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.     

基于系统辨识的模型参考自适应控制

介绍了荷兰Twente大学控制工程系研发的先进控制实验平台的基本组成、功能和基于机电一体化设计软件20-sim。以先进控制演示仪为研究对象,在20-sim环境下建立了系统的图标化数学模型,并从理论上对模型作了降阶分析。根据系统辨识的模型参考自适应控制（MRAC）原理,编制了模型参考自适应和Lyapunov求解模块。采用线性二次型调节器为控制模块,构成了完整的先进控制演示仪的MRAC控制系统。该系统仿真结果显示所建立的模型针对性强,可用于各种先进控制策略的仿真研究。     

Supervision of nonlinear adaptive controllers based on fuzzy models

A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing unmodelled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi–Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation, a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of a heat exchanger.     

基于LabVIEW的加速度计模型参数辨识模块设计

加速度计的模型参数辨识对研究加速度计动态特性具有重要作用.针对加速度计动态特性测试系统中加速度计模型参数辨识模块的设计,利用LabVIEW图形化编程的特点,以最小二乘原理为基础,通过对加速度计频域响应函数的拟合实现了加速度计模型参数的辨识,并构建了可视化软面板.实验测试表明:所设计模块能够实现加速度计模型参数辨识,有较高的参数辨识精度,并具有良好的人机交互,所辨识出的模型能够描述加速度计的动态特性.     

Tuning of PID controllers based on simplified single parameter optimisation

A simple method to design PID controllers in the frequency domain based on a simplified constrained optimisation is proposed. The method is based on the use of a single tuning parameter, defined as the quotient between the final crossover frequency and the zero of the controller. The tuning procedure is based on the maximisation of the controller gain subject to an equality constraint in the phase margin and an inequality constraint in the gain margin. The main advantage of the proposed method is that, even though the maximisation of the controller gain is straightforward, since there is only one parameter to be tuned, the solution is close to the optimal tuning obtained with direct numerical optimisation methods. Moreover the method is applicable to any linear model structure, including dead time and non-minimum phase systems.     

遗传算法在模糊模型参数辨识中的应用

介绍了T-S模糊模型的建模过程,在现有T-S模糊模型参数辨识方法的基础上,提出了一种先应用最小二乘法对结论参数进行粗略辨识,以确定参数的大致范围之后,再应用遗传算法对前提参数和结论参数同时优化的参数辨识方法,通过MATLAB对本算法进行了仿真,并对非线性函数进行了逼近实验,所取得的结果令人满意。     

Parameter convergence of a new class of adaptive controllers

A new class of adaptive control schemes for minimum-phase linear time invariant (LTI) systems has recently been developed using nonlinear design techniques which guarantee improved transient performance in addition to closed-loop stability and asymptotic tracking. In this paper we establish the parameter convergence properties of this new class of schemes in the presence of persistently exciting signals and compare them with the properties of the traditional adaptive controllers. We show that the new class of adaptive controllers has stronger parameter convergence properties in the presence of overparameterization     

Robust direct adaptive controllers with a new normalizationtechnique

This note presents a robust direct adaptive control scheme with a new normalization technique and its stability analysis in the presence of unmodeled dynamics and/or bounded disturbances. Following input-output stability theory, L₂ and L_∞ stability of the system is studied using the sector stability and passivity theorems and conditions for global stability are derived. It is also shown that robustness of the adaptive control system can be improved when relative dead zone and the novel normalization technique are incorporated into the scheme     

基于自适应微调因子的改进frost滤波

为避免frost滤波因使用同一微调因子带来边缘细节等结构信息的模糊,以及等距等权所带来的盲目平滑现象,对frost滤波算法提出了一种改进方案。该方案综合考虑滤波窗口本身的局域统计特性以及窗口内各像素本身的统计特性来自适应确定微调因子。通过对真实合成孔径雷达图像进行改进算法降噪实验,以等效视数和边缘保持指数两项指标为评价标准,并与不同微调因子的Frost滤波输出结果进行比较,结果表明改进算法比原算法有很大的改进,在边缘保持和去噪方面具有更好的滤波性能。     

一种基于鲁棒自适应观测器的故障区分方法

设计了一种鲁棒自适应观测器,通过其输出残差对传感器故障信息敏感性很高,而对系统故障信息敏感性很低的特点,实现了故障区分.设计了一套故障区分控制系统和检测策略,用以区分两类故障,以免发生误判现象.举例说明了该策略的可行性和有效性.     

基于同步的多旋翼无人机非线性模型自适应参数辨识

针对多旋翼无人机的非线性模型,提出了一种基于同步理论的自适应参数辨识方法.其主要思想是通过设计一种"从系统",在实现状态与原有非线性系统同步的同时,设计自适应更新律,使得对参数的估计收敛到真值.所提方法能够适用于以非线性形式存在的参数,其渐近稳定性由李雅普诺夫方法和拉塞尔不变性原理予以证明.仿真实例可以验证,合理选择自适应增益,即便真值发生突变,所提方法也能保证估计值快速收敛到最新的真值.     

A performance comparison of robust adaptive controllers: linear systems

We consider robust adaptive control designs for relative degree one, minimum phase linear systems of known high frequency gain. The designs are based on the dead-zone and projection modifications, and we compare their performance w.r.t. a worst case transient cost functional with a penalty on the ^∞ norm of the output, control and control derivative. We establish two qualitative results. If a bound on the ^∞ norm of the disturbance is known and the known a priori bound on the uncertainty level is sufficiently conservative, then it is shown that a dead-zone controller outperforms a projection controller. The complementary result shows that the projection controller is superior to the dead-zone controller when the a priori information on the disturbance level is sufficiently conservative.     

基于NARX神经网络航空发动机参数动态辨识模型

针对航空发动机参数非线性动态特性,提出一种基于外部输入非线性自回归（NARX）神经网络的发动机参数动态辨识模型。主要思路是根据NARX网络的非线性时序预测特性,结合发动机参数的稳态和动态参数,提出一种基于偏稳态差值预测的NARX参数动态模型结构。设计了SP-P辨识结构,整定了模型内部结构参数并建立N1（低压转子转速）、N2（高压转子转速）、EGT（涡轮后排气温度）参数非线性差分预测模型。最后依据某发动机试车样本,对推杆加减速时N1、N2、EGT动态辨模型进行仿真。仿真结果表明,N2相对误差小于0.2%,N1相对误差小于0.3%,EGT相对误差小于[1℃],满足发动机试车仿真需要。最后,将所建模型应用于某A320机务维修训练器的发动机仿真系统。     

Transient-performance improvement with a new class of adaptive controllers

Computable performance bounds are derived in addition to global stability and asymptotic tracking, a systematic improvement of transient performance can be achieved. The underlying linear nonadaptive controller is shown to possess a parametric robustness property, but for a large parameter uncertainty it requires high gain. A comparison between the adaptive and the nonadaptive performance bounds demonstrates that adaptation improves the overall performance without the undesirable effects of high gain.     

Tuning of multi-loop PI controllers based on gain and phase margin specifications

In this paper, a single-iteration strategy is proposed for the design of a multi-loop PI controller to achieve the desired gain and phase margins for two-input and two-output (TITO) processes. To handle loop interactions, a TITO system is converted into two equivalent single loops with uncertainties drawn from interactions. The maximum uncertainty is estimated for the initial controller design in one loop and single-input and single-output (SISO) controller design is applied. This controller is substituted to other equivalent loop for design, and finally, the first loop controller is refined on knowledge of other loop controller. For SISO controller tuning, a new method is presented to determine the achievable gain and phase margins as well as the relevant controller parameters. Examples are given for illustration and comparison.     

Stochastic adaptive one-step-ahead optimal controllers based oninput matching

Optimal adaptive controller based on the ELS algorithm is established using the input matching technique. The control signal is reduced to a constant weighted sum of the measurable information-state vector components using a one-step-ahead quadratic cost function to govern the behavior of the stochastic linear systems. The control effort can be estimated globally. The algorithm also predicts the convergence rate. With no excitation condition, the closed-loop system is globally stable and the input converges to the one-step-ahead optimal input     

Robustness of model reference adaptive controllers: an input-outputapproach

A general input-output approach is employed to study the direct model reference adaptive control problem in the presence of parametric and unstructured uncertainty. A sufficient condition for the boundedness of the closed-loop signals is developed, relating an appropriately defined size of the unstructured uncertainty to the size of the parametric uncertainty. This condition involves the intuitively appealing gains of various sensitivity operators and allows for the study of the effect of the design parameters on the boundedness properties of the closed-loop system. The results of this work indicate a direction towards a unified and systematic theory in adaptive control     

T-S model based indirect adaptive fuzzy control using online parameter estimation

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation.     

Combining model reference adaptive controllers and stochastic self-tuning regulators

The similarities between model reference adaptive controllers (MRAC) for minimum phase plants where the control objectives are specified by reference models and stochastic self-tuning regulators (S-STURE) for minimum phase plants where the control objectives are specified by autoregressive moving average (ARMA) stochastic models are investigated.These similarities permit the extension, for these two classes of adaptive systems, of the duality existing in the linear case with known parameters between modal control and minimum variance control. The convergence analysis of these schemes in a combined deterministic and stochastic environment allows one finally to define schemes which behave as a desired MRAC in a deterministic environment and as a desired S-STURE in a stochastic environment. The problem of removing the positive real conditions for convergence in deterministic and stochastic environment is also discussed.     

Improved identification and autotuning of PI controllers for MIMO processes by relay techniques

A technique for on-line identification and tuning is proposed to be used in the framework of a MIMO autotuning procedure. The proposed technique does not suffer from the risks of instability and the lack in performance of common tuning techniques in MIMO autotuning. Identification is accomplished through an extension of the well known ATV autotune identification method and requires only few additional tests in order to obtain some more knowledge about the process. The resulting model, which describes with good precision the process in a region of frequencies around the critical point, is then used for tuning: the integral time is found as a function of the model time constants and delay, while the gain is computed in order to give a desired value of the closed-loop resonance peak. Examples of application show that advantages over other proposed techniques can be retained for processes having different dynamic characteristics.