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1.
非线性分布参数系统跟踪控制的学习算法   总被引:13,自引:3,他引:10  
尝试性地将学习控制方法用于一类非线性分布参数系统的跟踪控制上,分别获得了 系统轨线于L2(Ω)空间,W1,2(Ω)空间中跟踪期望目标的结果.所给的学习算法避免了其收敛 性要依赖于理想输入ud(x,t)这一不确定的条件,且对系统的非线性要求只是定性的而不是 定量的,从而使得控制具有很强的鲁棒性能.  相似文献   

2.
非线性系统的迭代学习控制及其算法实现   总被引:7,自引:1,他引:7       下载免费PDF全文
研究了非线性系统的学习控制方法. 首先, 对学习控制方法在目前发展中所存在的一些问题进行了分析; 在此基础上, 通过引进新的(λ,ξ)范数及新的算法, 克服了这一理论研究中所存在的一些困难, 避免了以上问题的出现, 获得了控制算法全局收敛和目标跟踪精度较高的结果. 而且对所给算法的可实现性问题进行了分析.  相似文献   

3.
一类退化系统目标跟踪学习控制的吸引流形方法   总被引:1,自引:0,他引:1       下载免费PDF全文
针对一类退化系统目标跟踪的迭代学习控制问题进行了探讨.这类系统不满足目前对迭代学习控制通常所要求的收敛性条件,从而使得学习控制方法在这类系统上的应用遇到困难.为了解决这类问题,提出了一种新的设计方案——吸引流形方法.通过构造一个相应于所给系统稳定而吸引的流形,且在构造的过程中同时设计出学习控制函数序列,以使完成对所给期望目标的跟踪.同时也讨论了这种方法的可实现问题.另外,该方法可无本质困难地应用到相应的非线性系统上.  相似文献   

4.
讨论迭代初态与期望初态存在固定偏移情形下 的迭代学习控制问题, 提出带有反馈辅助项的PD型迭代学习控制算法, 可实现系统输出对期望轨迹的渐近跟踪. 为了进一步实现输出轨迹在预定有限区间上对期望轨迹的完全跟踪, 提出分别带有初始修正作用和终态吸引的学习算法. 文中给出所提出的学习算法的极限轨迹, 并对学习算法进行收敛性分析, 推导出收敛性充分条件, 可用于学习增益的确定. 通过数值结果, 验证所提学习算法的有效性.  相似文献   

5.
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.  相似文献   

6.
针对一类线性时不变系统,讨论存在固定初始偏移时的学习控制问题,提出带有反馈辅助项的比例微分(proportion differentiation,PD)型学习控制算法,分析所提算法在Lebesgue-p范数意义下的单调收敛性,获得对期望轨迹的渐近跟踪结果.进一步地,为获得系统输出对期望轨迹的完全跟踪,给出带有初始修正策略的比例–积分–微分(proportion multiple integration differentiation,PMID)型学习律,并给出了所提学习算法的单调收敛性能分析结果.最后,通过数值结果,验证了所提学习算法的跟踪性能和单调收敛性能.  相似文献   

7.
为了提高航迹控制器的跟踪性能,节省航程,本文推导了期望航向和航迹偏差的数学模型,设计了可预测期望航向的船舶航迹预测控制器,它不仅能预测系统的输出(船首向和航迹偏差),而且可以提前多步预测系统的参考输入(期望航向),以提前修正命令舵角.对货船的航迹控制计算机仿真结果表明,算法提高了控制器的跟踪速度和精度,缩短了船舶的实际航程.本文还给出了求解控制器中逆矩阵的递推算法,其计算量为通常算法的1/3.  相似文献   

8.
终端受限机器人系统轨道跟踪的新控制算法   总被引:1,自引:1,他引:0  
研究一类终端受限机器人系统的控制问题,针对系统的轨道跟踪控制给出了一种新 的学习控制算法.该算法克服了已有结果所存在的弱点,其跟踪学习控制的收敛过程既不依 赖理想运动控制和理想力控制,也不依赖于相应的初始控制数据,大大改善了控制效果.  相似文献   

9.
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.  相似文献   

10.
迭代学习初态问题研究及其在机器人中的应用   总被引:4,自引:0,他引:4  
在迭代学习控制研究中,通常的一个假设是:系统每次迭代初态与理想初态相等。这个假设对于系统的稳定性分析是非常重要的,因为迭代初态扰动将直接影响到迭代学习控制的跟踪精度。针对此问题,本文提出了一种新的迭代学习控制方法:利用遗忘因子控制初态偏移的影响,在保证系统迭代收敛的前提下,同时对初态进行学习,使其最终趋于理想初态,从而实现非线性系统对期望轨线的严格跟踪。最后,本文所提出方法在机器人模型中的仿真应用表明了本文方法的有效性。  相似文献   

11.
《Advanced Robotics》2013,27(13-14):1817-1838
We propose a path-tracking algorithm that is developed using an iterative learning control (ILC) technique and use the algorithm to control an omni-directional mobile robot. The proposed algorithm can be categorized as an open–closed PD-type ILC; it generates robot velocity commands by a PD-type ILC update rule using both previous and current information. When applied to the omni-directional mobile robot, it can decrease position errors and track the desired trajectory. Under the general problem setting that includes a mobile robot, we show that the proposed algorithm guarantees that the system states, outputs and control inputs converge to within small error bounds around the desired ones even under state disturbances, measurement noises and initial state errors. By using simulation and experimental tests, we demonstrate that the proposed algorithm converges fast to the desired path, and results in small root-mean-square (r.m.s.) position error under various surface conditions. The proposed algorithm shows better path-tracking performance than the conventional PID algorithm and achieves faster convergence and lower r.m.s. error than the existing two ILC algorithms.  相似文献   

12.
Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.  相似文献   

13.
A correlation between a learning and a fuzzy entropy, using the control of robotic part macro-assembly (part-bringing) task as an example, is introduced. Two intelligent part-bringing algorithms, to bring a part from an initial position to an assembly hole or a receptacle (target or destination) for a purpose of a part mating in a partially unknown environment containing obstacles, related to a robotic part assembly task are introduced. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part-bringing task. The degree of uncertainty associated with the part-bringing task is used as an optimality criterion, e.g. minimum entropy, for a specific task execution. Fuzzy set theory, well-suited to the management of uncertainty, is used to address the uncertainty associated with the macro-assembly procedure. In the first algorithm, a macro-assembly, locating various shaped assembly holes (targets) in the workspace corresponding to the shapes of the parts and then bringing the part to the corresponding target, despite existing obstacles is introduced. This is accomplished by combining a neural network control strategy coordinating with a mobile rectilinear grid composed of optical sensors as well as fuzzy optimal controls. Depending on topological relationships among the part's present position, the position of obstacles, and the target position in the workspace, a specific rulebase from a family of distinct fuzzy rulebases for avoiding obstacles is activated. The higher the probability, the input pattern (or value) of the neural network to be identified as the desired output is, the lower the fuzzy entropy is. Through the fuzzy entropy, a degree of identification between the input pattern and the desired output of the neural network can be measured. In the second algorithm, a macro-assembly with a learning algorithm and a sensor fusion for bringing the part to the target is introduced. By employing a learning approach, the uncertainty associated with the part-bringing task is reduced. The higher the probability of success is, the lower the fuzzy entropy is. The results show clearly the correlation between a probability of success related to the task execution of the part-bringing and the fuzzy entropy, and also show the effectiveness of above methodologies. The proposed technique is not only a useful tool to measure the behaviour of the learning but applicable to a wide range of robotic tasks including motion planning, and pick and place operations with various shaped parts and targets.  相似文献   

14.
具有滞后的饱和非线性工业控制系统的迭代学习控制   总被引:8,自引:1,他引:7  
基于稳态优化中递阶控制结构,对具有滞后的非平滑饱和非线性工业控制系统施行迭 代学习控制,提出了期望目标轨线δ-可达以及迭代学习算法的ε-收敛的慨念,给出了加权超前 PD-型开环迭代学习算法,对算法的收敛性进行论证.数字仿真证明了算法的有效性,并表明对 工业控制系统的动态品质有显著改进.  相似文献   

15.
In this paper, a modified learning algorithm for the multilayer neural network with the multi-valued neurons (MLMVN) is presented. The MLMVN, which is a member of complex-valued neural networks family, has already demonstrated a number of important advantages over other techniques. A modified learning algorithm for this network is based on the introduction of an acceleration step, performing by means of the complex QR decomposition and on the new approach to calculation of the output neurons errors: they are calculated as the differences between the corresponding desired outputs and actual values of the weighted sums. These modifications significantly improve the existing derivative-free backpropagation learning algorithm for the MLMVN in terms of learning speed. A modified learning algorithm requires two orders of magnitude lower number of training epochs and less time for its convergence when compared with the existing learning algorithm. Good performance is confirmed not only by the much quicker convergence of the learning algorithm, but also by the compatible or even higher classification/prediction accuracy, which is obtained by testing over some benchmarks (Mackey–Glass and Jenkins–Box time series) and over some satellite spectral data examined in a comparison test.  相似文献   

16.
非线性系统的学习控制及其在机器人中的应用   总被引:7,自引:0,他引:7  
分析了一类非线性系统的迭代学习控制问题,给出了迭代算法收敛的充分条件,使得经过逐次迭代后系统输出严格跟踪理想信号,且迭代初始状态与理想状态及理想输入无关。最后,机器人系统的仿真结果表明了本文方法的可行性及实用性。  相似文献   

17.
A performance oriented multi-loop approach to the adaptive robust tracking control of one-degree-of-freedom mechanical systems with input saturation, state constraints, parametric uncertainties and input disturbances is presented. The control system contains three loops. In the outer loop, constrained optimization algorithms are developed to generate a replanned trajectory on-line at a low sampling rate so that the converging speed of the overall system response to the desired target is maximized while not causing input saturation and the violation of state constraints. In the inner loop, a constrained adaptive robust control (ARC) law is synthesized and implemented at high sampling rate to achieve the required robust tracking performances with respect to the replanned trajectory even with various types of uncertainties and input saturation. In the middle loop, a set-membership identification (SMI) algorithm is implemented to obtain a tighter estimate of the upper bound of the inertia so that more aggressive replanned trajectory could be used to further improve the overall system response speed. Interaction of the three loops is explicitly characterized by a set of inequalities that the design variables of each loop have to satisfy. It is theoretically shown that the resulting closed-loop system can track feasible desired trajectories with a guaranteed converging time and steady-state tracking accuracy without violating the state constraints. Experiments have been carried out on a linear motor driven industrial positioning system to compare the proposed multi-loop constrained ARC algorithm with some of the traditional control algorithms. Comparative experimental results obtained confirm the superior performance of the proposed algorithm over existing ones.  相似文献   

18.
An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs’ centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system.The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.  相似文献   

19.
初始误差修正的多智能体一致性迭代学习控制   总被引:2,自引:0,他引:2  
研究了重复运行的分布式多智能体系统在有限时间内的一致性问题。针对具有固定拓扑结构的多智能体系统,在期望轨迹对应的初始状态未知,且系统存在干扰的情况下,引入虚拟领导者技术,提出了一种同时对各智能体的输入和初始状态误差进行迭代修正的分布式学习控制算法。收敛性分析表明,该算法能够消除由于各智能体初始状态和期望轨迹对应的初始状态不同而引起的各智能体输出不能完全跟踪期望轨迹的状况,实现系统在有限时间内的完全跟踪;仿真结果也证明了算法的有效性。  相似文献   

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