Self-tuning PID/I controller

A self-tuning controller based on the method of frequency identification and intended for controlling plants with time-varying parameters under arbitrary bounded exogenous disturbances was proposed. The impact of the harmonic frequencies of the test signal on the results of identification over a prescribed time was studied. Methods to choose frequencies and an algorithm to adjust the amplitudes of the harmonics of the test signal were proposed. The duration of the identification parameters of the plant model is determined automatically depending on the intensity of the exogenous disturbance and the desired relative accuracy of identification. The results of experimental studies of the proposed controller demonstrating its efficiency were presented.     

自适应有限拍感应电机电流控制器设计

基于有限拍方法设计了一种自适应矢量控制感应电机的电流控制器.通过差分消去电机模型中的反电动势,利用补偿电压进一步消除电流误差.针对电流控制器对电机参数变化敏感的问题,对电机参数进行在线辨识,保证控制器参数与电机的实际参数一致.仿真结果表明,定子电流在参数变化时能够实现对参考电流的快速跟踪,同时能获得良好的稳态性能.     

An improved stable adaptive fuzzy control method

Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it is shown that a major drawback of that concept, the necessity for new adaptation at every change of the reference signal, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural network domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simulation results     

一种新型多变量模糊自适应控制系统的研究

针对多变量非线性系统,提出了一种基于动态耦合特性的两级串联结构的模糊自校 正控制器,并提出了基于动态灵敏度矩阵和在线测量的自学习算法.同时利用一种智能梯度 法确定自校正学习迭代步长.仿真结果表明,算法的收敛性和系统的稳定性均有所改善.利用 这些控制策略,可以较好地解决化工反应器等复杂对象的过程控制问题.     

Online fuzzy identification for an intelligent controller based on a simple platform

The paper presents the identification issues of the self-tuning nonlinear controller ASPECT (Advanced control algorithmS for ProgrammablE logiC conTrollers). The controller is implemented on a simple PLC platform with an extra mathematical coprocessor, but is intended for the advanced control of complex processes. The model of the controlled plant is obtained by means of experimental modelling. A special batch-wise algorithm that is based on the Takagi–Sugeno model and uses “fuzzy instrumental variables” technique is described in the paper. Many robustness problems of the classical adaptive approaches can be circumvented to some extent by the proposed batch-wise approach combined with a supervisory mechanism. The paper also includes some experimental results on the hydraulic pilot plant and some simulation case studies.     

Self-tuning regulators for a class of multivariable systems

Control of a class of multivariable systems described by linear vector difference equations with constant but unknown parameters is discussed. A multivariable minimum variance strategy is first presented. This gives a generalization of the minimum variance strategy for single-input single-output systems. A multivariable self-tuning regulator based on the minimum variance strategy is then proposed. It uses a recursive least squares estimator and a linear controller obtained directly from the current estimates. The asymptotic properties of the algorithm are discussed. If the estimated parameters converge, the resulting controller will under certain conditions give the minimum variance strategy. The analysis also gives insight into the case when several single-input single-output self-tuning regulators are operating in cascade mode.     

隐式自校正广义预测控制器及其全局收敛性

通过引入等价性能指标,给出了一种基于CARIMA模型、考虑模型误差的隐式自校正广 义预测控制器.并通过构造两个辨识器,使控制器的全部参数能递推估计.文末给出了当采 用改进型最小二乘法和随机逼近法估计参数时的自校正广义预测控制隐式算法的全局收敛 性证明.     

Design weighting parameter tuning of multivariable self-tuning controllers

In this paper, we present the method for on-line tuning of a design weighting polynomial parameters of a multivariable self-tuning controller which adapts to changes in the higher-order nonminimum phase system parameters with time delays and noises. The algorithm effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins–Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed method is simple and effective compared with the pole restriction method. The computer simulation results are presented to adapt the higher-order multivariable system with nonminimum phase and with changeable system parameters.     

基于模糊神经网络的积分自适应控制

将模糊神经网络应用于传统线性积分自适应控制,构造了一类模糊神经自适应方法,用于消除非线性系统响应偏差.模糊神经网构成直接非线性自适应控制器.对线性及非线性对象的仿真控制以及与经典自适应控制的比较,表明了模糊神经自适应控制器的有效性.     

基于Dahlin算法的CMAC控制器设计

针对传统的基于Dahlin算法的控制器在对变时滞系统进行控制时控制效果恶化,甚至发生不稳定现象的弱点,提出了以CMAC神经网络与Dahlin算法相结合的控制方法。以CMAC神经网络作为一个前馈控制器,通过对Dahlin控制器输出的学习,实现时滞系统的自适应稳定控制。仿真实验表明,这种复合控制方法保留了Dahlin算法与CMAC神经网络的各自特长,同时具备学习速度快,适应能力强的优点,具有良好的稳定性控制效果。     

TORA系统基于自调节滑模干扰补偿器的解耦滑模控制

针对欠驱动TORA系统,提出一种基于自调节滑模干扰补偿器的解耦滑模控制方法。所提出的控制方法无需系统不确定性上界的先验信息,对于系统不确定性具有良好的适应性。该控制方法包括设计一种自调节滑模干扰补偿器和一种新型的双幂次趋近律,所设计的自调节滑模干扰补偿器能够利用切换增益自适应算法准确逼近上界未知的系统不确定性,所提出的新型双幂次趋近律能够保证系统状态的快速趋近并抑制控制器的高频抖动。采用Lyapunov稳定性理论证明闭环控制系统的稳定性,并通过数值仿真实验验证所提出的控制方法的有效性。     

Convergence rate for RLS-based direct self-tuning minimum-varianceregulation of ARMAX minimum phase plants

An upper bound for the error convergence rate of recursive least squares (RLS)-based direct self-tuning minimum-variance (DSTMV) regulation of minimum phase multiple time-delay autoregressive moving average with exogenous input (ARMAX) plant is derived. The RLS algorithm used is k-interlaced, with k being the plant time-delay. The bound is derived for known fixed b₀ by extending a previously proposed methodology. The bound provides a joint explanation of DSTMV regulation stability and parameter estimate convergence. The paper demonstrates that self-tuning is based on convergence properties of RLS as well as on the excitation quality of plant white noise, which generates (via controller feedback) the plant input     

Self-tuning controller design for systems with arbitrary time delays Part 2. Algorithms and simulation examples

Recent theoretical developments in the self-tuning control of multivariable systems with arbitrary time delays have been considered in Part 1 of this work (Tade et al. 1988) and a simple time delay extraction procedure has been proposed. In this paper, that procedure is used to derive an adaptive controller based on the generalized minimum variance criterion. The computation count for the proposed self-tuning control algorithm is compared with those for two other algorithms, one proposed by Prager and Wellstead (1981) and the other by Dugard et al. (1984). Assuming that all computation requirements for other stages of the implementation of these algorithms are equal, the results indicate that the proposed algorithm requires less computational effort for updating the control action at each sampling interval. Simulation examples are presented to demonstrate the performance of the proposed algorithm.     

智能控制与复合控制在二阶时变纯滞后对象的应用研究

针对二阶时变纯滞后对象难以控制的问题,提出了采用改进Smith预估器提高系统的稳定性和鲁棒性;采用CMAC和PID并行控制的算法来提高动态性能;以CMAC神经网络作为一个前馈控制器,通过对PID控制器输出的学习,实现二阶变时滞系统的自适应稳定控制。仿真实验表明,这种复合控制方法保留了Smith算法与CMAC神经网络和PID算法的各自特长,具有学习速度快,适应能力强、动态性能好的优点,具有良好的稳定性控制效果。     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.      

Self-tuning leader-follower games

The optimization of a class of linear discrete-time stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own single-stage quadratic objective is considered. Based on a centralized information pattern, a steady-state solution is obtained for the stochastic adaptive leader-follower game problem. It is shown that this class of leader-follower game problems can be transformed to an equivalent class of multiple-input-multiple-output self-tuning control problems with a single decision-maker. The convergence of the multiple-input-multiple-output self-tuning controller is established and applied to the leader-follower game. A numerical simulation illustrates the proposed self-tuning algorithm for an adaptive leader-follower strategy.     

多变量极点配置自校正Smith控制器及其应用

本文根据许多工业被控过程具有大纯滞后的特点,将自校正调节器与Smith预估器相结 合,给出一种多变量极点配置自校正Smith控制算法.该算法结构简单,不仅能对输出进行预 报和无偏跟踪,而且适用于非最小相位系统.该算法已成功地应用于太原钢铁公司轧辊罩式 退火炉微机自适应群控系统,实际运行结果表明控温精度和炉内温度场均匀度均在±3℃以 内,远比常规PID仪表的控温效果好.     

渐消记忆递推辨识实现的木材进锯自校正控制系统

针对木材锯割过程中被控对象参数随木材直径、硬度等因素而变化的特点，本文提出了一种适于木材进锯自动控制应用的辨识及自校正控制算法。该方法利用恒低速进锯阶段辨识估计获得对象参数初值。在此基础上，应用渐消记忆递推算法进行闭环自校正控制，以保证带锯交流电机负载电流在工艺条件允许的最大电流下工作，从而确保木材锯割的质量和产量，提高经济效益，现场应用结果表明，该辨识及自校正控制算法准确、系统设计合理、工作可靠     

基于人工免疫多目标寻优算法的PID自整定

提出了一种基于人工免疫多目标寻优算法(AIMOA)的PID参数自适应整定的设计方法.利用生物免疫系统的免疫机理设计系统响应的目标函数,再通过AIMOA算法搜索PID控制器的优化参数组,最后将基于AIMOA算法同基于遗传算法(GA) 和齐格勒-尼柯尔斯(Zi-Ni)方法的PID自整定进行了仿真比较.结果表明:AIMOA算法具有快速收敛性,能够较快地搜索到PID参数自适应整定的最优或者次最优解,体现了算法的优越性、实用性和有效性.     

LQG自校正控制器

尽管LQG控制具有很多优点,但由于计算量大,LQG 自校正控制仍未能得到成功应用。在本文,提出了一种新的 LQG 自校正控制器,它是基于最优控制理论和对象的输入输出模型设计的。该算法无需求解 Riccati 方程或进行谱因子分解,故计算量大为减小,易于实现。系统的闭环特性也在文中作了分析。