分域设计迭代学习控制器抑制非重复性扰动

为了抑制迭代方向上已知重复样式的非重复性输出扰动,提出了迭代学习控制(Iterative Learning Control,ILC)的分域算法。时间域内设计传统PID型迭代学习控制器,并且优化其参数;迭代域内利用内模原理抑制非重复性输出扰动,跟踪期望轨迹;利用加权思想将两者相结合,得到迭代学习控制器的分域设计算法。相对于已有算法,建立了针对一般扰动的设计框架,并且合理配置了算法的参数,使收敛速度及精度有所提高。仿真结果说明了该算法的有效性。     

煤层气发动机空燃比 PID 迭代学习控制

煤层气发动机优化控制问题,对预混合双阀控制,煤层气发动机空燃比前馈控制查询表需要稳态标定试验得到,传统的方法是进行反复的测试和离线修正来获得最优控制数据.为了降低试验工作量及其耗费,提出了反馈信息在线校正前馈控制脉谱的方法,采用迭代学习控制技术,分别设计了 PID 和模糊自适应整定控制器.利用辨识的发动机稳态模型,研究了不同初始状态下两种学习控制器的收敛性能和稳态空燃比校正效果.仿真结果表明,模糊自适应整定 PID 学习控制算法具有较快的收敛速度和较小的学习误差,更适合煤层气发动机位置控制参数的在线应用.     

气动人工肌肉关节的迭代反馈整定控制及优化

气动人工肌肉关节（PMA）具有低成本、柔顺性和与生物肌肉类似的力学特性等优点，在医疗设备、仿生机器人等领域得到广泛应用.本文针对气动人工肌肉充气变形过程中存在的强非线性、时变性和控制参数难以确定的问题，提出了一种基于迭代反馈整定（IFT）算法的数据驱动优化控制策略，直接基于系统的输入输出数据，定义跟踪性能准则函数并采用Gauss-Newton估计算法实现对PID控制器参数的迭代整定，并通过引入辅助因子获取性能准则函数加权因子的最优值进一步加快了IFT算法的收敛速度.仿真结果表明，该方法相对于Ziegler-Nichols等传统PID参数整定方法可以有效提高控制系统的跟踪性能和鲁棒性.     

增强学习的PID控制参数优化快速整定算法

为了解决利用粒子群算法对非线性和不确定系统进行PID控制参数整定时存在的种群多样性较低、控制参数在线学习能力差等问题,提出了增强学习的PID控制参数优化快速整定算法;首先,对进化学习算法进行改进;然后利用神经网络进行混合PID控制器设计,利用增强学习算法进行在线反馈学习;最后对每次种群进化后的多样性进行了自适应变异;通过对输入曲线的跟踪对比,验证了文中算法的参数整定效果,同时对种群的多样性进行了跟踪仿真;仿真实验表明,文中的算法具有较强的鲁棒性,算法收敛速度较快,整定效果较高。     

基于蚁群算法固定翼无人机PID参数控制方法

针对传统固定翼无人机PID控制器比例、积分和微分参数调节控制精度低,响应速度慢,难以得到最优线性PID参数组合等问题。本文利用蚁群算法寻优搜索对传统PID控制器进行改进,本文将PID参数寻优过程转化为多约束条件组合优化问题,并通过蚁群算法针对PID参数整定多次迭代来进行搜索最优数值路径来更加快速,精确的优化PID线性组合参数值,提高对固定翼的精确PID参数控制。     

基于初态修正的ILGA控制器参数整定

针对迭代学习遗传算法（ILGA）对PID控制器参数进行整定时要求系统初态等于理想初态的问题，采用在迭代前对实际初态自学习逼近理想初态的方法，放松了迭代学习遗传算法的应用条件，并具有迭代学习次数少，整定PID控制器的最佳控制参数快的特点．简化了控制器的设计过程。给出仿真算例显示了该方法在PID控制器参数自动寻优上的有效性。     

基于粒子群优化算法对PID参数的优化整定

本文首先介绍了PID控制器,在此基础上提出了一种基于智能群算法对PID控制器的比例、积分、微分三个参数进行优化整定的改进PSO算法,并利用Mat lab对PID工业控制器进行模拟仿真,利用仿真曲线进行直观的对比。通过与标准PSO优化算法及常规的Z-N整定法的比较,结果表明基于改进PSO算法对PID的整定方法不仅能快速的从全局搜索出优化的整定参数,而且也能够大大地提升整定效果。实验结果也表明该算法具体良好的收敛速度和稳定性,是一种具有高控制精度、高稳定性和快速性的PID整定算法。     

基于迭代学习的PID控制研究

本文利用迭代学习控制过程记忆的期望控制ud（t）、期望轨迹yd（t）以及跟踪误差ek（t）提出了拟合系统PID控制器参数的方法。这种方法实现的PID控制器结构简单,作用于系统可获得较佳的动态特性和较强的鲁棒性。仿真实例表明,这种方法具有很好的可行性和实用性。     

基于初次控制信号提取的迭代学习控制方法

在同一迭代学习控制(Iterative learning control, ILC)系统中,选取一个合适的初次迭代控制信号相对于从零开始学习达到目标跟踪精度的迭代次数更少.本文针对线性系统研究从历次轨迹跟踪控制信息中通过期望轨迹匹配提取初次迭代控制信号的方法.首先提出了一种轨迹基元优化匹配算法,在满足一定相似度的情况下,通过轨迹分割、平移与旋转变换,在轨迹基元库中寻找与当前期望轨迹叠合的轨迹基元组合轨迹;进而,依据线性叠加原理和轨迹叠合的平移矢量与旋转变换矩阵,获取与期望轨迹叠合的轨迹基元控制信号;在此基础上,通过轨迹基元控制信号串联组合和时间尺度变换,提取出当前期望轨迹的初次迭代控制信号.对于初次迭代控制信号在拼接处由边界条件差异引起的干扰,给出了一种H∞反馈辅助ILC方法.最后,在XYZ三轴运动平台实现所提算法,实验结果表明本文所提方法的有效性.     

基于异步优势执行器评价器的自适应PID控制

自适应PID较好地解决了传统PID无法自整定参数的问题,已成为控制领域内的研究热点。研究基于异步优势执行器评价器(Asynchronous Advantage Actor-Critic,A3C)算法设计了一种新的自适应PID控制器。该控制器利用A3C结构的多线程异步学习特性,并行训练多个执行器评价器(Actor-Critic,AC)结构的智能体,每个智能体采用多层前馈神经网络逼近策略函数和值函数实现在连续动作空间中搜索最优的参数整定策略,以达到最佳的控制效果。与已有的多种自适应PID控制器性能对比分析结果表明该方法具有收敛速度快,自适应能力强的特点。     

Robust adaptive control of a bio-inspired robot manipulator using bat algorithm

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.     

数据丢失下多智能体系统迭代学习跟踪控制

考虑数据丢失下非线性多智能体系统的一致性跟踪问题。假设多智能体系统使用固定网络通信拓扑结构,由于通信网络自身限制导致多智能体系统中存在数据丢失现象。将数据丢失现象描述为取值0/1的随机伯努利序列,设计分布式一致性跟踪误差,提出该系统在数据丢失下的P型迭代学习控制算法。采用压缩映射的方法给出收敛性条件,并在理论上分析了跟踪误差的收敛性。仿真结果表明,提出的算法可以实现该系统在有限时间区间上对期望轨迹的完全跟踪,验证了算法的有效性。     

Compensation of hysteresis in piezoelectric actuator with iterative learning control

This paper presents the application of iterative learning control (ILC) to compensate hysteresis in a piezoelectric actuator. The proposed controller is a hybrid of proportional-integral-differential (PID) control, whose main function is for trajectory tracking, and a chatter-based ILC, whose main function is for hysteresis compensation. Stability analysis of the proposed ILC is presented, with the PID included in the dynamic of the piezoelectric actuator. The performance of the proposed controller is analysed through simulation and verified with experiment with a piezoelectric actuator.     

Iterative learning control for position tracking of a pneumatic actuated X–Y table

The iterative learning control (ILC) obtains the unknown information from repeated control operations. Meanwhile, the tracking error from previous stages is used as the correction factor for the next control action. Therefore, the ILC controller can make the system tracking error converge to a small region within a limited number of iterations. This study builds a proportional-valve-controlled pneumatic X–Y table system for performing position tracking control experiments. The experiments involve implementing the ILC controllers and comparing the results. The P-type updating law with delay parameters is used for both the x- and y-axes in the repetitive trajectory tracking control. Experimental results demonstrate that the ILC controller can effectively control the system and track the desired circular trajectory at different speeds. The control parameters are varied to investigate their effects on the ILC convergence.     

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.      

局部对称积分型迭代学习控制

提出了一个新的迭代学习控制（ＩＬＣ）更新律用于连续线性系统的有限时间区间跟踪控制,迭代学习控制作为一个前馈控制,迭代学习控制作为一个前馈控制器加在已有的反馈控制器之上,对于上倥 的反馈控制信号作局部对称积分,所提出的迭代学习控制更新律具备较简单的形式且仅含有两个设计参数,即：学习增益和局部积分的区间长度,给出了收敛性分析以及设计步骤。     

执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

Iterative Learning Control Via Weighted Local-Symmetrical-Integration

A new iterative learning control (ILC) updating law is proposed for tracking control of continuous linear system over a finite time interval. The ILC is applied as a feedforward controller to the existing feedback controller. By using the weighted local symmetrical integral (WLSI) of feedback control signal of previous iteration, the ILC updating law takes a simple form with only two design parameters: the learning gain and the range of local integration. Convergence analysis is presented together with a design procedure. A set of experimental results are presented to illustrate the effectiveness of the proposed WLSI-ILC scheme.