基于状态方程的预测最优控制

本文提出了一种新的预测控制算法——基于状态方程的预测控制算法,推导了预测最优控制律;然后将该算法推广,提出一种关于高维系统的预测指标与状态预测算法以及最优预测控制算法,并给出该算法收敛的充分条件;最后介绍了该算法在导弹飞行控制系统中的应用及仿真结果。     

引导小生境回溯优化算法

回溯搜索优化算法（BSA）是近年提出的一种新型优化算法,针对其收敛速度较慢、易陷于局部最优的缺点,提出了一种基于最优个体引导和小生境技术相结合的改进BSA算法。本方法首先在BSA的变异操作中引入向最优个体学习的策略,以提高算法的收敛速度;其次,设计一种新的小生境排挤技术,根据每个个体到其他个体距离的平均最小值确定小生境半径,排除部分相似性较高的个体;结合群体当前的最差信息,设计一种新的变异方法产生一定数量的新个体补充到新的种群中,维持群体数量的恒定并增强群体多样性。改进的BSA算法充分考虑了算法的收敛速度和群体的多样性,较大地提高了传统BSA算法的性能。对10个典型函数进行仿真测试,并与其他算法结果进行对比,实验结果表明,改进算法在收敛速度与精度方面具有较好的效果。     

Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems

This paper proposes new stability analysis and convergence results applied to the Iterative Feedback Tuning (IFT) of a class of Takagi–Sugeno–Kang proportional-integral-fuzzy controllers (PI-FCs). The stability analysis is based on a convenient original formulation of Lyapunov’s direct method for discrete-time systems dedicated to discrete-time input affine Single Input-Single Output (SISO) systems. An IFT algorithm which sets the step size to guarantee the convergence is suggested. An inequality-type convergence condition is derived from Popov’s hyperstability theory considering the parameter update law as a nonlinear dynamical feedback system in the parameter space and iteration domain. The IFT-based design of a low-cost PI-FC is applied to a case study which deals with the angular position control of a direct current servo system laboratory equipment viewed as a particular case of input affine SISO system. A comparison of the performance of the IFT-based tuned PI-FC and the performance of the PI-FC tuned by an evolutionary-based optimization algorithm shows the performance improvement and advantages of our IFT approach to fuzzy control. Real-time experimental results are included.     

An algorithm for the solution of differential games with bounds on the state and control variables—I: The development of the algorithm

This paper deals with the development of a numerical algorithm for the solution of the multipoint boundary value problem arising as a necessary condition for extremals as obtained from the application of the theory of Calculus of Variation to two persons zero sum differential games. The paper is divided into two parts, where Part I contains the theoretical development of the algorithm and Part II contains an application of the method to a pursuit-evasion game. The numerical technique that is derived in Part I consists basically of a linearization around nominal trajectories of the performance index as well as of the static and dynamic constraints imposed on the game. A computation of optimal corrections to these trajectories is performed. Time variable stepsizes along the gradient directions of the Hamiltonian function of the system are derived to improve the convergence behavior of the algorithm. A restoration of the nominal trajectories is made during each iteration to force them to comply with the, in general, nonlinear constraints that define the game. This procedure generates nominals that represent feasible solutions to the original problem and the performance index can therefore be compared between iterations. Part II solves a nonlinear pursuit-evasion game using the technique derived in Part I. An evaluation of the advantage of the time variable step sizes is discussed in Part II.     

Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration

Recently an interesting evolutionary mechanism, sensibility, inherited from a concept model of Free Search (FS) was introduced and used for solving network problems. Unfortunately, the original FS is not easy to implement because it requires key knowledge that is not clearly defined in the existing literature to determine the neighborhood space that profoundly affects the performance of the original FS. This paper thus designs a new implementation for the concept model of FS, and proposes a new algorithm, called Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration (ADEQFS) to address this issue. In ADEQFS, we focus on designing a new mutation strategy by employing adaptive differential evolution techniques as well as concepts and principles from real-coded quantum-inspired evolutionary algorithm. In addition, we use the crossover operation from the traditional Differential Evolution scheme to alleviate the premature convergence for the proposed algorithm. Furthermore, we employ the greedy mechanism to preserve the best solutions found at each generation. The convergence analysis of the proposed algorithm is also presented in this paper. We give the proof of convergence by using the Markov chain model. Thirty-four optimization test functions with different mathematical characteristics are employed as benchmark set to test the performance of ADEQFS. The numerical results highlight the improved convergence rate and computation reliability.     

孤岛微电网中二阶滤波器与PID控制器一体化参数整定方法

在孤岛微电网系统中,为降低测量噪声影响从而引入二阶滤波器。然而二阶滤波器的引入,通常使原系统PID控制器的最优参数发生改变,从而导致控制系统性能指标下降。为解决此问题,提出了一种一体化参数整定方法。该方法利用混合粒子群算法,以不同参数下,系统性能指标在Topsis综合评价法中的高得分作为寻优目标,一体化整定PID控制器与二阶滤波器的参数,保证滤波器的引入不会对系统性能指标造成较大改变,从而提升控制器性能。通过孤岛微电网系统仿真实验,验证了该方法的可行性与有效性。     

多种群退火贪婪混合遗传算法

遗传算法是应用比较广泛的一种随机优化算法,遗传算法的收敛速度与问题解的质量是影响算法寻优性能的一对主要矛盾。为了提高遗传算法的性能,论文通过将局部搜索能力较强的贪婪算法引入遗传算法,并且同模拟退火和多种群并行遗传进化思想有机结合起来的方法,提出了一个改进型的算法——多种群退火贪婪混合遗传算法(MultigroupAnnealingGreedyHybridGeneticAlgorithm,简称MAGHGA)。仿真结果表明,该算法避免了在遗传算法中存在的早熟收敛问题,增强了算法的全局收敛性,同时也有效地提高了算法的收敛速度。     

Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints

In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme.     

New IIR filter-based adaptive algorithm in active noise control applications: Commutation error-introduced LMS algorithm and associated convergence assessment by a deterministic approach

In this study, commutation error (CE) is defined in adaptive infinite impulse response (IIR) filter-based ANC systems. CE is subsequently introduced into a new residual error to develop a new LMS-based ANC algorithm in an aim to liberate the restriction of slow adaptation posed on traditional ANC algorithms. A new deterministic analysis based on a linear time-varying system is performed to investigate convergence properties of the developed algorithm: (1) An optimal step size for the fastest convergence rate can be derived. (2) Given a persistent excitation condition and a step-size constraint, we find that the algorithm is uniformly asymptotically stable. Computer simulations indeed demonstrate a greatly improved convergence rate and efficient ANC performance for the developed algorithm as compared with that using the conventional algorithms. Experimental results verify the enhanced ANC performance in real applications. These together support the new IIR filter-based adaptive algorithm that includes CE for superior ANC performance with respect to the convergence rate and noise reduction level.     

MIMO-OFDM系统的IFD-SAGE信道估计算法

针对MIMO-OFDM系统中频域的空间交替广义期望最大化（FD-SAGE）算法估计信道性能较差以及收敛速度慢的问题,提出了一种改进的FD-SAGE信道估计算法。该算法在FD-SAGE算法的基础上,通过对SAGE算法的潜在数据和不完全数据进行分解分析推导出一种修正的SAGE算法,同时在SAGE的更新数据信息时引入最大似然算法,进而提高系统的可靠性。理论研究和仿真结果表明,该算法以牺牲少量复杂度为代价,能较好地追踪信道变化且收敛速度较快,其性能优于传统的LS算法,信号检测采用最大似然算法时,在相同误比特率情况下与理想信道估计仅相差0.5 dB。     

保留精英遗传算法收敛性和收敛速度的鞅方法分析

论文引入鞅方法取代传统的马尔科夫链理论,研究保留精英遗传算法(EGA)的收敛条件和收敛速度.通过把EGA的最大适应值函数过程描述为下鞅,基于下鞅收敛定理构造使算法满足几乎处处收敛的充分条件,分析了概率1收敛充分条件与算法操作参数的关系,并计算了EGA获得全局最优解所需的最大进化代数.使用鞅方法分析遗传算法收敛性具有独特的优势,成为分析遗传算法收敛性及其性能的新方法.     

Optimal Design of a 6-DOF Parallel Manipulator Using Particle Swarm Optimization

Performance indices of parallel manipulators (PMs) vary widely with the variation of geometric properties. Improvement of one parameter often leads to worsen the other parameters. Therefore, getting into an optimum design for the PMs has been subject of much recent research. In this paper, we optimize three performance parameters of a PM simultaneously including workspace, condition number, and stiffness. In addition, a new performance index is introduced for stiffness evaluation of the PMs. The index is invariant under similarities. Because of complexity of cost function and number of variables, choosing an optimization method that can converge to the optimum point is very important. We select particle swarm optimization (PSO) method and show that this algorithm is perfect for performance optimization of PMs. Furthermore, we propose a new subroutine added to PSO algorithm to improve its convergence.     

一种基于NFCS形态的模糊神经网络的学习算法

神经网络与模糊逻辑协同系统（NFCS）是神经网络与模型系统深度融合的一种形态，传统的BP算法也可作为NFCS的学习算法，但收敛性能不佳，针对NFCS形态的模糊神经网络提出了BP算法的一种新的改进算法（NFCS－BP），即在误差传播时不仅改变网络的连接权值，同时也改变模糊逻辑神经元模型的补偿参数，首先介绍了NFCS的协同机制和典型结构，然后详细推导了改进算法的迭代公式，实践证明，与传统BP算法相比，该算法具有收敛性能好，函数逼近精度高的优点。     

基于高斯映射和小孔成像学习策略的鲸鱼优化算法

针对鲸鱼优化算法（WOA）容易陷入局部最优解、收敛速度慢等缺陷,提出一种基于小孔成像反向学习策略的鲸鱼优化算法。首先采用高斯映射生成的混沌序列取代原始算法中随机产生的初始种群,增加种群的多样性;其次,提出了一种小孔成像反向学习策略,并结合最优最差反向学习思想,增加了寻优位置的多样性,提高了算法跳出局部最优的能力;最后,在算法中加入了一个非线性收敛因子和一个对数形式的概率阈值,在保留鲸鱼算法优点的前提下,协调了算法的全局搜索和局部开发能力。通过对10个基准函数进行仿真测试,实验结果表明改进算法在收敛速度和收敛精度等方面有明显的提高。     

hp-Legendre–Gauss collocation method for impulsive differential equations

The original Legendre–Gauss collocation method is derived for impulsive differential equations, and the convergence is analysed. Then a new hp-Legendre–Gauss collocation method is presented for impulsive differential equations, and the convergence for the hp-version method is also studied. The results obtained in this paper show that the convergence condition for the original Legendre–Gauss collocation method depends on the impulsive differential equation, and it cannot be improved, however, the convergence condition for the hp-Legendre–Gauss collocation method depends both on the impulsive differential equation and the meshsize, and we always can choose a sufficient small meshsize to satisfy it, which show that the hp-Legendre–Gauss collocation method is superior to the original version. Our theoretical results are confirmed in two test problems.     

基于P系统的粒子群优化算法

为了克服算法早熟收敛问题并提高算法精度, 引入了膜计算理论。将PSO算法与P系统相结合, 提出了一种基于P系统的粒子群优化算法 (P-PSO), 有效地平衡粒子群的全局搜索和局部寻优。采用常用的三个测试函数对新算法进行了实验, 结果表明, 提出的P-PSO算法有效地解决了算法早熟问题, 提高了算法的收敛精度。由此可见, P-PSO算法能够有效改进原有PSO算法的性能。     

块对角化分散控制的一种算法——参数插入算法

本文介绍一种参数插入方法,把最优全状态输出反馈转化为块对角形局部反馈,并使整体性能指标最小。文中对该算法的收敛性及对一般控制结构下系统最优解存在的条件及唯一性给以简要证明。     

Modified Kovarik Algorithm For Approximate Orthogonalization Of Arbitrary Matrices

In a previous paper the author presented an extension of an iterative approximate orthogonalization algorithm, due to Z. Kovarik, for arbitrary rectangular matrices. In this algorithm, as Kovarik already observed in his paper, at each iteration an inversion of a symmetric and positive definite matrix is made. The dimension of this matrix equals the number of rows of the initial one, thus the inverse computation can be very expensive. In the present paper we describe an algorithm in which the above matrix inversion step is replaced by an arbitrary odd degree polynomial matrix expression. We prove that this new algorithm converges to the same matrix as the original Kovarik's method. Some numerical experiments described in the last section of the paper show us that, even for small degree polynomial expressions the convergence properties of the new algorithm are comparable with those of the original one.     

Recursive identification of Hammerstein systems with dead-zone nonlinearity in the presence of bounded noise

The existing identification algorithms for Hammerstein systems with dead-zone nonlinearity are restricted by the noise-free condition or the stochastic noise assumption. Inspired by the practical bounded noise assumption, an improved recursive identification algorithm for Hammerstein systems with dead-zone nonlinearity is proposed. Based on the system parametric model, the algorithm is derived by minimising the feasible parameter membership set. The convergence conditions are analysed, and the adaptive weighting factor and the adaptive covariance matrix are introduced to improve the convergence. The validity of this algorithm is demonstrated by two numerical examples, including a practical DC motor case.     

Newton method based iterative learning control for discrete non-linear systems

Significant progress has been achieved in terms of both theory and industrial applications of iterative learning control (ILC) in the past decade. However, the techniques of solving non-linear ILC problems are still under development. The main result of this paper is a novel non-linear ILC algorithm that utilizes the capability of the Newton method. By setting up links between non-linear ILC problems and non-linear multivariable equations, the Newton method is introduced into the ILC framework. The implementation of the new algorithm allows one to decompose a nonlinear ILC problem into a sequence of linear time-varying ILC problems. Simulations on a discrete non-linear system and a manipulator model display its advantages. Conditions for its semi-local convergence are analysed. Links of ILC with existing non-linear topics are pointed out as ways to construct new non-linear ILC schemes. Potential improvements are discussed for future work.