Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.     

Adaptive fuzzy learning control for a class of nonlinear dynamic systems

This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed‐loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc.     

基于未知控制增益的非线性系统自适应迭代反馈控制

针对一类单输入单输出不确定非线性重复跟踪系统, 提出一种基于完全未知控制增益的自适应迭代反馈控制. 与普通迭代学习控制需要学习增益稳定性前提条件不同, 所提自适应迭代反馈控制律通过不断修改Nuss baum形式的反馈增益达到收敛. 证明当迭代次数i→δ时, 重复跟踪误差可一致收敛到任意小界δ. 仿真显示了所提控制方法的有效性.     

Turbo-均衡及迭代均衡的结构模型

给出了Turbo-均衡、硬迭代判决反馈均衡器、软迭代判决反馈均衡器和分步迭代判决反馈均衡器的结构模型,并对这些模型进行了分析和对比。     

基于模糊推理的均热炉温度迭代学习控制研究

根据均热炉装钢的初值条件，基于模糊推理基本理论，获得温度控制的前馈量，构成知识库，利用迭代学习算法，修正前次迭代输入的前馈量。在烧钢加热过程中，采用前馈、反馈与迭代学习算法相结合的控制方案。     

模糊不确定时滞系统的静态输出的反馈镇定:迭代线性矩阵不等式方法

对一类不确定非线性时滞系统利用模糊T＿S模型进行建模,研究了静态输出反馈镇定问题.用矩阵不等式的形式给出了模糊T＿S不确定时滞系统可通过静态输出反馈镇定的充分条件.并将矩阵不等式的条件转化为迭代线性矩阵不等式(ILMI),并给出相应的算法.     

基于深度强化学习与迭代贪婪的流水车间调度优化

流水车间调度是应用背景最为广泛的调度问题,其智能算法研究具有重要的学术意义和应用价值.以最小化最大完工时间为目标,提出求解流水车间调度的一种基于深度强化学习与迭代贪婪算法的框架.首先,设计一种新的编码网络对问题进行建模,解决了传统模型受问题规模影响而难以扩展的缺陷,并利用强化学习训练模型以获取优良输出结果;然后,提出一种带反馈机制的迭代贪婪算法,以网络的输出结果为初始解,协同利用多种局部操作提高搜索能力,并根据性能反馈调节各操作的使用,进而获得最终的调度解.仿真结果和统计对比表明,所提出的深度强化学习与迭代贪婪融合的算法能够取得更好的性能.     

Robust iterative learning controller design using the performance weighting function of feedback control systems

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the ?₂-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the ?₂-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.     

Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints

In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme.     

A reinforced learning control using iterative error compensation for uncertain dynamical systems

This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of a high speed, computer-controlled machining process. It is specially useful in mass-produced parts produced by a high-speed machine tool system. This method uses an iterative learning technique which adopts machine commands and cutting errors experienced from previous manoeuvres as references for compensation actions in the current manoeuvre. Non-repetitive disturbances and nonlinear dynamics of the cutting processes and servo systems of the machine which greatly affect the convergence of the learning control systems were studied in this research. State feedback and output feedback methods were used for controller design. Stability and performance of learning control systems designed via the proposed method were verified by simulations on a single degree of freedom servo positioning system.     

基于适应策略的迭代学习调度

适应调度是一种基于状态/性能反馈的定性控制方法．适应调度知识完成从状态到 调度规则的映射，与制造系统配置、生产任务构成、调度目标函数密切相关，并随调度问题 的改变而改变，具有较强的针对性．采用迭代学习方法，根据具体调度问题自动修正适应调 度知识，提高系统运行性能；另一方面，由于引入了调度目标函数，使生产管理者最关心的 性能得到优化，最大限度满足生产管理者的生产目标．仿真实验结果验证了这一方法的有效 性．     

一类非线性相似组合大系统的迭代学习控制

利用状态反馈部分线性化技术研究了一类非线性相似组合大系统的迭代学习控制问题。与现有结果不同的是，它不直接研究系统本身，而是构造一个适当的 反馈，然后对闭环系统给出其迭代学习收敛的充分条件，     

基于输出反馈的直升机变结构控制律设计

针对直升机的小扰动模型,设计了静态输出反馈滑模控制器（SOFSMC）。利用参考模型,实现了直升机的相关动态指标。针对滑模面的设计问题,将其等价地转化为一组双线性矩阵不等式（BLMI）,并采用迭代线性矩阵不等式（ILMI）技术来求解。针对控制律的综合问题,给出了一种基于单位向量法的控制律,并通过引入线性反馈来保证系统进入滑动模态后线性控制部分与标称系统的线性控制部分的一致性。仿真结果验证了该方法的有效性。     

Iterative inversion of neural networks and its application toadaptive control

An iterative constrained inversion technique is used to find the control inputs to the plant. That is, rather than training a controller network and placing this network directly in the feedback or feedforward paths, the forward model of the plant is learned, and iterative inversion is performed on line to generate control commands. The control approach allows the controllers to respond online to changes in the plant dynamics. This approach also attempts to avoid the difficulty of analysis introduced by most current neural network controllers, which place the highly nonlinear neural network directly in the feedback path. A neural network-based model reference adaptive controller is also proposed for systems having significant dynamics between the control inputs and the observed (or desired) outputs and is demonstrated on a simple linear control system. These results are interpreted in terms of the need for a dither signal for on-line identification of dynamic systems.     

基于策略迭代算法的连续时间线性Markov跳变系统非零和微分反馈Nash控制

针对一类连续时间线性Markov跳变系统,本文提出了一种新的策略迭代算法用于求解系统的非零和微分反馈Nash控制问题.通过求解耦合的数值迭代解,以获得具有线性动力学特性和无限时域二次成本的双层非零和微分策略的Nash均衡解.在每一个策略层,采用策略迭代算法来计算与每一组给定的反馈控制策略相关联的最小无限时域值函数.然后,通过子系统分解将Markov跳变系统分解为N个并行的子系统,并将该算法应用于跳变系统.本文提出的策略迭代算法可以很容易求解非零和微分策略所对应的耦合代数Riccati方程,且对高维系统有效.最后通过仿真示例证明了本文设计方法的有效性和可行性.     

Static output feedback controller design for fuzzy systems: An ILMI approach

This paper examines the problem of static output feedback control of a Takagi-Sugeno (TS) fuzzy system. The existence of a static output feedback control law is given in terms of the solvability of bilinear matrix inequalities. An iterative algorithm based on the linear matrix inequality is proposed to compute the static output feedback gain. To reduce the conservatism of the design, the structural information of the membership function of the fuzzy rules is incorporated. Numerical examples are used to illustrate the validity of the methods.     

具有时滞的Lurie型组合系统的分散输出反馈镇定

通过构造一比较系统,将Lurie型组合系统的稳定性问题转化为讨论维数较低的比较系统的稳定性问题,并利用M矩阵特性导出比较系统稳定的一个充分条件;为求取输出反馈增益,建立等价的稳定条件的QLMI表示形式.这一方法的特点是使大系统的稳定控制器设计的复杂度保持在子系统一级的水平上,给出的实例说明算法在实际工程应用中是有效的.     

A computational method for simultaneous LQ optimal control designvia piecewise constant output feedback

The paper is concerned with simultaneous linear-quadratic (LQ) optimal control design for a set of LTI systems via piecewise constant output feedback. First, the discrete-time simultaneous LQ optimal control design problem is reduced to solving a set of coupled matrix inequalities and an iterative LMI algorithm is presented to compute the feedback gain. Then, simultaneous stabilization and simultaneous LQ optimal control design of a set of LTI continuous-time systems are considered via periodic piecewise constant feedback gain. It is shown that the design of a periodic piecewise constant feedback gain simultaneously minimizing a set of given continuous-time performance indexes can be reduced to that of a constant feedback gain minimizing a set of equivalent discrete-time performance indexes. Explicit formulas for computing the equivalent discrete-time systems and performance indexes are derived. Examples are used to demonstrate the effectiveness of the proposed method.     

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.