基于SVM逆模型控制的储能电源逆变器研究

电网供电系统的储能电源变换器是储能电源系统的核心,其控制效果的好坏直接影响输出电能的质量.为进一步提高储能电源逆变输出电压波形质量,增强控制系统的鲁棒性,基于逆系统控制思想,提出一种支持向量机(SVM)直接逆控制与PID控制相结合的复合控制策略,并将其应用在储能电源控制系统中.采用支持向量机离线训练的方法,实现了对系统逆模型的辨识,并将辨识出的逆模型作为控制器,与被控对象串联构成一个伪线性系统,在此基础上,采用增量式PID控制器(IPIDC)进行补偿式控制.Matlab仿真结果表明该控制器有效减少了超调量,抗干扰能力和鲁棒性强,适用于非线性系统.     

基于混沌优化的非线性预测控制器

针对非线性系统的控制问题,本文将神经网络辨识、混沌优化和预测控制思想有机结合,提出了一种新型非线性预测控制器.该控制器以神经网络作为预测模型,混沌优化算法作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题.另外在训练神经网络过程中,采用了带混沌机制的自适应学习率的BP算法,以提高神经网络的收敛能力和收敛速度.仿真研究说明了该非线性预测控制器的有效性及实时性.     

一种多变量预测控制的分程控制策略实现方法

针对包含分程控制回路的控制系统具有 非线性特征,提出一种适用于多变量模型预测控制的分程控制实现方法.在分程控制的各线性区间内根据 各分程阀的不同动作组合建立预测模型,通过在线比较控制器输出信号值与相应定义的中间阈值选择模型,模型 发生切换时保留切换前模型中的输出预测值作为当前模型下的初始输出预测值;解决了控制过程存在分程 控制需求时,多变量预测控制器的设计问题,提高了线性预测控制器的适用范围.仿真结果验证了所提出 方法的有效性.     

基于混沌优化的LS-SVM非线性预测控制方法

针对非线性系统的控制问题,提出了一种基于LS-SVM的预测控制技术。该方法将LS-SVM和非线性预测控制思想有机结合,利用混沌映射的特性,通过引入混沌优化技术对LS-SVM参数进行优化,同时又将其作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题,仿真研究说明了该非线性预测控制器的有效性及实时性。     

基于Volterra模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

一种基于Wiener模型的非线性预测控制算法

针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号，将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wiener模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性.     

一类非线性系统的自适应预测函数控制

针对一类具有输出反馈耦合的离散非线性系统，将过程的非线性状态空间模型等效为线性时变状态空间模型；然后利用最小二乘法辨识系统参数，并通过在目标函数中引入系统状态的变化给出一种具有类似离散PI最优调节器结构的新型自适应预测函数控制器．由于引入了新的优化目标函数，该控制器控制效果与鲁棒性要优于仅考虑预测输出误差的传统预测函数控制器．仿真结果表明，该控制器优于经典离散PI最优调节器．     

基于双阶段网络模型的改进多变量广义预测控制

针对多变量非线性时滞系统存在多变量间复杂的耦合情况,多输入多输出系统转化为多个多输入单输出系统,并构建多变量双阶段神经网络时滞预测模型。在考虑耦合关系的基础上,将改进比例性能指标型广义预测控制器引入到多变量系统中。该控制器含有预测控制增量表征系统未来变化趋势,将其作为当前控制量的补偿,优化控制性能。通过300MW单元机W型火焰直吹式燃煤锅炉系统的仿真研究验证了控制方案的有效性。     

模糊预测控制炉窑温度系统中应用

针对炉窑温度系统的大时滞、多扰动和非线性的特点,将T-S模糊状态空间模型作为预测控制的预测模型,并将T-S模糊表示的非线性系统转化为线性时变系统,给出了基于状态空间的多变量复杂系统的T-S模糊模型表达形式,设计出预测时域内多模型的非线性模糊预测控制器。根据实际控制中对控制量和输出的约束,将控制器输出求解转化为二次规划问题。     

基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法

对于非线性系统预测控制问题, 本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值, 通过输出反馈和偏差校正减少预测误差, 由PSO滚动优化获得非线性系统的控制量. 该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器. 通过对单变量多变量非线性系统进行仿真, 证明了该预测控制方法是有效的, 且具有良好的自适应能力和鲁棒性.     

Linearizing feedforward–feedback control of pH processes based on the Wiener model

This paper examines the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve). Conditions under which the pH process behaves like an exact Wiener system are examined. Linearization by output transformation using both the true inverse of the titration curve and an estimate of the inverse is employed to make the pH process appear linear enabling the application of a linear feedback (PI) controller. Although many others have utilized an identified nonlinearity for linearizing feedback control of pH processes, much less work has been done on using the nonlinearity for linearizing feedforward control. Here, a simple linearizing feedforward controller is proposed based on a current estimate of the inverse titration curve. Simulated closed-loop results demonstrate the superiority of the linearizing feedforward–feedback strategy versus linearizing feedback only, when the inverse titration curve is accurately estimated.     

基于神经网络补偿的非线性时滞系统时滞正反馈控制

A new adaptive time-delay positive feedback controller (ATPFC) is presented for a class of nonlinear time-delay systems. The proposed control scheme consists of a neural networks-based identification and a time-delay positive feedback controller. Two high-order neural networks (HONN) incorporated with a special dynamic identification model are employed to identify the nonlinear system. Based on the identified model, local linearization compensation is used to deal with the unknown nonlinearity of the system. A time-delay-free inverse model of the linearized system and a desired reference model are utilized to constitute the feedback controller, which can lead the system output to track the trajectory of a reference model. Rigorous stability analysis for both the identification and the tracking error of the closed-loop control system is provided by means of Lyapunov stability criterion. Simulation results are included to demonstrate the effectiveness of the proposed scheme.     

基于多模型和SVM逆系统单元机组解耦控制

火力单元机组协调控制系统是一个多变量、强耦合的控制系统,具有非线性、耦合和延迟等特性,其性能直接影响单元机组运行的安全性和经济性.为了有效解决火力单元机组协调控制系统的耦合特性和动态非线性,设计了基于多模型和支持向量机(SVM)逆系统的解耦控制方法,并进行了相应实验研究.针对一个300 MW单元机组的试验仿真模型,得到单元机组在5个典型工作点的线性化模型,然后对每个线性化模型分别设计SVM逆模型及其动态PID控制器,进而用模型线性组合成多模型全局控制系统.通过加权多项式选取合成的多模型控制方法,可以解决负荷大范围变化引起的非线性问题;支持向量机与逆系统的结合能很好地解决非线性系统的强耦合问题.仿真研究证明了这种控制算法设计的有效性和优越性.     

Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation

It is a challenging work to design high precision/high performance motion controller for permanent magnet synchronous motor (PMSM) due to some difficulties, such as varying operating conditions, parametric uncertainties and external disturbances. In order to improve tracking control performance of PMSM, this paper proposes an adaptive fuzzy robust control (AFRC) algorithm with smooth inverse based dead-zone compensation. Instead of nonsmooth dead-zone inverse which would cause the possible control signal chattering phenomenon, a new smooth dead-zone inverse is proposed for non-symmetric dead-zone compensation in PMSM system. AFRC controller is synthesized by combining backstepping technique and small gain theorem. Discontinuous projectionbased parameter adaptive law is used to estimate unknown system parameters. The Takagi-Sugeno fuzzy logic systems are employed to approximate the unstructured dynamics. Robust control law ensures the robustness of closed loop control system. The proposed AFRC algorithm with smooth inverse based dead-zone compensation is verified on a practical PMSM control system. The comparative experimental results indicate that the smooth inverse for non-symmetric dead-zone nonlinearity can effectively avoid the chattering phenomenon which would be caused by nonsmooth dead-zone inverse, and the proposed control strategy can improve the PMSM output tracking performance.     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.     

Adaptive actuator compensation control with feedback linearization

An adaptive inverse controller is developed for feedback linearizable nonlinear systems with nonsmooth actuator nonlinearities. The use of an actuator nonlinearity inverse and a feedback linearizing controller leads to an error equation suitable for deriving an adaptive update law for the inverse parameters. Closed-loop signal boundedness is proved analytically, and system performance improvement is shown by simulation results. Such adaptive control schemes are also developed for multivariable nonlinear systems with actuator nonlinearities. For nonlinear systems that do not possess a relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities     

补偿信号法驱动的Pendubot自适应平衡控制

Pendubot是以电机转矩为输入,主动臂角度和欠驱动臂角度为输出的强非线性、多变量、欠驱动机械系统,受到具有时变不确定性的摩擦影响,且模型参数随摆臂质量与长度的改变而变化.本文将上述被控对象采用确定线性模型与未知高阶非线性项来描述,设计消除前一时刻高阶非线性项及其变化率对系统输出影响的补偿器,叠加于基于确定线性模型设计的PD控制器,提出了补偿信号法驱动的自适应平衡控制方法,并对所提方法进行了稳定性和收敛性分析.仿真和物理对比实验表明,当Pendubot系统模型参数改变时,所提控制算法可以有效地消除摩擦的影响,将两摆臂输出角度稳定在目标位置.     

多输入非线性系统控制的分步变换与设计

研究多输入非线性系统基于分步变换及设计思想的非线性控制器设计问题。针对一般多输入非线性系统，首先利用非线性状态变换的方法进行非线性变换，得到一中间非线性系统模型，然后引入状态反馈，使得闭环系统为线性能控标准模型；从而利用分步设计的思想，实现非线性系统的最优控制。最后以－液位－温度控制系统为例，通过仿真说明该方法的有效性和可行性。     

Identification and control of Hammerstein systems via wireless networks

This article investigates a systematic approach for the identification and control of Hammerstein systems over a physical IEEE 802.11b wireless channel. Three major factors which may affect system stability and stabilisation are concerned: wireless network-induced delays, nonlinearity and model mismatch. First the network-induced delays are characterised by an inverse Gaussian distribution model according to IEEE 802.11b protocol and a model-based compensation method is used to estimate the delayed samples. Then an inverse function of nonlinear part of the identified model is used to attenuate the influence of nonlinearity, while the model mismatch is regarded as disturbance which is then dealt with by H _∞ control approach. A sufficient condition for mean-square asymptotic stability is obtained and expressed by a set of linear matrix inequalities, enabling direct controller design. Finally, numerical simulation examples are used to confirm the efficacy of the proposed approach.     

Adaptive tracking control of MIMO nonlinear nonminimum phase system with unknown input nonlinearity

This paper proposes a nonlinear adaptive control for output tracking of multi‐input multi‐output nonlinear nonminimum phase system with input nonlinearity. The parameters of the input nonlinearity are assumed to be unknown. This problem is challenging, not only because of the unstable internal dynamics of nonminimum phase system, but also the existence of the unknown input nonlinearity. The partially linearized model of the original system is obtained through input/output linearization, and a states tracking model is constructed based on the computed ideal internal dynamics. A nonlinear adaptive controller, which can guarantee the bounded of output tracking error in the existence of unknown input nonlinearity, is proposed. Finally, a numerical simulation on vertical takeoff and landing aircraft is given to show the effectiveness of the proposed control methods.