具有加性和乘性噪声的线性离散时间随机系统的无模型最优跟踪控制

本文研究一类同时受加性和乘性噪声影响的离散时间随机系统的最优跟踪控制问题.通过构造由原始系统和参考轨迹组成的增广系统,将随机线性二次跟踪控制(SLQT)的成本函数转化为与增广状态相关的二次型函数,由此推导出用于求解SLQT的贝尔曼方程和增广随机代数黎卡提方程(SARE),而后进一步针对系统和参考轨迹动力学信息完全未知的情形,提出一种Q-学习算法来在线求解增广SARE,证明了该算法的收敛性,并采用批处理最小二乘法(BLS)解决该在线无模型控制算法的实现问题.通过对单相电压源UPS逆变器的仿真,验证了所提出控制方案的有效性.     

基于随机扰动梯度近似的图像复原与重构算法

为了复原缺乏先验知识的降质图像以及探索层析X射线图像重构的新途径,借鉴Spall 和Cristion的随机扰动近似(SPSA)方法,将其扩展到高阶和多元的情形,进而提出了一种新的随机扰动梯度近似算法.此算法无须先验知识或后验概率,具有良好的稳定收敛性.对比实验表明,将此算法用于图像的复原和重构可获得良好的效果,而且性能稳定.     

基于模拟退火高斯扰动的蝙蝠优化算法

蝙蝠算法（bat algorithm, BA）是一类新型的搜索全局最优解的随机优化技术。为了提高BA算法的搜索效果, 把模拟退火的思想引入到蝙蝠优化算法中, 并对蝙蝠算法的某些个体进行高斯扰动, 提出了一种基于模拟退火的高斯扰动蝙蝠优化算法（SAGBA）。分别将蝙蝠优化算法、模拟退火粒子群算法、SAGBA在20个典型的基准测试函数中进行仿真对比, 结果表明SAGBA不仅增加了全局收敛性, 而且在收敛速度和精度方面均优于其他两种算法。     

随机优化算法的基本数学原理与实践

试图探究随机优化算法的有效性,即收敛性存在背后的原理,据原理构造出两个随机优化算法。随机优化算法是对生物的一种模拟,用于解决函数或者策略的寻优问题。证明了随机优化算法要取得全局收敛所需的条件,并通过仿真验证了提出的两个随机优化算法的有效性。     

电流源型逆变器谐波电流的仿真与分析

根据梯形PWM技术的原理,重点分析在PWM生成的波形过程中起主要作用的两个变量M(半个周期中的脉冲数)和m(调制比)对电流源型逆变器输出电流中谐波分布情况的影响.利用MATLAB建立其仿真模型,并得出结论.为电流源型逆变器对电机供电时的谐波分析及其抑制提供参考依据.     

基于梯度投影法与随机优化算法的约束优化方法

针对带有线性等式和不等式约束的无确定函数形式的约束优化问题,提出一种利用梯度投影法与遗传算法、同时扰动随机逼近等随机算法相结合的优化方法。该方法利用遗传算法进行全局搜索,利用同时扰动随机逼近算法进行局部搜索,算法在每次进化时根据线性约束计算父个体处的梯度投影方向,以产生新个体,从而能够严格保证新个体满足全部约束条件。将上述约束优化算法应用于典型约束优化问题,其仿真结果表明了所提出算法的可行性和收敛性。     

基于扩张状态观测器的永磁同步电机PWM电流预测控制

针对传统永磁同步电机PWM电流预测控制中电机参数扰动偏差造成的输出电流静差及振荡问题,提出基于扩张状态观测器的新型PWM电流预测控制算法.分析电机参数扰动偏差对PWM电流预测控制系统的影响,构建相应的扩张状态观测器来观测参数偏差造成的系统扰动,为传统预测控制算法提供实时性扰动补偿,并通过极点配置验证新型算法的稳定性.仿真结果表明,新型算法能够快速无静差地观测系统扰动,有效避免电感参数扰动偏差对电流预测系统的影响.     

水波优化算法收敛性分析

水波优化(Water Wave Optimization,WWO)算法是一种受浅水波现象启发的新兴进化算法,它通过模拟水波的传播、折射、碎浪等运动机制来在高维解空间中进行高效搜索。该算法已被证明在大量基准测试问题和工程实际问题上优于其它许多前沿的启发式优化算法。从理论上分析了WWO算法的收敛性条件。通过对目标问题和算法参数设置的简化,证明了WWO中任何个体在两种特殊情况下都是收敛的:(1)只执行传播操作;(2)只执行折射操作。这两种情况分别对应两种特殊的适应度变化状态。进行了数值仿真实验,验证了上述两种收敛性条件。     

PWM整流器的径向基函数神经网络控制新方法

提出了一种用于PWM单位功率因数整流器的神经网络(neural network， NN)控制方法．运用预测电流对电压型PWM整流器的有功、无功电流实现解耦，电压环采用基于径向基函数(radial basis function， RBF)神经网络自适应调整参数的PI控制器．仿真结果表明，这种PI控制器可以在线调整PI参数，快速跟踪整流器的变化过程，使PWM整流器获得较好的动、静态特性，并对电网负载扰动有较强的适应能力．     

参考轨迹更新的点到点迭代学习控制算法优化及应用

针对受非重复扰动作用的离散线性系统的输出跟踪控制问题,提出一种基于参考轨迹更新的点到点迭代学习控制算法.首先通过构建性能指标函数对控制器进行范数优化,并给出相应的收敛性条件,使得系统输出能够跟踪上更新后参考轨迹处的期望点.其次,当系统输出端受到某批次非重复扰动的影响时,进一步通过引入拉格朗日乘子算法构造多目标性能指标函数,以优化鲁棒迭代学习控制器,达到提高收敛速度和跟踪精度的目的.最后将该算法应用于电机驱动的单机械臂控制系统中,仿真结果验证了算法的合理性和有效性.     

A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods.     

MCEDA: A novel many-objective optimization approach based on model and clustering

To solve many-objective optimization problems (MaOPs) by evolutionary algorithms (EAs), the maintenance of convergence and diversity is essential and difficult. Improved multi-objective optimization evolutionary algorithms (MOEAs), usually based on the genetic algorithm (GA), have been applied to MaOPs, which use the crossover and mutation operators of GAs to generate new solutions. In this paper, a new approach, based on decomposition and the MOEA/D framework, is proposed: model and clustering based estimation of distribution algorithm (MCEDA). MOEA/D means the multi-objective evolutionary algorithm based on decomposition. The proposed MCEDA is a new estimation of distribution algorithm (EDA) framework, which is intended to extend the application of estimation of distribution algorithm to MaOPs. MCEDA was implemented by two similar algorithm, MCEDA/B (based on bits model) and MCEDA/RM (based on regular model) to deal with MaOPs. In MCEDA, the problem is decomposed into several subproblems. For each subproblem, clustering algorithm is applied to divide the population into several subgroups. On each subgroup, an estimation model is created to generate the new population. In this work, two kinds of models are adopted, the new proposed bits model and the regular model used in RM-MEDA (a regularity model based multi-objective estimation of distribution algorithm). The non-dominated selection operator is applied to improve convergence. The proposed algorithms have been tested on the benchmark test suite for evolutionary algorithms (DTLZ). The comparison with several state-of-the-art algorithms indicates that the proposed MCEDA is a competitive and promising approach.     

Numerical treatment for boundary value problems of Pantograph functional differential equation using computational intelligence algorithms

In this study, stochastic computational techniques are developed for the solution of boundary value problems (BVPs) of second order Pantograph functional differential equation (PFDE) using artificial neural networks(ANNs), simulated annealing (SA), pattern search (PS), genetic algorithms (GAs), active-set algorithm (ASA) and their hybrid combinations. The strength of ANNs is exploited to construct a model for PFDE by defining as unsupervised error to approximate the solution. The accuracy of the model is subjected to find the appropriate design parameters of the networks. These optimal weights of the networks are trained using SA, PS and GAs, used as a tool for viable global search, hybridized with ASA for rapid local convergence. The designed schemes are evaluated by solving a numbers of BVPs for the PFDE and comparing with standard results. The reliability and effectiveness of the proposed solvers are investigated through Monte-Carlo simulations and their statistical analysis.     

Adaptive directed mutation for real-coded genetic algorithms

Adaptive directed mutation (ADM) operator, a novel, simple, and efficient real-coded genetic algorithm (RCGA) is proposed and then employed to solve complex function optimization problems. The suggested ADM operator enhances the abilities of GAs in searching global optima as well as in speeding convergence by integrating the local directional search strategy and the adaptive random search strategies. Using 41 benchmark global optimization test functions, the performance of the new algorithm is compared with five conventional mutation operators and then with six genetic algorithms (GAs) reported in literature. Results indicate that the proposed ADM-RCGA is fast, accurate, and reliable, and outperforms all the other GAs considered in the present study.     

基于PMW三相并网逆变器矢量解耦控制系统的设计

三相PWM逆变器是风力发电并网系统的主要部分,开发高性能的逆变器控制策略已成为研究的重点。对于相位幅值逆变控制电路,为电压单环结构,响应速度慢,且网侧存在直流电流偏移量,瞬态时,输入电压滤波器会出现振荡且负载电流会发生畸变。提出一种矢量解耦控制策略,对直流侧电容电压的平衡进行分析与补偿设计,给出矢量解耦控制算法的软件流程。实验结果表明,该控制策略能获得较好的控制性能,并能实现单位功率因数校正。该逆变器运行效率高,可靠性好,完全满足并网要求。     

A comparative study on the performance of dissortative mating and immigrants-based strategies for evolutionary dynamic optimization

Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behavior is known as random mating. However, non-random protocols, in which individuals mate according to their kinship or likeness, are more common in natural species. Previous studies indicate that when applied to GAs, dissortative mating - a type of non-random mating in which individuals are chosen according to their similarities - may improve their performance (on both speed and reliability). Dissortative mating maintains genetic diversity at a higher level during the run, a fact that is frequently observed as a possible cause of dissortative GAs’ ability to escape local optima. Dynamic optimization demands a special attention when designing and tuning a GA, since diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behavior of the Adaptive Dissortative Mating GA (ADMGA) in dynamic problems and compares it to GAs based on random immigrants. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with dynamic trap functions and dynamic versions of Road Royal and knapsack problems indicate that ADMGA is able to outperform other GAs on a wide range of tests, being particularly effective when the frequency of changes is low. Specifically, ADMGA outperforms two state-of-the-art algorithms on many dynamic scenarios. In addition, and unlike preceding dissortative mating GAs and other evolutionary techniques for dynamic optimization, ADMGA self-regulates the intensity of the mating restrictions and does not increase the set of parameters in GAs, thus being easier to tune.     

A novel optimal PID controller autotuning design based on the SLP algorithm

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.     

容错逆变器驱动的PMSM双环预测控制的研究

为了提高三相四开关容错逆变器驱动的永磁同步电机调速系统的性能,提出了调速系统双闭环预测控制策略。在转速环中设计了带扰动补偿的模型预测控制方法,通过离散扰动观测器估计负载扰动并进行前馈补偿,与模型预测速度控制相结合得到[q]轴电流环的期望给定值。在电流环中设计了基于离散滑模的有限控制集模型预测控制方法。通过与传统PI、FCS-MPC方法进行仿真对比,验证了该方法在空载启动、给定转速突变以及存在负载扰动、参数变化时,可使容错逆变器驱动的永磁同步电机调速系统具有更好的动态响应能力和鲁棒性。     

Design of output filter for inverters using fuzzy logic

This paper proposes a design method to improve the harmonic of output voltage of a single phase inverter with an L-C output filter using fuzzy logic controller (FLC). In practice, the harmonic characteristics of circuits are complicated and entangled. There are two kinds of harmonic sources that cause inverter output voltage waveform distortion: One is the PWM switching of inverter and the other is the nonlinear characteristics of the load. In general, PI feedback control by coefficient diagram method (CDM) is used to design the output voltage filter. The relation between the L-C value and the system time constant are described with the closed form and the filter values must be calculated repeatedly to satisfy the prescribed voltage total harmonic distortion (THD) of the system. Therefore, the MATLAB Fuzzy Logic Toolbox for the fuzzy logic control algorithm is proposed. The L-C value of the filter can be set to a fixed range in the nonlinear characteristic of the practical condition, to improve the harmonic of output voltage more effectively and to avoid repeated calculation.     

BLDCM控制系统功率逆变电路发热分析及实验验证

针对BLDCM控制系统功率逆变电路能耗问题,分析了功率器件工作时产生损耗的机理,探讨了PWM斩波方式、斩波频率及BLDCM绕组电感3个因素对功率逆变电路发热的影响并进行了相应实验验证,给出了不同条件下BLDCM电枢绕组电流波形和功率器件温升实验数据,对降低BLDCM控制系统功率逆变电路损耗进而提高控制系统效率、增强工作可靠性、提升BLDCM控制系统使用寿命等具有重要的理论及工程应用价值。