开放平台下调速系统模糊控制的教学实践

本文介绍了基于开放式计算机控制直流调速系统教学实验平台,指导学生在传统PID调节器中加入模糊控制策略的教学实践.笔者分析了Matlab/SimuIink软件环境下参数在线自整定模糊PID控制器的设计过程,并对比分析了不同控制策略实验结果.开发的实验装置新功能可以有效提升实验教学水平.     

基于Dahlin算法变风量空调自校正PID控制

变风量空调具有时变、延时、非线性等特点,在此类系统中传统的PID控制算法难以取得最佳的控制效果.基于广义预测自校正的控制算法,控制精度高,具有较强的跟踪性能,但是以增大的计算量和频繁的调节系统参数为代价的.文中提出一种基于改进的Dahlin参数整定方法的自校正PID控制器,该算法在理论建模的基础上,采用在线的带遗忘因子最小二乘法,实现传递函数的参数估计,建立了系统PID参数之间的关系式,实现变风量空调系统的末端控制.算法仿真运算及实验表明,与传统PID及广义预测自校正控制方法相比,文中方法控制精度高于传统PID,同时计算复杂度低于通常广义预测自校正控制.     

聚合物微纳结构热压机的温度控制策略研究

设计实现性能良好的温度控制是聚合物微纳热压工艺探索及高质量热压加工的必要条件。对热压机温控系统进行了建模分析;将飞升曲线法和最小二乘法相结合,对系统进行了辨识;针对系统模型参数的不确定性,设计并实现了基于系统辨识整定控制参数的PID(比例-积分-微分)控制器。实验证明,此控制器超调量0.5℃、系统稳态误差±0.2℃,并且有良好的适应性,满足微纳热压工艺要求。     

DSP-based self-tuning IP speed controller with load torquecompensation for rolling mill DC drive

This paper describes the design and the implementation of a self-tuning integral-proportional (IP) speed controller for a rolling mill DC motor drive system, based on a 32-bit floating point digital signal processor (DSP)-TMS 320C30. To get a better transient response than conventional proportional-integral (PI) and/or integral-proportional (IP) speed control in the presence of transient disturbance and/or parameter variations, an adaptive self-tuning IP speed control with load torque feedforward compensation was used. The model parameters, related to motor and load inertia and damping coefficient, were estimated online by using recursive extended least squares (RELS) estimation algorithm. On the basis of the estimated model parameters and a pole-placement design, a control signal was calculated. Digital simulation and experimental results showed that the proposed controller possesses excellent adaptation capability under parameter change and a better transient recovery characteristic than a conventional PI/IP controller under load change     

Online self-tuning ANN-based speed control of a PM DC motor

This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of the ANN. The complete system is implemented in real time using a digital signal processor controller board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC motor drive system under different operating conditions. The comparative results show that the ANN-based speed control scheme is robust, accurate, and insensitive to parameter variations and load disturbances     

基于神经网络的增量PID算法及仿真研究

为了解决传统PID控制器对时变系统控制能力不强的弱点。利用神经网络理论与传统PID控制理论相结合。设计了一种基于神经网络的增量PID控制器,实现PID参数的在线自整定。通过实例仿真试验比较,这种控制器比传统的PID具有较强的适应性,可以获得满意的控制效果。     

A DSP-based adaptive controller for a smooth positioning system

The implementation of a self-tuning regulator for the positioning of a direct-drive servomotor is described. The servo motor is a permanent magnet DC motor in which no speed reducer is used. The auto-tuning regulator consists of two major loops. The inner loop contains a feedback (PD or PID) regulator with additional feedforward terms. The parameters of the feedforward compensation are adjusted by the outer loop, which contains an online parameter estimator. The estimator is based on a recursive least-squares equation, and the estimated parameters are the load inertia and viscous friction. This self-tuning regulator has been simulated with PC.MATLAB, and the results demonstrate the high performance of the scheme. Experimental results obtained with a small DC motor (Electrocraft E-576) are presented, and these results show good agreement with the digital simulation results. There are two innovative aspects to this work. First, parameter estimation is used to adapt the feedforward compensation terms instead of the gains of the feedback controller, as usually is the case in conventional indirect self-tuning regulators. Secondly, the complete adaptive controller has been implemented using a single-chip digital signal processor (DSP), which results in the reduction of system hardware and cost     

模糊自整定PID控制器的FPGA实现

提出了一种基于VHDL描述、FPGA实现的模糊自整定PID设计。首先对模糊增量式数字PID的算法进行了介绍和数学推导,然后使用自顶向下的设计方法完成了控制器的VHDL分层设计,最后,在一个具体的FPGA芯片上实现了该控制器。由于采用了离线计算、在线查表的模糊自整定参数技术和增量式PID算法,本设计既降低了FPGA的资源耗费,又改善了传统PID控制器的控制性能。     

A new method of rotor resistance estimation for vector-controlledinduction machines

The estimation of rotor time constant, or rotor resistance, in a vector-controlled induction machine is necessary to achieve high-performance torque control. A new method of estimating the rotor resistance online, for use in a vector-controlled induction machine, is presented. It uses short duration pulses added to the constant flux reference current i_dse* and based on the resultant torque command current produced by a proportional-integral controller i_qse * adjusts the rotor resistance estimate. This method of self-tuning the vector controller to the rotor time constant, when operating in a closed-loop speed control loop, does not produce torque pulsations when correctly tuned. In comparison to other online methods such as the extended Kalman filter and the extended Luenberger observer, this method does not require voltage sensors and is computationally simpler. The rotor resistance estimation technique is illustrated through simulation and practical implementation of a vector-controlled induction machine     

VSS type self-tuning control

Discrete variable-structure control and its application to self-tuning control are considered. The proposed variable structure control does not use state variables but uses present and past output or input-output signals. Variable-structure control for the deterministic discrete-time system based on the minimum variance and generalized minimum variance control is proposed. The method is then extended so that self-tuning control is realized for the plants with uncertain parameters by identifying the control law online. A simulation study shows the effectiveness of the proposed algorithm     

Logical covariance matrix reset in self-tuning control

The paper investigates the application of a state-space self-tuning pole-placement strategy to a non-linear hydraulic test rig designed specifically for evaluating the effectiveness of self-tuning control schemes. The test rig is representative of a wide range of industrial systems exhibiting both non-linear characteristics and fast dynamic behaviour, such factors, until recently, prohibiting the effective use of linear self-tuning schemes. In recognition of this shortfall, a novel technique for enhancing the covariance matrix update stage within the parameter estimation algorithm of a self-tuning controller is proposed. The technique, termed logical covariance matrix reset, requires only a minimum of computational overhead and is shown to give rise to a significantly improved parameter tracking ability by making more effective use of potential information available to the estimation algorithm at set-point change. Results of simulation studies involving the application of the logical resetting technique to the hydraulic test rig are presented when the rig is subjected to slow variation in feed flow and sudden variation in load.     

Improvements on VSS-type self-tuning control for a trackingcontroller

This paper points out that the VSS-type self-tuning controller of K. Furuta (see ibid., vol.40, p.37-44, 1993) can be improved by modifications of the sector with separate gains and the equivalent control algorithm. The controlled system is stable in the outside of the sector and ultimately bounded in the inside of the sector. The proposed VSS-type self-tuning controller prevents the fluctuation of the estimated parameters that can occur during the parameter adaptation. This controller, having reduced the switching sector, is actually applicable to the tracking control of discrete systems in the presence of parameter uncertainties     

自校正解耦融合Kalman预报器及其收敛性

对于带有未知模型参数和噪声方差的多传感器系统,通过系统辨识方法,得到模型参数和噪声方差的信息融合估计,将其代入到最优分量按标量加权融合Kalman预报器中,得到自校正信息融合Kalman预报器,实现了状态分量的解耦。通过动态误差系统分析（DESA）方法严格证明了提出的自校正Kalman预报器按一个实现收敛于最优融合Kalman预报器,因此它有渐近最优性。应用信号处理的仿真例子验证了其有效性。     

模糊工程PID在气动位置伺服中的应用

针对现有高精度气动位置伺服控制算法复杂,难于在工程上实现的问题。提出一种基于调整因子的模糊工程PID控制器,采用归一化加速度参量反映系统响应的快慢,引入变论域的思想构建模糊工程PID控制器的自调整机构。该机构根据系统误差和归一化加速度参量输出调整因子,动态调整基于工程整定法的模糊PID控制器的比例因子,以改变模糊控制器输入输出与模糊子集的映射关系,从而改善模糊控制器的动、静态性能。仿真与试验结果表明,该方法简单且具有良好的控制效果。     

Reliable neuro self-tuning control using autoassociative neural networks for the water treatment

This paper extends the results of the research presented recently in Vienna and incorporates an algorithm to obtain very precise control of the addition of the coagulation chemical by developing a neuro self-tuning policy for automatically adjusting the parameters of a conventional PI controller.     

交流电机的智能PID控制应用研究

针对交流调速传统控制调速过程中往往会出现转速波动大和超调量等问题,无法满足控制系统的高性能要求,提出了一种自适应神经网络PID控制算法,应用反向传播人工神经网络理论,对于系统模型参数未知的情况下,使用两个人工神经网络分别进行控制系统在线辨识与PID控制器参数在线调整。经与PID控制对比进行了试验验证,表明本控制算法能让系统在很短的时间内调整出优良的控制参数,能够很好的跟踪负载变化,动态响应快,速度跟随准确,具有很强的自适应性和鲁棒性。     

基于GPC-PID的制冷系统过热度控制器

针对制冷系统的过热度控制问题,为保证系统动态性能,给出了一种基于广义预测控制滚动优化策略的PID参数自调整算法,设计了GPC-PID控制器.首先根据试验数据,基于最小二乘方法辨识得到过热度与膨胀阀开度间的传递函数模型,随后考虑系统模型的纯滞后环节,基于广义预测控制的性能指标,根据Kuhn-Tucker条件,通过滚动优化策略自动整定PID控制器的参数.最后的仿真结果表明,与传统PID控制器相比,GPC-PID控制器超调小,响应速度快,具有较好的控制效果.     

零极值目标函数系统辨识算法

该文提出一种零极值目标函数最小化系统辨识算法。目标函数为系统均方误差与系统噪声方差之差的平方,其极小值为零。在系统辨识过程中采用滑动平均法在线估计系统均方误差、输入自相关矩阵以及输入与期望响应的互相关向量。推导出自适应滤波器权值向量的更新表达式。算法的步长能够根据统计量自适应地调整,使得在得到较小稳态误差的同时提高算法收敛速度。分析了算法的稳定性,得到了算法收敛的条件。对比实验结果表明,该算法具有更快的收敛速度,更小的稳态误差以及更好的稳定性。     

State-Space Self-Tuning Control for Stochastic Fractional-Order Chaotic Systems

Based on the modified state-space self-tuning control (STC), a novel low-order tuner via the modified observer/Kalman filter identification (OKID) is proposed for stochastic fractional-order chaotic systems. The OKID method is a time-domain technique that identifies a discrete input-output map by using known input-output sampled data in the general coordinate form, through an extension of the eigensystem realization algorithm (ERA). First, the estimated system in the general coordinate based on the conventional OKID method is transformed to the one in an observer form to fit the state-space innovation form for the STC. Then, in stead of the conventional recursive least squares (RLS) identification algorithm used for STC, the Kalman filter as a parameter estimator with the state-space innovation form is presented for effectively estimating the time-varying parameters. Besides, taking the advantage of the digital redesign approach, the derivation of the current-output-based observer is proposed for the modified STC. As a result, the low-order state-space self-tuner with the high-gain controller property is then proposed for stochastic fractional-order chaotic systems, which the fractional operators are well approximated using the standard high integer-order operators. Finally, the fractional-order Chen and Roumlssler systems with stochastic system process and measurement noises are used as illustrative examples to demonstrate the effectiveness of the proposed methodology     

Experimental evaluation of a self-tuning controller

A self-tuning control (STC) scheme is examined in real time by comparing its performance with that of two other control schemes. Experimental results show that the controller is superior to a well-known conventional self-tuning controller and a proportional-plus-integral (PI) self-tuning controller whose parameters are selected on the basis of a pole assignment method. The control scheme can be extended to multi-input multi-output systems