三相电压型PWM整流器的非线性控制研究

基于非线性系统反馈线性化理论，建立了三相电压型PWM整流器非线性模型，设计了一种非线性控制器。对三相电压型PWM整流器非线性控制方法进行了细致的分析与研究，仿真结果表明，PWM整流器采用这种非线性控制器比采用普通线性控制器具有更为优越的动静态性能。     

复杂系统非线性励磁控制的数字仿真研究

对一个IEEE典型的13机系统进行了数字仿真。比较了在其中一台发电机上装设比例积分微分控制器(PID)、电力系统镇定器(PSS)、线性最优励磁控制器(LOEC)和非线性励磁控制器(NEC)四种励磁控制器，对整个系统的暂态稳定的影响。仿真结果表明，非线性励磁控制器与采用其他三种控制器相比，电力系统的暂态稳定性有较明显的改善。在多机系统中的主力发电机组上装设非线性励磁控制器，将对整个系统的大干扰稳定性有明显的改善，从而揭示了非线性励磁控制(NEC)较其它3种励磁控制方式具有更优越的性能。     

三相电压型PWM整流器的级联式非线性PI控制

该文以同步旋转坐标系模型为基础，设计了三相电压型PWM整流器的级联式非线性PI控制器。采用双环结构，用三个非线性PI环节实现了对整流器直流电压与功率因数的高性能控制。以误差的非线性函数与传统PI环节构成的级联式控制器，既提高了系统响应的快速性，又增强了抗扰能力，同时计算简单易于实现。与传统PI控制器的仿真对比表明该方法能够明显改善系统的动态性能，而实验结果也验证了非线性PI控制器的优良性能。     

一种基于蚁群算法的最优非线性PID控制器的设计

本文以蚁群算法为基础，提出了一种新的非线性PID控制器及其参数优化设计方法。该控制器是基于PID控制器各增益参数与误差信号之间呈现非线性关系，根据一般控制系统的阶跃响应曲线，在不同响应时间阶段PID3个增益调节参数的理想变化情况，提出根据控制信号与误差、误差变化率之间的调节规律，拟合一组增益参数的非线性函数，并利用蚁群算法搜索出一组最优的非线性PID参数，构造最优非线性PID控制器。计算机仿真结果表明，与基于遗传算法（GA）、模拟退火算法（SA）和Z—N法设计的PID控制器（GA—PID、SA-PID、ZN-PID）相比，这种基于蚁群算法的非线性PID控制器（NLAS-PID）具有良好的动态和稳态性能，可用于控制多种不同的对象和过程。     

微机非线性励磁控制的理论研究

应用直接反馈线性化理论，结合实际应用，为发电机励磁系统设计了实用化的单机和多机统一的非线性励磁控制器，解决了电力系统的非线性特性在励磁控制器设计上的困难，使实现线性化的过程比微分几何更加简捷、清晰。通过合理的实用化过程，使所设计的非线性励磁控制器达到了分散控制的要求     

基于Hamilton能量理论的发电机汽门与励磁非线性稳定控制器的设计

基于Hamilton能量理论，该文提出了一种新的非线性稳定控制器的设计方法。文中详细阐述了该方法的理论依据并给出了控制器的具体设计步骤。应用此方法设计了发电机汽门与励磁稳定控制器，仿真结果验证了理论分析的正确性与控制律的有效性。由于在控制器设计中未用到任何线性化方法，因而所得控制器充分利用了系统的非线性特性；考虑到Hamilton能量函数实际上是动态系统的Lyapunov函数，因此，该文也给出了一种从量角度设计电力系统非线性稳定控制器的新方法。     

静止无功补偿器非线性控制对系统功角稳定的影响

用直接反馈线性化控制理论，为SVC设计了电压型非线性控制器，提出纯电压型SVC控制器只能维持装设点电压，不能增加系统功角振荡的阻尼。对纯电压型SVC非线性控制器进行了改进，设计出了既有维持SVC装设点电压水平能力，又能显著增加系统功角振荡的阻尼，较为完善的信号调制型SVC综合非线性控制器。对输电线上装设SVC的单机无穷大系统进行了数字仿真，证明控制器具有良好的动态性能。所设计的两种控制器均实现了当地信号控制，并从理论上保证了控制器具有很好的鲁棒性。     

SVC非线性控制的逆系统方法

利用非线性系统的逆系统方法，将静止无功补偿装置非线性控制问题变换为相应的线性化控制问题，设计了ＳＶＣ非线性控制器，经仿真研究表明，所设计的控制器可以显著地改善电力系统的稳定性。     

基于多目标进化算法的TCSC非线性控制器设计

提出了一种利用多目标进化算法设计可控串联补偿（TCSC）非线性稳定控制器的方案，设计目标是选取合适的控制器参数，达到提高电力系统暂态稳定性的效果。将非线性控制器反馈增益的选择归结为参数优化问题，并且在优化过程中同时考虑电力系统响应的多项性能指标。采用该方法优化的非线性控制器参数可以较有效地阻尼电力系统振荡。     

基于多目标进化算法的TCSC非线性控制器设计

提出了一种利用多目标进化算法设计可控串联补偿(TCSC)非线性稳定控制器的方案，设计目标是选取合适的控制器参数，达到提高电力系统暂态稳定性的效果。将非线性控制器反馈增益的选择归结为参数优化问题，并且在优化过程中同时考虑电力系统响应的多项性能指标。采用该方法优化的非线性控制器参数可以较有效地阻尼电力系统振荡。     

稳定平台非线性误差伪微分负反馈控制

为了进一步提高稳定平台的抗干扰能力,提出了一种基于非线性误差伪微分负反馈NLE PDFF控制新方法.首先,建立了稳定平台的数学模型;然后,设计了非线性误差反馈环节和伪微分负反馈控制器;最后,对首次提出的NLE_PDFF控制器在飞行器模拟转台上以5 Hz以内频率扰动作用,测试其抗扰动的性能.实验结果表明,对比传统的平方PI控制器,采用NLE_PDFF控制器,系统的扰动隔离度提高34％.该控制器成功应用于某型稳定平台中,经充分考核,验证了其有效性.     

一类非线性不确定系统的分散保成本控制

针对一类不确定项具有有界约束特性的非线性组合系统,基于Lyapunov稳定性理论,运用线性矩阵不等式（LMI）处理方法,提出了系统存在非线性分散鲁棒保成本控制器的充分条件。该控制器能保证闭环系统渐近稳定并且能使所给定的线性二次性能指标具有确定的上界,并利用LMI方法给出了设计该控制器的一种算法。仿真实例表明,组合系统的所有状态均能很快收敛到零,该控制器是一个快速的保成本控制器,说明了所采用方法的有效性。     

Suboptimal control for nonlinear slow-fast coupled systems using reinforcement learning and Takagi–Sugeno fuzzy methods

In this article, by using singular perturbation theory, reinforcement learning (RL), and Takagi–Sugeno (T-S) fuzzy methods, a RL-fuzzy-based composite suboptimal control method is proposed for nonlinear slow-fast coupled systems (SFCSs) with unknown slow dynamics. First, the SFCSs is decomposed into slow and fast subsystems and the original optimal control problem is reduced to two subproblems. Then, for the slow subsystem, a nonlinear coordinate transformation is introduced to transform the nonquadratic slow utility function into the quadratic form. Unmeasurable virtual slow subsystem state is reconstructed by the state measurements of original system and slow controller design algorithm is proposed in the framework of RL by utilizing the actor-critic neural networks to approximate the controller and cost function. For the fast subsystem, T-S fuzzy model is established and state measurements of the original system are exploited to reconstruct the unmeasurable fast subsystem state. Fast controller is designed with the approach of parallel distributed compensation. The obtained slow and fast controllers form the composite suboptimal controller for the original SFCSs. Considering the state reconstruction error, convergence of the slow controller design algorithm, suboptimality of the composite controller, and stability of the closed-loop SFCSs are analyzed. Finally, the effectiveness of our proposed method is illustrated by examples.     

Decentralized adaptive controller for synchronization of nonlinear dynamical heterogeneous networks

For a network of interconnected nonlinear dynamical systems, an adaptive leader–follower output feedback synchronization problem is considered. The proposed structure of decentralized controller and adaptation algorithm is based on speed gradient and passivity. Sufficient conditions of synchronization for one class of heterogeneous networks are established. An example of synchronization of the network of nonidentical Chua systems is analyzed. The main contribution of the paper is adaptive controller design and analysis under conditions of incomplete measurements, incomplete control, and uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

非线性PID控制器在超导磁储能装置中的应用

基于非线性比例积分微分PID(Proportional Integration Differential)控制器在设计上具有不依赖于被控系统数学模型的特点,设计了用于电力系统的超导磁储能装置SMES(Superconducting Magnetic Energy Storage)的非线性PID控制器。概述了非线性PID控制器利用“跟踪-微分器”非线性结构产生控制所需的比例、积分、微分信号的原理。介绍了含SMES的电力系统模型及非线性PID控制器的设计。数字仿真结果验证了所设计的控制器是可行的,同时表明该控制器结构简单、易实现。     

Passivity‐based parameterized adaptive disturbance attenuation controller design for switched polynomial nonlinear systems

This paper is concerned with the adaptive disturbance attenuation control problem for a class of switched polynomial nonlinear systems. At first, a parameterized controller is designed to transform the switched polynomial nonlinear systems into switched Hamiltonian systems with polynomial structure. Then, combining with the solve‐parameter algorithm described in this paper, a mixed adaptive passivity and H₂/H_∞ control method is devoted. Comparing with the existing results, the obtained adaptive disturbance attenuation controller has better performance. Finally, a numerical example is given to illustrate the effectiveness of the proposed methods.     

Microsecond nonlinear model predictive control for DC-DC converters

In this paper, we present a novel nonlinear model predictive control (NMPC) formulation for the transient control of a DC-DC converter. We demonstrate that a real-time implementation of the proposed NMPC scheme using the PANOC solver can be efficiently applied to control DC-DC converters in the microsecond range. Moreover, an embedded, code-generated version of PANOC can be implemented using microcontrollers or digital signal processors. The algorithm is incorporated in the transient simulator of PWM DC-DC converters, and the operation of the simulator on the boost converter's example is presented, comparing the performance of our NMPC-controller with that of a classical PID controller. The operation of the boost converter controlled using the proposed NMPC algorithm is validated experimentally.     

An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems

An improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the prediction model works well. The primary controller is designed based on IENN using the backpropagation (BP) learning algorithm with PSO. PSO is used to adjust the learning rates in the BP process for improving the learning capability. The adaptive learning rates of the controller are investigated via the Lyapunov stability theorem. The proposed controller performance is verified through numerical simulation. The method is compared with the fuzzy switching and 0/1 switching methods to show its effectiveness in terms of stability, accuracy, and robustness. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

A decoupled adaptive control algorithm for global state feedback stabilization of a class of nonlinear systems

A decoupled adaptive control algorithm, namely the combined dynamic gain and adaptive homogeneous domination approach, is introduced to solve the global state feedback stabilization problem for a class of uncertain nonlinear systems. Compared with the conventional adaptive backstepping/tuning functions approach, the algorithm differs in the way of constructing the estimator and handling the nonlinear drifts, and allows the adaptive control law that is decoupled via a dynamic gain to be designed only by choosing some appropriate constants. The proposed adaptive controller guarantees that all the states of the closed‐loop system are globally bounded and the system solutions converge to zero asymptotically. Both physical and academic examples are provided to demonstrate the validness of the theory. Copyright © 2014 John Wiley & Sons, Ltd.