基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。     

一类不确定系统基于滑模干扰补偿的广义预测控制

广义预测控制(GPC)是基于非线性机理模型的一种优化控制策略, 但当系统存在内部不确定性、建模动态误差和外部干扰的情况下, 采用基于标称系统模型的GPC方法的系统性能将显著下降. 为此, 针对一类不确定非线性系统, 首先分析设计了一种基于不确定模型的理想GPC控制律; 同时设计了一种滑模干扰补偿器(SMDC)对系统的复合干扰进行估计, 将其输出作为补偿控制与标称GPC控制律结合以消除不确定性和外干扰的影响, 并利用Lyapunov理论分析了闭环复合系统的性能; 最后将其应用于一种高超声速飞行器(HSV)姿态控制系统, 仿真结果表明该方法具有很好的鲁棒特性和干扰衰减特性.     

随机非线性系统基于事件触发机制的自适应神经网络控制

针对一类具有严格反馈结构且控制方向未知的随机非线性系统，提出了基于事件触发机制的自适应神经网络（Adaptive neural network，ANN）输出反馈控制方法.利用径向基神经网络逼近系统中未知的非线性函数.通过引入Nussbaum增益函数并设计滤波器，解决了系统控制方向未知的问题.通过设计具有相对阈值的事件触发机制，保证了闭环随机非线性系统的有界性.最后给出数值仿真例子验证所提控制方法的有效性.     

冷轧机液压AGC系统GPC控制仿真研究

冷轧机液压AGC系统已经成为现代带钢轧机中控制带钢厚度的关键设备,其动态性能具有大惯性、大时滞、非线性等特点.通过参考典型的液压元件的作用机理,建立了冷轧机液压系统的数学模型.利用基于支持向量机(SVM)的广义预测控制(GPC)算法对液压系统进行控制,构成了液压AGC计算机仿真系统,仿真结果表明:SVM学习速度快,在小样本情况下具有良好的非线性建模和泛化能力:基于SVM的GPC算法具有很好的控制性能.     

自适应的时变比例增益的预测PID控制器

为了利用PID控制获得先进的控制性能,将广义预测控制(GPC)用于PID参数的实时优化,在此基础上提出了一种新的基于GPC的自适应PID控制器的设计方法.该PID控制器具有时变的比例增益,并且PID控制器的设计利用了GPC的未来参考输入.因此,GPC控制律能由设计的PID控制器精确实现.为使GPC控制器稳定地获得比例增益,采用了基于互质因子分解扩展的强稳定GPC,独立于利用标准GPC设计的闭环系统而重新设计GPC控制器,保证了闭环系统的稳定性.此外,利用递推最小二乘法对系统进行在线辨识,修正模型参数,增强了系统的抗扰性.以一阶时滞非最小相位系统为被控对象,在Matlab中对该设计方法进行了仿真,仿真结果验证了该方法的有效性.     

基于ANN的动态系统状态方程辨识建模仿真

对系统辨识原理、基于神经网络（ANN）的动态系统辨识进行了分析,针对动态系统辨识模型描述的复杂性,为简化ANN辨识建模的输入／输出关系的表达,提高算法的简洁性,采用了状态方程辨识模型,并给出了基于ANN的动态系统状态方程辨识模型。为比较分析不同网络结构的辨识建模效果及网络模型泛化能力,针对三种不同网络结构方案进行了辨识建模仿真研究。仿真结果最示,基于ANN的动态系统状态方程模型的辨识建模是有效的,并且简单合理的网络结构方案,可提高网络辨识模型的泛化能力。     

非线性分离法广义预测控制稳态特性分析

对具有输入饱和约束和Harnmerstein非线性的系统,采用“非线性分离法广义预测控制(GPC)”策略,即采用线性GPC时先不考虑Hammerstein非线性,然后采用解非线性代数方程的方法处理该非线性。根据处理饱和约束和解方程的不同顺序,可得到两种“两步法GPC”和一种“非线性移去法GPC”,分析了这些方法的稳态特性,并通过仿真进行了验证。     

超临界锅炉过热汽温神经网络内模控制

锅炉过热汽温是燃煤机组运行中的重要参数,过高、过低都会对机组的安全经济运行构成威胁.由于锅炉结构复杂,系统庞大,汽温对象具有变时滞、变参数等特性,喷水减温系统采用的串级PID控制,在大范围变工况下效果往往很不理想,且PID参数整定耗时耗力.为此,该文针对600 MW超临界锅炉过热器的喷水减温系统,研究了过热汽温神经网络(ANN)内模控制方案.基于Matlab建立了汽温系统的ANN正模型和逆模型,并设计出ANN内模实时控制器.仿真表明,与原串级PID控制相比,该方案显著改善了过热汽温的控制品质.     

快速收敛的非线性SISO系统变结构控制设计

讨论了SISO非线性系统的两种滑模变结构控制设计.分别从线性滑模的趋近率及非线性滑模设计提出了新的方法及相应控制方案.文中设计的线性滑模趋近率,其趋近速度及滑动状态随系统状态变化而变化;非线性滑模从系统收敛速度及动静态性能综合考虑进行了设计.研究结果表明,相比于终端滑模,系统具有显著的快速性,并具有良好的动静态性能.最后通过三个仿真实例验证了该方案的有效性.     

基于U模型的网络控制系统时延补偿研究

针对网络控制系统非线性问题解决方法少的情况,引入U模型方法进行被控对象建模,降低时延故障对网络控制系统造成的影响,结合广义预测控制(GPC)算法和PI控制器反馈结构,提出基于U模型的改进PI型广义预测控制方法 U-PIGPC。然后结合改进SSA-BP时延预测器,设计基于SSA-BP和U-PIGPC的网络控制系统时延补偿控制器。通过仿真对比验证了U-PIGPC补偿控制的性能优于GPC和U-GPC,其超调量更小,反应速度更快,具有更好的时延补偿控制效果。     

Generalized predictive control based on neural networks

This paper presents the Generalized Predictive Control (GPC) strategy based on Artificial Neural Network (ANN) plant model. To obtain the step and the free process responses which are needed in the generalized predictive control strategy we iteratively use a multilayer feedforward ANN as a one-step-ahead predictor. A bioprocess was chosen as a realistic nonlinear SISO system to demonstrate the feasibility and the performance of this control scheme. A comparison was made between our approach and the adaptive GPC (AGPC).     

On the practice of artificial intelligence based predictive control scheme: a case study

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier, while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme, an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results of the proposed control scheme are desirably verified.     

非线性系统的蚁群优化预测PID控制

针对非线性、时变及大惯性系统的控制问题,提出了一种基于蚁群算法的预测PID控制算法。该算法以神经网络作为预测模型,将预测控制和PID控制相结合,并用蚁群算法在线优化控制器参数,其中以常规的Ziegler-N ichols方法整定的控制器参数为基础,选取蚁群优化变量的动态搜索区间。该算法考虑了控制能量受限情况下,非线性系统的预测控制问题。计算机仿真结果表明,该非线性控制方案具有较好的鲁棒性,相对传统PID控制策略还表现出了良好的动态性能,能够满足对再热汽温对象的控制要求。     

基于SVM的PVC汽提过程预测控制方法

针对汽提塔温度控制系统的非线性和参数时变的特性,采用支持向量机对汽提塔温度进行建模,结合非线性模型的实时线性化和广义预测控制隐式算法,提出了基于支持向量预测模型的广义预测控制算法.同时将该算法应用到聚氯乙烯汽提生产过程当中,并将模型在线校正和误差反馈校正相结合,根据实际情况进行了多种情况下的仿真,仿真结果表明了方法的有效性.     

Adaptive-Predictive Control of a Class of SISO Nonlinear Systems

In this paper, an adaptive-predictive control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalized predictive control (GPC) approach. The design is model-free, based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using a recursive least squares type of identification algorithm. The proposed control is especially useful for nonlinear systems with vaguely known dynamics. Robust stability of the closed-loop system is analyzed and proven in the paper. Simulation and real-time application examples are provided for real nonlinear systems which are known to be difficult to model and control.     

A new T-S fuzzy model predictive control for nonlinear processes

In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems.     

Identification of nonlinear dynamic systems using functional linkartificial neural networks

We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the input pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.     

Support vector machines‐based generalized predictive control

In this study, we propose a novel control methodology that introduces the use of support vector machines (SVMs) in the generalized predictive control (GPC) scheme. The SVM regression algorithms have extensively been used for modelling nonlinear systems due to their assurance of global solution, which is achieved by transforming the regression problem into a convex optimization problem in dual space, and also their higher generalization potential. These key features of the SVM structures lead us to the idea of employing a SVM model of an unknown plant within the GPC context. In particular, the SVM model can be employed to obtain gradient information and also it can predict future trajectory of the plant output, which are needed in the cost function minimization block. Simulations have confirmed that proposed SVM‐based GPC scheme can provide a noticeably high control performance, in other words, an unknown nonlinear plant controlled by SVM‐based GPC can accurately track the reference inputs with different shapes. Moreover, the proposed SVM‐based GPC scheme maintains its control performance under noisy conditions. Copyright © 2006 John Wiley & Sons, Ltd.     

基于C-R模糊模型的广义预测控制算法

该文对非线性系统的建模采用Cao-Ress(C-R)模糊模型，并用卡尔曼滤波算法在线辨识模糊模型的结论参数，从而减少了参数辨识的数量和避免了矩阵的求逆运算，然后在每一个采样点对该系统进行局部动态线性化，根据得到的系统线性化模型对系统采取广义预测控制(GPC)方法得到当前的控制动作。仿真结果表明了该方法的有效性。