Identification based adaptive iterative learning controller

In recent years, more research in the control field has been in the area of self‐learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self‐learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired trajectory within a specified error limit. Conventional ILC algorithms have the problem of relatively slow convergence rate and adaptability. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome the aforementioned problems. The ensuing design procedure is explained and results are accrued from a number of simulation examples. A key point in the proposed scheme is the computation of gain matrices using the steepest descent approach. It has been found that the learning rule can be guaranteed to converge if certain conditions are satisfied. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

高阶无模型自适应迭代学习控制

针对一类非线性非仿射离散时间系统,提出了高阶无模型自适应迭代学习控制方案.控制器的设计和分析仅依赖于系统的输入/输出(I/O)数据,不需要已知任何其他知识.该方法采用了高阶学习律,可利用更多以前重复过程中的控制信息提高系统收敛性,且学习增益可通过"拟伪偏导数"更新律迭代调节.仿真结果验证了所提出算法的有效性.     

A new recursive identification algorithm for singularity free adaptive control

Reportedly, guaranteeing the controllability of the estimated system is a crucial problem in adaptive control. In this paper, we introduce a least-squares-based identification algorithm for stochastic SISO systems, which secures the uniform controllability of the estimated system and presents closed-loop identification properties similar to those of the least-squares algorithm. The proposed algorithm is recursive and, therefore, easily implementable. Its use, however, is confined to cases in which the parameter uncertainly is highly structured.     

Subspace identification for continuous-time stochastic systems via distribution-based approach

This paper presents a novel approach for system identification of continuous-time stochastic state space models from random input-output continuous data. The approach is based on the introduction of random distribution theory in describing (higher) time derivatives of stochastic processes, and the input-output algebraic relationship is derived which is treated in the time-domain. The efficacy of the approach is examined by comparing with other approaches employing the filters.     

Continuous time stochastic adaptive control: non-explosion, ε-consistency and stability

For completely observed continuous time constant parameter stochastic linear systems, an indirect adaptive control law is presented which, subject principally to a weak location hypothesis concerning the true parameter, and a persistent excitation hypothesis, generates ε-consistent recursive least squares parameter estimates and ensures the system is mean square sample path stable. The adaptive control algorith mentails (i) recursively calculating the least squares estimate of the system parameter, and (ii) recursively generating the LQR feedback gain matrix lying in a set of matrix gains γ known to contain a stabilizing gain. The a.s. non-explosion of the system is a direct consequence of this construction.     

非参数模型控制在液位控制系统中的应用研究

针对工业控制过程中液位系统的时变和明显的滞后特征,研究了非参数模型控制方法在液位控制系统中的设计方案,讨论了控制算法中引入的伪偏导数的在线估计问题,实现了通过液位系统的输入输出信息并利用递归最小二乘法对伪偏导数进行在线估计的过程,仿真实验验证了非参数模型算法对液位控制的鲁棒性、快速性及抗干扰性,通过仿真比较,展示了该算法性能优于PID算法和模糊控制的结果.     

On convergence proofs in system identification – a general principle using ideas from learning theory

It is shown that learning theory offers convergence analysis tools that are useful in system identification problems. They allow analysis in a parameter-free context, which elevates the analysis from parameter sets to model sets and from parameter identification to model identification. When a parameterization is eventually introduced, this leads to alternative assumptions on the parameterization and parameter set. Moreover, structural identification can be analyzed within the same framework. Another advantage is that the proofs are technically and conceptually simple.     

An introduction to some statistical aspects of PAC learning theory

In this paper, a brief introduction is given to some statistical aspects of PAC (probably approximately correct) learning theory. It is shown that there is a close connection between the principal results in PAC learning theory and those in empirical process theory, the latter being a well-established branch of probability theory. The main results in each area are summarized without proofs, and the reader is directed to appropriate sources in the literature.     

一种基于增强学习的自适应控制方法

针对模型未知时变非线性对象的控制问题，提出一种直接的自适应控制策略。该策略基于径向基神经网络并结合增强学习的自调节能力，无需知道控制对象的动态特性，而是通过在线试错在控制过程中不断积累与问题相关的信息，用以产生可接受的逐步优化的解。     

The zero divisor problem of multivariable stochastic adaptive control

Stochastic adaptive minimum variance control algorithms require a division by a function of a recursively computed parameter estimate at each instant of time. In order that the analysis of these algorithms is valid, zero divisions must be events of probability zero. This property is established for the stochastic gradient adaptive control algorithm under the condition that the initial state of the system and all finite segments of its random disturbance process have a joint distribution which is absolutely continuous with respect to Lebesgue measure. This result is deduced from the following general result established in this paper: a non-constant rational function of a finite set of random variables {x₁},x_n} is absolutely continuous with respect to Lebesgue measure if the joint distribution function of {x₁,…,x_n} has this property.     

一种新的复杂动态网络学习控制的自适应同步算法

提出了一种新的具有未知时变参数复杂动态网络同步的自适应学习控制方法.运用重新参数化技术,设计周期时变参数的自适应学习律、常参数的更新律以及自适应控制策略确保同步误差渐近收敛.通过构造复合能量函数给出同步的一个充分条件.最后给出一个复杂网络的例子验证所提方法的有效性.     

A novel approach to output feedback control of fuzzy stochastic systems

This paper investigates the problem of Hankel-norm output feedback controller design for a class of T–S fuzzy stochastic systems. The full-order output feedback controller design technique with the Hankel-norm performance is proposed by the fuzzy-basis-dependent Lyapunov function approach and the conversion on the Hankel-norm controller parameters. Sufficient conditions are established to design the controllers such that the resulting closed-loop system is stochastically stable and satisfies a prescribed performance. The desired output feedback controller can be obtained by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, a Henon map system is used to illustrate the effectiveness of the proposed techniques.     

Tracking and parameter identification for model reference adaptive control

We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.     

Process control of pH neutralization based on adaptive algorithm of universal learning network

This paper presents an adaptive algorithm of universal learning network (ULN) and its application to identify pure time delay of a plant model. Universal learning network can be used in model predictive control for stabilizing a class of nonlinear systems with long time delay. Depending on ULN model with single neuron controller, the control architectures are introduced and applied to pH neutralization process. Simulation results prove the applicability and effectiveness of the ULN model. The general architecture and adaptive learning algorithm give ULN more representing abilities to model and control the nonlinear black box systems with long time delay.     

基于强化学习的模型参考自适应控制

提出了一种基于强化学习的模型参考自适应控制方法,控制器采用自适应启发评价算法,它由两部分组成:自适应评价单元及联想搜索单元.由参考模型给出系统的性能指标,利用系统反馈的强化信号在线更新控制器的参数.仿真结果表明:基于强化学习的模型参考自适应控制方法可以实现对一类复杂的非线性系统的稳定控制和鲁棒控制,该控制方法不仅响应速度快,而且具有较高的学习速率,实时性较强.     

一种基于在线辨识的Web 服务自适应接纳控制

设计并实现了一种基于量子行为粒子群算法(QPSO)系统模型在线辨识的Web服务自适应接纳控制,根据系统模型的变化在线调节比例积分控制器参数.通过接纳时间比反馈控制机制,调整控制周期内服务器接纳请求的时间长度,进而实现接纳控制.通过仿真实验,并与多种不同控制方法进行比较,所得结果表明,在线辨识自适应控制能够在服务器过载的情况下更有效地控制系统资源,进一步提高了服务质量.     

Decentralised adaptive control for a class of stochastic switched interconnected nonlinear systems

In this paper, we focus on the problem of adaptive stabilisation for a class of interconnected uncertain switched stochastic nonlinear systems. Classical adaptive and backstepping technique are employed for control synthesis. Instead of estimating the switching parameters directly, we design the adaptive controller based on the estimations of bounds on switching time-varying parameters. A smooth function is introduced to deal with the difficulties caused by unknown interactions and tuning function approach is used to circumvent the overparameter problem. It is shown that under the action of the proposed controller all the signals of the overall closed-loop systems are globally uniformly bounded in probability under arbitrary switching. Simulation results are presented to illustrate the effectiveness of the proposed approach.     

基于模型辨识的非线性连续系统的迭代学习控制

基于非线性连续动态的模型辨识算法, 给出了非线性连续系统的一种非常有效的迭代学习控制方案. 该控制方案不要求非线性连续系统中具体的非线性关系, 并且容许系统初始误差的存在.     

Randomized algorithms for the synthesis of cautious adaptive controllers

We introduce a new methodology for the design of cautious adaptive controllers based on the following two-step procedure: (i) a probability measure describing the likelihood of different models is updated on-line based on observations, and (ii) a controller with certain robust control specifications is tuned to the updated probability by means of randomized algorithms. The robust control specifications are assigned as average specifications with respect to the estimated probability measure, and randomized algorithms are used to make the controller tuning computationally tractable.This paper provides a general overview of the proposed new methodology. Still, many issues remain open and represent interesting topics for future research.