基于迭代学习控制的移动机器人轨迹跟踪控制

为提高移动机器人对特定轨迹的重复跟踪能力,提出了采用开闭环PD型迭代学习控制算法对移动机器人进行轨迹跟踪控制的方法。建立了包含外界干扰的非完整约束条件下的轮式移动机器人运动学模型,给出了系统的控制算法和控制结构。仿真结果表明,采用开闭环PD型迭代学习控制算法对轨迹跟踪是可行有效的,收敛速度优于其他迭代学习算法。     

迭代学习在网络控制中的应用*

针对网络拥塞控制中网络拥塞本身无法建立精确的数学模型的问题,基于迭代学习控制具有结构简单及对系统精确模型不依赖等优点,首次提出了用迭代学习控制算法来解决网络拥塞,其主要目的是提高网络资源的利用率并提供给信源公平的资源分配份额。在提出算法前,首先通过分析网络模型建立了网络拥塞被控系统;然后提出了针对该被控系统的开闭环PID型迭代学习控制算法并证明了其收敛性;最后运用此算法建立了网络拥塞控制模型。通过实验和仿真表明,该算法对解决网络拥塞问题有很好的效果。     

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.     

非线性系统在任意初值下的PID型迭代学习控制

针对一类在有限时间区间上重复运行的非线性系统,给出了一种可以解决迭代学习控制中任意初值问题的PID型迭代学习算法及其收敛条件。采用算子理论证明了该算法的收敛性,结果表明该算法不仅有效解决了迭代学习控制的初值问题,而且放宽了收敛条件。仿真分析及与PD型迭代学习控制算法的仿真结果的对比证明,非线性系统在任意初值条件下经过PID型迭代学习后跟踪精度显著提高,输出误差曲线更快速趋于零,表明了该算法的有效性。     

一种新型的迭代学习控制器设计研究

针对一般连续系统的迭代学习控制问题进行了讨论,通过对常用的P型迭代学习控制算法的分析,在分析比较P型、PD型迭代学习控制律存在问题的基础上,提出了一种新型的迭代学习控制算法,利用误差信号以及相邻两次误差的差值信号对系统控制律进行逐次修正,既能避免PD型迭代算法由于微分作用而出现的不良影响,又可以充分地利用了系统已保存的有效信息,从而实现良好的跟踪效果以及较快的跟踪收敛速度,最后通过对一非线性连续系统的仿真,结果验证了算法相对于传统P算法的有效性与优越性.     

非线性系统迭代跟踪控制的批次遗忘学习算法

针对P型迭代学习算法对初始偏差和输出误差扰动敏感,以及PD型迭代学习算法容易放大系统噪声,降低系统鲁棒性的问题,研究了具有任意有界扰动及期望输出的重复运行非线性时变系统的PD型迭代学习跟踪控制算法.利用迭代学习过程记忆的期望轨迹、期望控制以及跟踪误差,给出基于变批次遗忘因子的学习控制器设计,并借助λ范数理论和Bellman-Gronwall不等式,讨论保证闭环跟踪系统批次误差有界的学习增益存在的充分必要条件,及分析控制算法的一致收敛性.本算法改善了系统的鲁棒性和动态特性,单关节机械臂的跟踪控制仿真验证了方法的有效性.     

水轮机调速系统模糊神经PID控制

提出一种模糊神经PID控制算法,该算法采用RBF网络对被控对象进行在线辨识,利用模糊神经网络在线调整PID控制参数。将该算法应用于水轮机调速系统,仿真结果表明该控制算法优于传统的PID控制算法。     

工业过程抗非重复性干扰改进的迭代学习控制

对于实际工业过程系统中存在的非重复性干扰,传统的PD型迭代学习控制不能很好地加以抑制.为此,提出加权PD型指数变增益加速闭环迭代学习控制算法.通过采集非重复性扰动信号,将其转化为设定值阶跃变化的序列,并采用改进的加权PD型指数变增益闭环算法,消除非重复性干扰,从而获得更为理想的系统输出,使控制系统的动态性能得到改善.算法研究表明,当迭代次数趋于无穷时,跟踪误差一致收敛到零.系统仿真验证了所提控制算法的有效性.     

基于支持向量机的TSK模糊模型辨识与控制

研究非线性系统TSK模糊模型的辨识与控制，利用TSK模型，可以将线性控制理论应用于非线性系统控制。基于支持向量机和递推最小二乘法，辨识出TSK模糊模型，并且通过遗传算法优化隶属度函数参数，最小化辨识误差。针对TSK模型进行控制，控制器包括两个部分：权重最大子系统反馈控制及其监督控制，监督控制保证了系统的稳定性。辨识和控制仿真结果证明了算法的有效性。     

二型Takagi-Sugeno-Kang模糊模型和不确定高斯混合模型的等价性

不确定的高斯混合模型和二型Takagi-Sugeno-Kang(TSK)模糊模型之间的对应关系被建立: 任何一个不确定的高斯混合模型都唯一对应着一个二型TSK模糊系统, 不确定的高斯混合模型的条件均值和二型TSK模糊 系统的输出是等价的. 基于此, 一种设计二型模糊系统的新方法被提出: 通过建立不确定的高斯混合模型确定二型TSK模糊系统, 即用概率统计的方法设计二型模糊系统. 仿真实验结果表明利用不确定高斯混合模型设计的二型模糊系统比其它模型具有更强的抗噪性和更快的速度.     

机械臂自调整因子模糊PD迭代学习控制研究

大部分模糊控制器不具有适应控制对象变化的能力,基于此设计一种自调整因子模糊控制器,并针对机械臂长时间重复操作导致运动精确度下降这一类问题,结合迭代学习控制方法,提出一种自调整因子模糊PD迭代学习控制方法;以双关节机械臂为研究对象,利用Fuzzy工具箱编写模糊控制规则,通过系统产生的误差以及误差的变化率作为模糊控制器的输入量调整模糊系统中的量化因子和比例因子,实现模糊规则的更新和对迭代学习控制中的PD参数的实时调整,系统的自适应性得到提高,并在Simulink中进行机械臂的运动控制实验,仿真结果表明,所提控制方法最终产生的误差可以精确到0.0001 rad,同时在进行第2次迭代时关节角度和角速度误差收敛基本趋于零,整体的控制效果较好。     

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.     

Robust TSK fuzzy modeling for function approximation with outliers

The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches     

Bayesian zero-order TSK fuzzy system modeling

Different from the existing TSK fuzzy system modeling methods, a novel zero-order TSK fuzzy modeling method called Bayesian zero-order TSK fuzzy system (B-ZTSK-FS) is proposed from the perspective of Bayesian inference in this paper. The proposed method B-ZTSK-FS constructs zero-order TSK fuzzy system by using the maximum a posteriori (MAP) framework to maximize the corresponding posteriori probability. First, a joint likelihood model about zero-order TSK fuzzy system is defined to derive a new objective function which can assure that both antecedents and consequents of fuzzy rules rather than only their antecedents of the most existing TSK fuzzy systems become interpretable. The defined likelihood model is composed of three aspects: clustering on the training set for antecedents of fuzzy rules, the least squares (LS) error for consequent parameters of fuzzy rules, and a Dirichlet prior distribution for fuzzy cluster memberships which is considered to not only automatically match the “sum-to-one” constraints on fuzzy cluster memberships, but also make the proposed method B-ZTSK-FS scalable for large-scale datasets by appropriately setting the Dirichlet index. This likelihood model indeed indicates that antecedent and consequent parameters of fuzzy rules can be linguistically interpreted and simultaneously optimized by the proposed method B-ZTSK-FS which is based on the MAP framework with the iterative sampling algorithm, which in fact implies that fuzziness and probability can co-jointly work for TSK fuzzy system modeling in a collaborative rather than repulsive way. Finally, experimental results on 28 synthetic and real-world datasets are reported to demonstrate the effectiveness of the proposed method B-ZTSK-FS in the sense of approximation accuracy, interpretability and scalability.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

高相对阶连续时间系统的间接迭代学习控制

本文提出了一类高相对阶线性连续时间系统的间接迭代学习控制算法,该算法相对独立于系统局部控制器,因此可以应用于已有局部反馈控制器的系统.采用具有极点配置的H∞鲁棒控制器作为系统的内环控制,而在外环通过迭代学习控制调整内环系统的指令信号.通过引入拉氏变化,构建了迭代学习系统的2-D Roesser模型,推导了系统渐近收敛条件,并研究了存在有界初始条件偏移和迭代变化外部干扰时算法的鲁棒性能.最后,利用空中加油对接控制的算例进一步验证了算法的有效性.     

基于BP神经网络的迭代学习初始控制策略研究

针对传统迭代学习控制(ILC)在面临新的环境或控制任务时学习时间长、收敛速度慢的问题,提出基于BP神经网络的迭代学习初始控制策略。通过BP神经网络拟和经验数据,对以往控制经验加以充分利用,避免了对初始控制输入量的盲目选择。仿真验证了方法的可行性和有效性。     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional–derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) – chosen as the feedforward filter – and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.