一类不确定运动系统的空间迭代学习控制

本文讨论了一类在有限空间区间内重复运行的不确定运动系统的跟踪控制问题.通过引入空间状态微分算子和空间复合能量函数,提出了一种空间周期的自适应迭代学习控制算法.首先利用空间状态微分算子,将系统从时间域转化到空间域形式.然后基于空间复合能量函数设计了控制器,利用含限幅作用的参数自适应律逼近系统中的不确定性,同时引入鲁棒项共同抑制非参数不确定性的影响.通过严格的数学分析,证明了在标准初始条件和随机有界初始误差两种情况下的跟踪误差收敛性.最后通过列车仿真进一步验证了该算法的有效性.     

一类不确定性离散非线性重复过程的迭代学习容错控制

针对一类具有非线性和执行器故障的重复运行不确定离散系统,提出了一种迭代学习鲁棒容错控制算法.首先通过定义执行器故障系数矩阵,将迭代学习控制过程转化为等价形式的不确定性非线性重复过程模型,然后基于混合李亚普若夫函数方法讨论非线性重复过程在时间轴和批次轴两个维度上的稳定性,并以线性矩阵不等式形式给出鲁棒容错控制器存在的充分条件和设计方法,同时保证系统正常和执行器故障情形下系统的容错稳定性能.最后,单杆机械手系统的输出跟踪控制仿真结果验证了本文算法的有效性.     

离散非线性系统的迭代学习轨迹跟踪鲁棒算法优化及应用

针对一类存在随机输入状态扰动、输出扰动及系统初值与给定期望值不严格一致的离散非线性重复系统,提出了一种P型开闭环鲁棒迭代学习轨迹跟踪控制算法．基于λ范数理论证明了算法的严格鲁棒稳定性,并通过多目标函数性能指标优化P型开闭环迭代学习控制律的增益矩阵参数,保证了优化算法下系统输出期望轨迹跟踪误差的单调收敛性,达到提高学习算法收敛速度和跟踪精度的目的．最后应用于二维运动移动机器人的实例仿真,验证了本文算法的可行性和有效性．     

基于单网络评判学习的非线性系统鲁棒跟踪控制

针对一类具有不确定性的连续时间非线性系统,提出一种基于单网络评判学习的鲁棒跟踪控制方法.首先建立由跟踪误差与参考轨迹构成的增广系统,将鲁棒跟踪控制问题转换为镇定设计问题.通过采用带有折扣因子和特殊效用项的代价函数,将鲁棒镇定问题转换为最优控制问题.然后,通过构建评判神经网络对最优代价函数进行估计,进而得到最优跟踪控制算法.为了放松该算法的初始容许控制条件,在评判神经网络权值更新律中增加一个额外项.利用Lyapunov方法证明闭环系统的稳定性及鲁棒跟踪性能.最后,通过仿真结果验证该方法的有效性和适用性.     

变参考轨迹下的鲁棒迭代学习模型预测控制

迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H_∞控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性.     

离散区间系统鲁棒容错控制器的设计

讨论了离散线性系统的鲁棒容错控制问题.针对离散线性系统,当系统矩阵和控制矩阵存在参数区间摄动时,通过Lyapunov稳定性理论分析了系统同时存在传感器失效情况下的鲁棒稳定问题,给出了系统鲁棒稳定的一个充分条件,并在此基础上给出离散区间系统鲁棒容错控制器的设计方法.最后用数值算例解释了该算法的有效性.     

一类线性离散切换系统的迭代学习控制

考虑具有任意切换序列线性离散切换系统的迭代学习控制问题. 假设切换系统在有限时间区间内重复运行, P型ILC算法可实现该类系统在整个时间区间内的完全跟踪控制. 采用超向量方法给出了算法在迭代域内收敛的条件, 并在理论上分析了的收敛性. 仿真示例验证了理论的结果.     

参考轨迹更新的点到点迭代学习控制算法优化及应用

针对受非重复扰动作用的离散线性系统的输出跟踪控制问题,提出一种基于参考轨迹更新的点到点迭代学习控制算法.首先通过构建性能指标函数对控制器进行范数优化,并给出相应的收敛性条件,使得系统输出能够跟踪上更新后参考轨迹处的期望点.其次,当系统输出端受到某批次非重复扰动的影响时,进一步通过引入拉格朗日乘子算法构造多目标性能指标函数,以优化鲁棒迭代学习控制器,达到提高收敛速度和跟踪精度的目的.最后将该算法应用于电机驱动的单机械臂控制系统中,仿真结果验证了算法的合理性和有效性.     

非线性不确定系统准最优学习控制

针对不确定非线性系统, 提出准最优学习控制方法, 解决参数与非参数不确定特性同时存在情形下的轨迹跟踪问题. 给出迭代学习与重复学习两种控制策略, 根据Sontag公式解决标称系统的优化控制, 并以鲁棒学习手段处理参数与非参数不确定特性. 提出断续函数连续化方案, 以避免传统Sontag公式在实现时可能存在的颤振问题. 分析证明经过足够多次迭代或足够多个周期的重复运行后, 闭环系统可实现系统状态以预设精度跟踪参考信号. 仿真结果表明所设计学习系统在收敛速度 方面快于非优化设计.     

含输入死区系统的滤波误差初始修正迭代学习控制

针对一类输入环节含死区非线性特性且误差初值非零的非参数不确定系统,提出滤波误差初始修正学习控制方案,分别解决死区斜率下限可知与未知两种情形下的轨迹跟踪问题.给出了两种修正滤波误差信号构造方法,并根据Lyapunov综合方法设计学习控制器,采用鲁棒学习策略处理非参数不确定性和死区非线性特性.经过足够多次迭代后,实现滤波误差在预设的作业区间也收敛于零.文中所提出的控制方案,具有构造简单与实施方便的特点,仿真结果表明了本文所提控制方法的有效性.     

Robust object tracking with occlusion handle

Occlusion is a major problem for object tracking algorithms, especially for subspace-based learning algorithms like PCA. In this paper, we introduce a novel incremental subspace (robust PCA)-based object tracking algorithm to deal with the occlusion problem. The three major contributions of our works are the introduction of robust PCA to object tracking literature, a robust PCA-based occlusion handling scheme and the revised incremental PCA algorithm. In order to handle the occlusion problem in the subspace learning algorithm framework, robust PCA algorithm is employed to select part of image pixels to compute coefficients rather than the whole image pixels as in traditional PCA algorithm, which can successfully avoid the occluded pixels and therefore obtain accurate tracking results. The occlusion handling scheme fully makes use of the merits of robust PCA and can avoid false updates in occlusion, clutter, noisy and other complex situations. Besides, the introduction of incremental PCA facilitates the subspace updating process and possesses several benefits compared with traditional R-SVD-based updating methods. The experiments show that our proposed algorithm is efficient and effective to cope with common object tracking tasks, especially with strong robustness due to the introduction of robust PCA.     

基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统设计

为了保证机器人能够在保持稳定的情况下,按照规划轨迹执行工作任务,从硬件和软件两个方面,设计了基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统。装设机器人传感器与状态观测器,改装机器人鲁棒滑模跟踪控制器,完成系统硬件设计;综合机器人结构、运动机理和动力机制3个方面,构建机器人数学模型;根据状态数据采集结果与规划轨迹之间的偏差,计算机器人跟踪控制量;依据滑模运动与切换方程,利用Sigmoid函数生成机器人鲁棒滑模控制律,将生成控制指令作用在机器人执行元件上,实现系统的鲁棒滑模跟踪控制功能;在系统测试与分析中,所设计控制系统的平均位置跟踪控制误差为0.93 mm,与设定轨迹目标基本重合,机器人姿态角跟踪控制误差为0.06 mm,具有较好的鲁棒滑模跟踪控制效果,能够有效提高机器人鲁棒滑模跟踪控制精度。     

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L ² tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L ² control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.     

Robust Backstepping Control of Robotic Systems Using Neural Networks

Neural network (NN) controllers for the robust back stepping control of robotic systems in both continuous and discrete-time are presented. Control action is employed to achieve tracking performance for unknown nonlinear system. Tuning methods are derived for the NN based on delta rule. Novel weight tuning algorithms for the NN are obtained that are similar to -modification in the case of continuous-time adaptive control. Uniform ultimate boundedness of the tracking error and the weight estimates are presented without using the persistency of excitation (PE) condition. Certainty equivalence is not used and regression matrix is not computed. No learning phase is needed for the NN and initialization of the network weights is straightforward. Simulation results justify the theoretical conclusions.     

非参数不确定系统约束迭代学习控制

讨论一类非参数不确定系统的约束迭代学习控制问题.构造二次分式型障碍李雅普诺夫函数(Barrier Lyapunov functions),用于学习控制器设计.控制方案采用鲁棒方法与学习机制相结合的手段处理非参数不确定性,鲁棒方法对处理后的不确定性的界予以补偿,学习机制对处理后的不确定性进行估计.可实现系统状态在整个作业区间上完全跟踪参考轨迹,并使得系统误差的二次型在迭代过程中囿于预设的界内,进而在运行过程中实现状态约束.提出的迭代学习算法包括部分限幅与完全限幅学习算法.采用这种BLF约束控制系统有利于提高控制系统中设备安全性.仿真结果用于验证所提出控制方法的有效性.     

Stabilizing position/force control of robots interacting with environment by learning connectionist structures

This paper is mostly concerned with the application of connectionist architectures for fast on-line learning of robot dynamic uncertainties used at the executive hierarchical control level in robot contact tasks. The connectionist structures are integrated in non-learning control laws for contact tasks which enable stabilization and good tracking performance of position and force. It has been shown that the problem of tracking a specified reference trajectory and specified force profile with a present quality of their transient response can be efficiently solved by means of the application of a four-layer perceptron. A four-layer perceptron is part of a hybrid learning control algorithm through the process of synchronous training which uses fast learning rules and available sensor information in order to improve robotic performance progressively in the minimum possible number of learning epochs. Some simulation results of the deburring process with robot MANUTEC r3 are shown to verify effectiveness of the proposed control learning algorithms.     

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.     

一类输出饱和系统的学习控制算法研究

传感器饱和是控制系统中较为常见的一种物理约束. 本文针对一类含饱和输出的受限系统, 提出了两种学习控制算法. 具体而言, 首先, 对于重复运行的被控系统, 设计了开环P型迭代学习控制器, 实现在有限时间区间内对期望轨迹的完全跟踪, 并在λ范数意义下分析了算法的收敛性, 给出了含饱和输出的迭代学习控制系统的收敛条件. 进而, 针对期望轨迹为周期信号的被控系统, 提出了闭环P型重复学习控制算法, 并分析了这类系统的收敛性条件. 最后, 通过一个仿真实例验证了本文所提算法的有效性.     

具有输入死区的非线性系统的鲁棒重复控制

针对一类输入含死区非线性特性的周期时变系统, 在周期时变参数不可参数化的情形下设计鲁棒重复控制器. 采用微分自适应律估计未知死区参数, 剩余的有界项通过鲁棒方法予以消除, 为避免出现颤振现象, 采用饱和函数替代符号函数. 在系统输出跟踪周期轨迹的情形下, 将非参数化不确定项转化为含周期时变参数的形式, 以达到利用周期学习律进行估计的目的. 理论分析与仿真结果表明, 采用部分饱和或全饱和学习算法均能实现输出误差有界收敛, 并保证闭环系统所有信号有界.     

Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.