基于虚拟参考反馈整定的改进无模型自适应控制

针对一类离散时间非线性系统, 提出一种基于虚拟参考反馈整定的改进无模型自适应控制方案. 首先, 利用动态线性化方法给出非线性系统的紧格式动态线性化模型; 然后, 基于优化技术设计控制算法和伪偏导数估计算法; 最后, 设计基于虚拟参考反馈整定的伪偏导数初值和重置值的估计算法. 该控制方案设计仅依赖于被控系统的输入和输出数据, 且能保证闭环系统的稳定性和收敛性. 仿真比较结果验证了所提出方法的有效性.

     

直线电机的非参数模型直接自适应预测控制

将基于紧格式线性化的非参数模型直接自适应预测控制方法应用到直线电机速度和位置控制中.控制器的设计是直接基于伪偏导数的估计和预报,而伪偏导数信息则足通过参数估计算法和预报算法利用I/O数据在线导出.仿真演示了该方法对电机这种不确知动态非线性系统的有效性和抗干扰能力.     

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.     

基于BP算法的无模型自适应迭代学习控制

为了改善针对一般非线性离散时间系统的控制性能,引入"拟伪偏导数"概念,给出了般非线性离散时间系统沿迭代轴的非参数动态线性化形式,并综合BP神经网络以及模糊控制各自的优点,提出了基于BP算法无模型自适应迭代学习控制方案.仿真结果表明,该控制器对模型有较强的鲁棒性和跟踪性.     

数据驱动的无模型自适应直线伺服系统精密控制和实现

针对现代制造业对高精度机床伺服系统的要求, 将数据驱动的无模型自适应控制方法应用到直线伺服系统的位置控制中, 控制器设计不包括直线伺服系统结构的任何信息, 是直接基于动态线性化模型中伪偏导数的估计和预报, 而伪偏导数是根据直线电机电压输入和位置输出在线估计的. 永磁同步直线电机运动控制系统的实时实验结果表明, 在相同条件下, 数据驱动的无模型自适应控制方法的位置跟踪误差比PID减小了0.4mm到2.6 mm, 比神经网络控制时减小了0.2mm到0.5 mm. 该方法还提高了对负载扰动的鲁棒性.     

具有未知负载扰动的水井钻机电液伺服系统无模型自适应控制

针对电液伺服系统在水井钻机推进工况下存在的参数不确定以及未知负载扰动突变等非线性因素,提出了基于径向基(RBF)神经网络扰动观测器的无模型自适应控制方法.首先,通过改进的无模型自适应控制动态线性化方法,将被控系统线性化为与输入输出相关的增量形式,并将未知负载扰动合并到一个非线性项中;然后,设计了径向基神经网络扰动观测器对含有未知负载扰动的非线性项进行估计,作为对未知扰动的补偿;最后,设计了时变参数估计律,通过在线调整伪偏导数,给出了电液伺服系统的控制更新律.仿真结果表明,所设计的控制器能够对未知负载扰动突变进行补偿,并能确保跟踪误差有界收敛.     

一种基于虚拟参考反馈调整的无模型自适应控制

针对无模型自适应控制算法中对伪偏导数估计值信息利用率不高的问题,提出一种新的无模型自适应控制算法.运用虚拟参考反馈调整方法思想,将紧格式线性化后的线性模型与基本无模型自适应控制器构成的闭环系统作为参考模型,再以参考模型的输出与系统期望输出的误差作为控制器的输入,从而将伪偏导数过去时刻的估计值引入到新的控制律中,提高了伪偏导数估计值信息利用率.仿真结果表明改进的无模型自适应控制具有较好的控制性能.     

基于逆系统的赖氨酸发酵多变量解耦内模控制

针对赖氨酸发酵过程的时变、非线性和高耦合性,提出基于逆系统的赖氨酸发酵多变量解耦内模控制方法。根据动态递归模糊神经网络(DRFNN)的非线性辨识原理离线建立发酵过程的逆模型,将得到的逆模型串联在发酵系统之前,实现了发酵过程输入输出解耦线性化,从而得到伪线性系统;对复合后的伪线性系统采用内模控制。仿真结果表明,该方法能够适应赖氨酸发酵过程模型的不确定性和参数的时变性,具有较强的鲁棒性,且结构简单,易于实现。     

基于非线性系统T-S模糊模型的随机最小方差控制

以非线性系统的T-S模糊模型为基础,提出了一种基于最小方差性能指标的随机控制方法。分析了系统的稳定性问题并给出了系统稳定的充分条件。将此方法用于连续反应搅拌釜(CSTR)中和过程pH值控制的仿真试验,仿真结果表明了该方法的有效性。     

快速路入口匝道的非参数自适应迭代学习控制

基于快速路交通系统重复性和周期性的特征, 引入“拟伪偏导数”概念, 给出了宏观交通流模型沿迭代轴的非参数动态线性化形式. 进一步, 提出了快速路入口匝道的非参数自适应迭代学习控制(NP-AILC)方案. 该控制方法本质上是无模型的, 并且学习增益可迭代调节. 收敛性分析表明当系统初始状态随迭代次数随机变化时, 该方法可实现几乎完全跟踪性能. 仿真结果进一步验证了方法的有效性.     

永磁直线电机的无模型自适应控制方法研究

将基于全格式线性化的单入单出非线性离散时间系统的无模型学习自适应控制方法应用在永磁直线电机的速度和位置控制中.控制器的设计是无模型的,是直接基于称为拟梯度的向量,拟梯度向量是通过新型参数估计算法,根据给出的永磁直流直线电机运动模型的输入输出信息在线导出的.无模型控制方法非常适用于实际的阶数难以知道或难以辨识,且是时变的非线性系统.实现了系统阶数较高时的有效控制,弥补了经典自适应控制阶数高时在线计算量过大而不能适应于系统快速变化过程的不足.利用Matlab软件进行仿真实验,验证了该方法对电机这种具有不确知动态的非线性系统的稳定性和抑止外部干扰和噪声的有效性和鲁棒性.     

Learning control of process systems with hard input constraints

In this paper, a novel and simple learning control strategy based on using a bounded nonlinear controller for process systems with hard input constraints is proposed. To enable the bounded nonlinear controller to learn to control a changing plant by merely observing the process output errors, a simple learning algorithm for parameter updating is derived based on the Lyapunov stability theorem. The learning scheme is easy to implement, and does not require any a priori process knowledge except the system output response direction. For demonstrating the effectiveness and applicability of the learning control strategy, the control of a once-through boiler, as well as an open-loop unstable continuously stirred tank reactor (CSTR), were investigated. Furthermore, extensive comparisons of the proposed scheme with the conventional PI controller and with some existing model-free intelligent controllers were also performed. Due to significant features of simple structure, efficient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligent control of process systems subject to hard input constraints.     

Fuzzy control of a neutralization process

This paper studies the control of pH neutralization processes using fuzzy controllers. As the process to be controlled is highly nonlinear the usual PI-type fuzzy controller is not able to control these systems adequately. To solve this problem, based on prior knowledge of the process, the pH neutralization process is divided into several fuzzy regions such as high-gain, medium-gain and low-gain, with an auxiliary variable used to detect the process operation region. Then, a fuzzy logic controller can also be designed using this auxiliary variable as input, giving adequate performance in all regions. This controller has been tested in real-time on a laboratory plant. On-line results show that the designed control system operates the plant in a range of pH values, despite perturbations and variations of the plant parameters, obtaining good performance at the desired working points.     

Model-free learning control of neutralization processes using reinforcement learning

The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid–base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This “on-the-fly” learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.     

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.     

容积室压力的无模型自适应控制研究

为实现对具有非线性、时变和多干扰等特性的机车气制动力的精确控制,提出一种基于无模型控制算法的容积压力控制方法。它不依赖控制系统参数数学模型,不需要复杂的人工参数整定即可控制时变、多变量等复杂过程,可以避免建模不精确带来的系统误差,实现对气室压力的精确控制。仿真试验表明,该系统具有响应快速、控制精确、适应能力强、控制稳定等优点。     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

基于通用学习网络的预估控制

利用通用学习网络具有所有节点互连，任意两节点之间可以有多重连接，且连接允许有任意延迟时间的特点，对典型非线性、大滞后系统进行了辨识．结合PID控制器对pH中和过程实现了高精度的预估控制．通过与传统的Smith预估控制的比较，证明该网络能有效地应用于大滞后系统，利用该网络进行系统辨识是对未知对象模型控制的一种有效新方法．     

时滞分布参数系统间歇非周期中和控制器设计

针对时滞分布参数系统,设计中和控制器。考虑该控制器作用过程中各控制点的控制过程可能存在非连续性的问题,将中和控制方法与非周期间歇控制方案结合,研究系统稳定性。利用Lyapunov稳定性理论并结合LMI处理方法,得出时滞分布式参数系统间歇非周期中和控制器存在的充分条件。最后结合所给条件,给出一个数值仿真说明其有效性。     

一种带有无模型外环补偿的PID控制

提出了一种新的控制器来解决传统的PID控制器不能很好地控制的系统,如非线性系统、变结构系统等。使用无模型控制作为补偿,用来解决由时变参数和参数估计误差引起的系统跟踪误差。当把外环去掉时,就是PID控制器。如果PID控制效果已经达到了理想状态,即受控对象的输出已经跟踪了期望输出,无模型控制的输出控制信号不起作用。证明了无模型控制的收敛性,仿真结果表明了该方法的有效性。