显式Clarke-Gawthrop自校正控制器

基于原始CARMA模型的在线辨识和显式多步自校正递推预报器,本文提出了显式Clarke—Gawthrop自校正控制器,仿真例子说明了所提出的结果的有效性。     

An implicit self-tuning technique for processes with variable time-delay

Most implicit self-tuning schemes are based on least-squares k-step-ahead prediction over the system time-delay. On systems where the time-delay is variable the use of such self-tuning algorithms presents problems. For the case when there is a significant variation in the time-delay, it has been thought necessary either to determine the actual time-delay explicitly or to use explicit self-tuning schemes. Neither is in fact necessary, and it is shown in this paper how an implicit scheme may be used for such systems. In the proposed scheme, knowledge of the system time-delay is not required. A simulation example and a practical application are included which demonstrate the capability of the technique when dealing with systems having variable time delay.     

非线性系统鲁棒自校正控制间接算法

提出一种非线性系统鲁棒自校正控制间接算法．借助于神经网的作用，有效地辨识系统的建模误差，其辨识结果在控制算法中加以补偿，于是，使基于低阶线性模型的自校正控制算法有效地应用于复杂的非线性系统．文中给出了算法的鲁棒性分析和仿真结果．     

Implicit and explicit LQG self-tuning controllers

The design of optimal controllers for use in self-tuning systems is considered. Linear Quadratic Gaussian (LQG) controllers are widely used elsewhere but have not until recently been applied in self-tuning systems, except in minimum output variance forms. The LQG controllers offer a guarantee of stability (when the plant parameters are known) which is particularly useful for nonminimum phase systems. The explicit LQG self-tuning controllers introduced in the following are relatively simple to implement. The first version of an implicit LQG self-tuning controller is also introduced. The magnitude of the transport delay terms need not be known a priori and integral action may be introduced easily. If required the desired closed-loop poles of the system can be prespecified.     

一种增益可调的极点配置自校正调节器的显式和隐式算法

本文提出一种新颖的极点配置自校正调节器算法。在配置闭环极点的同时,对调节器的高频增益加以限制,以减弱控制信号的高频振荡。文中给出了显式和隐式算法。算法简单、直观、易于实现。计算机仿真结果表明,采用本文提出的算法,控制信号的方差明显降低。     

Robust self-tuning control based on discrete-time sliding mode for auto-regressive mathematical model in the presence of unmodelled dynamics

In this paper, we propose a new robust self-tuning control, called the generalized minimum variance αl-equivalent selftuning control (GMVSTC-αl) for the linear timevarying (LTV) systems, which can be described by the discrete-time auto-regressive exogenous (ARX) mathematical model in the presence of unmodelled dynamics. The estimation of the parameters contained in this mathematical model is made on the basis of the proposed modified recursive least squares (m-RLS) parametric estimation algorithm with dead zone and forgetting factor. The stability analysis of the proposed parametric estimation algorithm m-RLS is treated on the basis of a Lyapunov function. A numerical simulation example is used to prove the performances and the effectiveness of the explicit scheme of the proposed robust self-tuning control GMVSTC-αl.     

Multivariate weighted minimum variance self-tuning controllers†

The weighted minimum variance (WMV) controller is employed in a self-tuning control system for plants which can be multivariate and non-square. Both explicit and implicit self-tuning strategies are discussed. Existing strategies, based upon k-steps ahead cost functions, have stability problems on systems whose open-loop dynamics are both unstable and non-minimum phase. The WMV controller has the advantage that when plant parameters are known and the control weighting is zero, the optimal system is guaranteed to be stable.     

显式自校正增量型模型算法控制器的内模结构及其全局收敛性

本文提出一种基于增量式脉冲响应模型的显式自校正内模预测控制算法,分析了其闭环特性及鲁棒性,证明了在一定条件下,显式自校正算法是全局收敛的,仿真研究说明算法是可行的。     

一种针对组播的分布式自调节显式速率控制器

拥塞控制是组播服务需要解决的重要问题.当存在大量异质的、传播时延较高的组播接收者时,到达源端的反馈在一定程度上已经过时,因此常常严重影响控制效果.提出了一种分布式、自调节的显式速率比例控制方案,它在源端和中间节点实施控制算法调整各自的发送速率,并且比例控制系数会根据路由器缓冲区占有量偏离理想值的程度自动调节.这种网络参与控制的主动行为比传统路由器尽力服务的工作方式对网络状态的响应更加迅速,自调节的控制系数比固定的控制系数更能及时调节发送速率.仿真实验结果表明,算法具有响应快,链路利用率高和路由器缓冲区占有量比较稳定的特性.     

Generalized predictive control—Part I. The basic algorithm

Current self-tuning algorithms lack robustness to prior choices of either dead-time or model order. A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement. This receding-horizon method depends on predicting the plant's output over several steps based on assumptions about future control actions. One assumption—that there is a “control horizon” beyond which all control increments become zero—is shown to be beneficial both in terms of robustness and for providing simplified calculations. Choosing particular values of the output and control horizons produces as subsets of the method various useful algorithms such as GMV, EPSAC, Peterka's predictive controller (1984, Automatica, 20, 39–50) and Ydstie's extended-horizon design (1984, IFAC 9th World Congress, Budapest, Hungary). Hence GPC can be used either to control a “simple” plant (e.g. open-loop stable) with little prior knowledge or a more complex plant such as nonminimum-phase, open-loop unstable and having variable dead-time. In particular GPC seems to be unaffected (unlike pole-placement strategies) if the plant model is overparameterized. Furthermore, as offsets are eliminated by the consequence of assuming a CARIMA plant model, GPC is a contender for general self-tuning applications. This is verified by a comparative simulation study.     

基于专家知识的分层启发式自校正动态矩阵控制

本文提出一种基于专家知识的分层启发式自校正动态矩阵控制算法,其上层基于实时输出偏差和内模误差确定校正的策略,下层则根据模型失配的情况,采用带有模型辨识自校正或启发式自校正的动态矩阵控制,这种算法集人的调查经验和传统的辨识校正法于一体,改善了闭环系统在模型失配情况下的动态响应。     

Generalized pole-placement self-tuning controller Part 2, Application to robot manipulator control

A practical application of self-tuning generalized pole placement (GPP) is discussed. The application, which involves the control of a five-axis, electrically actuated robot manipulator, is presented for two reasons. First, it illustrates the performance of a novel neo-classical multi-step predictive self-tuner in an important area of applied research—namely, robot control. Second, since the manipulator in question is electrically driven through a harmonic gearbox, the investigation has a general relevance to the area of self-tuning electromechanical servomechanisms. Two forms of GPP algorithms are compared, one based upon a controlled autoregressive integrated moving average model and the other upon a controlled autoregressive moving average model. The relative merits are discussed in the context of (i) single-input single-output and multiloop robot joint control, (ii) programmed setpoint control, and (iii) the use of the performance tuning aids with which the GPP algorithm is equipped.     

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

In this work a practical study evaluates two parametric modelling approaches — linear and non-linear (neural) — for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and different types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.     

Predictor-based self-tuning control

Linear finite-memory output predictors updated in real-time appear to be a suitable internal representation of the system in a digital self-tuning controller. A new time-domain method of quadratic-optimum control synthesis for systems described by such predictors is presented. The synthesis covers both the servo problem and the regulation problem, including the program control (with preprogrammed command signal) and the feedforward from measurable external disturbances. Unlike the standard Riccati equation, the method leads to algorithms (or explicit formulae in low-order cases) which are numerically robust and therefore suitable for real-time computation using microprocessors with reduced wordlength.     

Fuzzy-PID控制起动机性能测试系统的研究

针对起动机性能测试系统,设计由标准ＰＩＤ控制器和模糊自整定机构组成的模糊自整定ＰＩＤ控制器和其模糊自整定机构通过调整参数ａ来在线整定ＰＩＤ参数以求控制的快速必和鲁棒性,实际运行结果表明起动机性能测试系统检测精度高,快速性好,可靠同,已稳定运行于复杂环境的电机生产线上。     

Convergence of self-tuning Riccati equation with correlated noises

For the linear discrete time-invariant stochastic system with correlated noises,and with unknown model parameters and noise statistics,substituting the online consistent estimators of the model paramet...     

Theory and applications of self-tuning regulators

This paper reviews work on self-tuning regulators. The regulator algorithms, their theory and industrial applications are reviewed. The paper is expository—the major ideas are covered but detailed analysis is given elsewhere.     

单输出系统最优自校正滤波新方法及其在跟踪系统中的应用

对于带未知噪声统计且含未知模型参数的单输出系统,本文用现代时间序列分析方法提出了一种新的自校正滤波方法,给出了具有渐近最优性的自校正滤波器,新方法的特点是基于ARMA新息模型通过计算自校正输出预报器和自校正观测噪声滤波器就可得到自校正状态滤波器,文中给出了在跟踪系统中的应用例子,仿真结果说明了新方法的有效性。     

A self-tuning regulator based on optimal output feedback theory

Self-tuning control of stochastic systems is considered. The underlying control problem is a parametric linear quadratic problem for fixed structure controllers. An explicit self-tuning regulator is described based on optimal output feedback theory. The proposed self-tuner is a generalization of state-space LQG self-tuners to parametric LQ problems. Two interesting application areas of parametric LQ self-tuners are autotuning of parameters of low-order regulators, such as PID-regulators, and adaptive decentralized control.