液压伺服位置系统模型参考模糊自适应PID控制器的设计

针对液压伺服位置系统被控对象，提出了用模型参考模糊自适应机构对PID控制器比例系数进行在线调节，以减小液压伺服位置系统中参数摄动等引起的超调和振荡；同时为简化控制器，提出了用变积分系数的方法来消除负载扰动给系统带来的稳态误差。仿真研究结果表明，具有模糊自适应和变积分系数的控制器使控制系统既有较高的稳态精度，同时也使系统具有较快的动态响应，整个系统具有很好的鲁棒性。     

基于逆系统方法的非线性系统内模控制

本文的目标是采用逆系统方法来达到对一类特殊的非线性连续动态系统的跟踪控制。基于高斯基函数的径向基数神经网络以其在函数逼近方面的优势而被用来逼近已知对象的α-阶积分逆系统。为了提高控制精度，我们利用内模控制来减少由于建模及扰动引起的误差作用。仿真结果表明此方法具有理想的效果和高的精度，且内模控制器设计简单。     

基于PCC的神经网络PID控制器设计

提出了一种基于可编程计算机控制器（PCC）的神经网络PID控制器,以PCC为硬件,利用神经网络逼近任意非线性函数的功能,实现了PID控制算法中三个参数Kp、Ki、Kd的在线调整。并利用神经网络模型对被控对象的输出值进行预测,根据预测值对神经网络各层中的加权系数进行在线修正,同时引入了带死区的控制算法。该设计方案具有调节速度快、适应能力强、可靠性高等优点。实验结果表明,该控制器具有强抗扰、响应快、鲁棒性好等特点。     

一般二维模糊控制器的等效神经网络建模与验证

针对模糊控制器的计算复杂性,实时性能差,易产生维度灾难等问题,利用神经网络的万能函数逼近能力,构建一个神经网络模型,精确的逼近已知的模糊控制器,从而减少运算量,实现实时控制.以一个已知的二输入单输出模糊控制器为例,建立一个与之等效的神经网络,通过训练,使得精确的逼近模糊控制系统.最后,给定输入信号,分别用模糊控制器和神经网络控制同一个被控对象.结果表明,用一个与模糊控制器等效的神经网络来控制同一个对象,控制效果非常相似.因此,用模糊控制器的等效神经网络模型代替,在实现环节上可以减少计算复杂性,维度灾难,提高实时性能.     

增量式自适应逆控制及在过热汽温中的应用

被控对象的迟延和惯性特性是影响控制系统品质的重要因素之一.从常规PID增量式控制算法入手,在对具有迟延和惯性被控对象的动态特性进行定性分析的基础上,给出了一种改进型增量式控制算法的表达式;分析了被控对象校正逆模型的参数向量与控制器的参数向量之间的联系,进而将控制器的荻取与被控对象校正逆模型的建立相结合,将控制器的自适应过程归结为被控对象校正逆模型的在线辨识问题,通过对校正逆模型的在线辨识直接对改进型增量式控制算法中控制器的参数进行在线修正,形成了一种增量式自适应逆控制系统.将该方法应用于锅炉过热汽温控制系统中,并通过与常规PID串级控制和直接自适应逆控制系统的控制效果对比,验证了所提方法的有效性.     

求解方程组的控制微粒群算法

研究了利用微粒群算法求解大型线性、非线性方程组的问题。并提出了将微粒群算法看作被控对象,引入控制器,利用适应值函数的信息调节惯性权重,以改变动态进化行为,提高算法性能来解决方程组的求解问题。数值实例显示了方法的优越性。     

基于神经网络的自校正PID控制研究

传统的比例积分微分（PID）控制器具有一定的局限性，尤其是当被控对象含有非线性、不确定性和时变特性时，常规的PID控制器往往难以发挥作用，甚至会失稳。利用神经网络进行复杂过程的PID控制很好地解决了上述问题，通过神经元的自学习能力来自适应调整权系数，利用BP算法最小化输出误差，大大提高了控制器的鲁棒性。     

基于稳定平台的广义预测控制算法

广义预测控制算法(GPC)大都属于间接算法,即通过辨识被控对象的参数,进行多步预测和在线滚动优化来设计控制律,所以需要求解逆矩阵,计算量大。提出一种改进的广义预测控制算法,这是一种直接算法,直接估计控制器的参数,避免求逆矩阵。这种控制器能任意逼近多自由度稳定平台这种不确定对象,不用知道系统的精确结构,同时由于预测控制的加入,使系统在线计算方便,控制质量提高了。通过MATLAB软件的仿真结果可以看出,传统的PID对于不确定对象不能很好的解决,而采用提出的改进广义预测控制算法,系统的鲁棒性和快速性都得到了改善,并且具有较好的控制效果。     

多性能指标系统的控制器自设计方法及其应用

针对具有多指标的被控对象,提出一种基于神经网络的控制器自设计方法。算法利用并行遗传算法按照被控对象各项性能指标进化神经网络控制器,在遗传算法每代结束时利用适应性权重法根据各项指标数据计算综合适应度值,选择综合适应度最佳个体进行遗传操作,从而获得综合性能指标最佳的控制器。将算法应用于异步电机矢量控制系统的速度控制器自设计中,通过仿真实验验证了本方法的有效性。     

基于多模型的动态矩阵控制

针对动态特性随工况而变化的复杂工业过程,本文提出一种基于多模型的动态矩阵控制。根据被控对象参数的变化范围建立多个模型,同时建立以模型输出误差为变量的具有积分特点的指标切换函数。在每一采样时刻根据指标函数与实际对象最接近的模型,并将基于此模型的控制器切换为当前控制器。仿真结果表明对多工况的控制令人满意。     

一种新型非线性PID控制器及其参数设计

方法简单、参数效率高是非线性PID控制器所期望的特征. 本文提出一种新的非线性PID控制器设计方法,并就其非线性函数的函数特点以及控制器参数选择问题进行了分析. 首先, 利用电容极间场强的作用原理提出了一种非线性函数构造方法, 并用来对常规PID控制器进行修正, 形成一种非线性PID控制器; 其次, 对非线性函数进行了分析, 指出了不同参数对非线性函数的影响; 最后, 利用T–无源的有关结论对这种非线性PID控制器的稳定性进行分析, 得到了这一非线性PID控制器的参数设计依据. 仿真实例表明了文中所提控制     

Constructing modal control systems for plants with meromorphic transfer function

A problem of constructing an infinite-dimensional controller that ensures the given desired distribution of poles and a part of zeros of the transfer function of the closed-loop system is solved for the plant with distributed parameters. Transfer functions of the plant, controller and closed-loop system are considered in the class of meromorphic functions. The modal controller is synthesized directly based on the desired transfer function of the closed-loop system. The method of synthesizing the controller is reduced to searching an interpolation series. An example is given.     

Pole assignment self-tuning controller for robotic manipulators

This paper presents an effective adaptive controller for robotic manipulators. A perturbation difference model of the manipulator is established for the first time, based on which a modified pole assignment self-tuning control algorithm is developed. The controller is designed such that the variance of a generalized cost function is minimized and the controller parameters are estimated directly. Closed-loop pole assignment is achieved by adjusting on-line the weighting factors in the cost function. Simulations to a manipulator with three degrees of freedom are given to demonstrate the effectiveness of this self-tuning controller.     

An experimental study on the optimization of controller gains for an electro-hydraulic servo system using evolution strategies

This paper deals with an experimental optimization problem of the controller gains for an electro-hydraulic position control system through evolution strategies (ESs)-based method. The optimal controller gains for the control system are obtained by maximizing fitness function designed specially to evaluate the system performance. In this paper, for an electro-hydraulic position control system which would represent a hydraulic mill stand for the roll-gap control in plate hot-rollings, the time delay controller (TDC) is designed, and three control parameters of this controller are directly optimized through a series of experiments using this method. It is shown that the near-optimal value of the controller gains is obtained in about 5th generation, which corresponds to approximately 150 experiments. The optimal controller gains are experimentally confirmed by inspecting the fitness function topologies that represent system performance in the gain spaces. It is found that there are some local optimums on a fitness function topology so that the optimization of the three control parameters of a TDC by manual tuning could be a task of great difficulty. The optimized results via the ES coincide with the maximum peak point in topologies. It is also shown that the proposed method is an efficient scheme giving economy of time and labor in optimizing the controller gains of fluid power systems experimentally.     

遗传优化的径向基函数船舶模糊控制器

研究径向基函数模糊神经网络在船舶控制器设计中的应用 ,设计了一个新型的径向基函数模糊神经网络控制器用以适应船舶在时变和不确定环境下的控制性能要求 .控制器设计的主导思想是在传统的径向基函数神经网络中增加一个模糊隐层 ,并采用遗传算法对控制器参数进行优化 .与传统方法相比 ,控制器模糊规则库的设计过程所需的先验知识更少 .最后采用Matlab 6 .1的Simulink工具以船舶运动模型为对象进行了船舶控制的仿真试验 ,结果证明了其有效性     

Switching fuzzy controller design based on switching Lyapunov function for a class of nonlinear systems

This paper presents a switching fuzzy controller design for a class of nonlinear systems. A switching fuzzy model is employed to represent the dynamics of a nonlinear system. In our previous papers, we proposed the switching fuzzy model and a switching Lyapunov function and derived stability conditions for open-loop systems. In this paper, we design a switching fuzzy controller. We firstly show that switching fuzzy controller design conditions based on the switching Lyapunov function are given in terms of bilinear matrix inequalities, which is difficult to design the controller numerically. Then, we propose a new controller design approach utilizing an augmented system. By introducing the augmented system which consists of the switching fuzzy model and a stable linear system, the controller design conditions based on the switching Lyapunov function are given in terms of linear matrix inequalities (LMIs). Therefore, we can effectively design the switching fuzzy controller via LMI-based approach. A design example illustrates the utility of this approach. Moreover, we show that the approach proposed in this paper is available in the research area of piecewise linear control.     

具有非线性不确定参数的电液伺服系统自适应backstepping控制

针对电液伺服系统中存在非线性不确定参数的问题,提出了一种采用积分型Lyapunov函数的自适应backstepping控制方法.首先定义积分型Lyapunov函数,将电液伺服系统中的非线性不确定参数转化为线性表示;然后逐步递推设计backstepping控制器,同时在控制律中加入阻尼项,从而补偿外界干扰对控制性能的影响;基于Lyapunov稳定性方法,设计了参数自适应律,并且在自适应律中引入充分光滑投影算子,实现对电液伺服系统中不确定参数漂移的抑制作用.搭建了AMESim与MATLAB的联合仿真平台,对所设计的自适应backstepping控制器进行仿真,作为对比,设计了不带有非线性参数估计的自适应backstepping控制器和PID算法.仿真表明,本文所设计的控制器具有良好的跟踪性能和补偿非线性不确定参数变化的能力.     

Observer-based adaptive FNN controller optimized by NAPSOSA for nonlinear time-delay systems

This study presents an observer-based adaptive fuzzy-neural network controller based on novel adaptive particle swarm optimization simulated annealing (NAPSOSA) for a class of uncertain nonlinear time-delay systems. Firstly, NAPSOSA is used to adjust the weighting function. Then, adaptive laws are adopted to approximate unknown nonlinear functions with unknown uncertainties, respectively. By examining the controller design, the observer-based control law and the weighting update law of the fuzzy-neural-network (FNN) controller are proposed for a class of nonlinear systems. In addition, based on strictly-positive-real (SPR) Lyapunov theory, the stabilization conditions for the closed-loop system are propounded. Furthermore, for obtaining a better performance, an algorithm consists of the adaptive FNN with NAPSOSA is presented to adjust the membership function of the controller. Finally, one simulation example is given to illustrate the effectiveness of the proposed approach.     

Optimal controller tuning for nonlinear processes

The present work proposes a systematic methodology for the optimal selection of controller parameters in the sense of minimizing a performance index, which is a quadratic function of the tracking error and the control effort. The performance index is calculated explicitly as an algebraic function of the controller parameters by solving Zubov's partial differential equation (PDE). Standard optimization techniques are then employed for the calculation of the optimal values of the controller parameters. The solution of Zubov's PDE is also used to estimate the closed-loop stability region for the chosen values of the controller parameters. The proposed approach is finally illustrated in a chemical reactor control problem.     

Solution to the positive real control problem for lineartime-invariant systems

In this paper we study the problem of synthesizing an internally stabilizing linear time-invariant controller for a linear time-invariant plant such that a given closed-loop transfer function is extended strictly positive real. Necessary and sufficient conditions for the existence of a controller are obtained. State-space formulas for the controller design are given in terms of solutions to algebraic Riccati equations (or inequalities). The order of the constructed controller does not exceed that of the plant