确定非完整移动机械臂的鲁棒镇定分析,仿真结果验证了所提出控制方法的正确有效性．     

自适应神经网络四旋翼无人机有限时间轨迹跟踪控制

针对带有模型不确定性和未知外部干扰的四旋翼无人机轨迹跟踪控制问题,提出一种基于径向基(radial basis function, RBF)神经网络的自适应全局快速终端滑模控制方法,确保系统对期望轨迹的有限时间跟踪。该方法考虑到全局快速终端滑模控制在实际应用中的适应性和抖振问题,利用RBF神经网络替代等效控制量,以神经网络的在线学习能力补偿系统内部的不确定性和未知的外部干扰,有效地降低了系统的抖振;根据Lyapunov方法导出的自适应律在线调整神经网络权值,以保证闭环系统的稳定性。通过一系列仿真算例和飞行实验验证了该方法的有效性与可行性,结果表明:该控制方法相对于滑模控制的抖振更小,具有更好的收敛性和抗干扰能力,同时对模型的参数摄动具有更强的鲁棒性。     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

Neural-network control of mobile manipulators

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.     

可重构机械臂反演时延分散容错控制

针对存在模型参数不确定性的可重构机械臂系统执行器故障,提出一种基于反演设计与时延技术相结合的容错控制方法.该方法利用反演设计的基本思想,通过神经网络补偿子系统动力学模型中的参数不确定项和关联项.利用时延控制的逼近能力来补偿执行器的故障,使得故障发生时能及时实现容错控制.该方法具有不需要在线进行故障诊断的特点,仿真结果表明了所提出控制方法的有效性.