An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems.

In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.     

An adaptive learning algorithm for a wavelet neural network

Abstract: An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals.     

Wavenet fuzzy PID controller for nonlinear MIMO systems: Experimental validation on a high-end haptic robotic interface

A novel global PID control scheme for nonlinear MIMO systems is proposed and implemented for a robot as study case, this scheme is called AWFPID from its adaptive wavelet fuzzy PID control structure. Basically, it identifies inverse error dynamics using a radial basis neural network with daughter RASP1 wavelets activation function; its output is in cascaded with an infinite impulse response (IIR) filter to prune irrelevant signals and nodes as well as to recover a canonical form. Then, online adaptive fuzzy tuning of a discrete PID regulator is proposed, whose closed-loop guarantees global regulation for nonlinear dynamical plants. The wavelet network includes a fuzzy inference system for online tuning of learning rates. A real-time experimental study on a three degrees of freedom haptic interface, the PHANToM Premium 1.0A, highlights the regulation with smooth control effort without using the mathematical model of the robot.     

永磁同步电机模型预测转矩控制模糊排序法研究

针对永磁同步电机(PMSM)模型预测转矩控制(MPTC)中成本函数权重系数难以设计和调节的问题, 以降低逆变器开关频率的多目标控制问题为例, 本文研究了基于排序法的模型预测转矩控制策略. 本文通过优先级的设计解决了排序过程中最优电压矢量解不唯一的问题. 考虑到控制目标重要性并不完全等同, 提出了一种带有缩放因子的排序优化方法, 利用缩放因子来调节控制目标的重要程度. 不同于连续变化的权重系数, 缩放因子的作用效果具有离散分段特性, 因此其调整过程可得到有效简化. 本文进一步提出了基于模糊排序法的模型预测转矩控制策略, 实现了缩放因子的动态优化, 从而更好地适应电机不断变化的运行状态. 仿真结果表明, 与固定权重系数的传统模型预测转矩控制相比, 本文所提控制策略可降低系统的平均开关频率、转矩与磁链脉动, 并可有效抑制动态条件下的转矩和磁链脉动. 实时性实验结果表明, 排序法不会严重降低模型预测转矩控制的实时性.     

基于连接数据分析和OSELM分类器的网络入侵检测系统

针对网络入侵的实时高效检测问题,提出一种基于网络连接数据分析和在线贯序极限学习机(OSELM)分类器的网络入侵检测系统(IDS)。首先,对入侵数据库中的网络连接数据进行分析,通过特征选择算法选择出最优特征子集。然后,迭代执行交叉验证,并通过Alpha剖析来缩减样本尺寸,以此减低后续分类器的计算复杂度。最后,利用优化后的样本特征集来训练OSELM分类器,以此构建一个网络实时入侵检测系统。在NSL-KDD数据库上的实验结果表明,提出的IDS具有较高的检测率和较低的误报率,同时检测时间较短,符合实时入侵检测的要求。     

实时动态规划的最优行动判据及算法改进

主要以提高求解马尔可夫决策问题的实时动态规划(real-time dynamic programming,简称RTDP)算法的效率为目的.对几类典型的实时动态规划算法所使用的收敛判据进行了对比分析,并利用值函数上界、下界给出了称为最优行动判据的收敛判据,以及一个更适合实时算法的分支选择策略.最优行动判据可以更早地标定当前状态满足精度要求的最优行动供立即执行,而新的分支选择策略可以加快这一判据的满足.据此设计了一种有界增量实时动态规划(bounded incremental RTDP,简称BI-RTDP)算法.在两种典型仿真实时环境的实验中,BI-RTDP均显示出优于现有相关算法的实时性能.     

Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem

In this paper we discuss an online algorithm based on policy iteration for learning the continuous-time (CT) optimal control solution with infinite horizon cost for nonlinear systems with known dynamics. That is, the algorithm learns online in real-time the solution to the optimal control design HJ equation. This method finds in real-time suitable approximations of both the optimal cost and the optimal control policy, while also guaranteeing closed-loop stability. We present an online adaptive algorithm implemented as an actor/critic structure which involves simultaneous continuous-time adaptation of both actor and critic neural networks. We call this ‘synchronous’ policy iteration. A persistence of excitation condition is shown to guarantee convergence of the critic to the actual optimal value function. Novel tuning algorithms are given for both critic and actor networks, with extra nonstandard terms in the actor tuning law being required to guarantee closed-loop dynamical stability. The convergence to the optimal controller is proven, and the stability of the system is also guaranteed. Simulation examples show the effectiveness of the new algorithm.     

双层结构模型预测控制算法性能分析

双层结构模型预测控制是在常规模型预测控制的基础上集成了稳态目标计算层,所增加稳态目标计算层通过对给定外部目标进行调节或执行独立经济优化为底层动态控制提供设定值.本文着重于分析增加稳态目标计算层对常规模型预测控制策略在控制性能、经济性能、及鲁棒性方面的影响.阐明稳态目标计算是在动态不确定性下的最优决策过程,可实时考虑当前工况环境给出最佳设定值,使动态层控制量的计算更加合理,并可保证在动态控制层实现无静差控制;稳态目标计算层可利用包含目标跟踪和经济优化的性能指标,在保证跟踪控制要求的同时利用操作自由度进行经济性优化,有助于提高系统经济性能,此外由于操作自由度增加使得系统对于特定方向的扰动和通道失配具有更强的鲁棒性.最后通过实验仿真验证了所做分析的正确性.     

异构监测传感器网络寿命最大化模型及其求解

对于有两类无线传感器节点组成的异构监测网络,给出了考虑连通覆盖约束条件的求解网络寿命的最优化模型;根据KKT条件,最优解处的不等式约束可以转化为等式约束,得到了模型的最优解,该最优解对于实时调整传感器网络的感知/发射半径具有很强的指导意义.数值结果表明,网络最大寿命值随传感器的感知/发射半径的增加而减小;同时传感器发射半径的调整,可以提高网络寿命.     

Multi-player non-zero-sum games: Online adaptive learning solution of coupled Hamilton–Jacobi equations

In this paper we present an online adaptive control algorithm based on policy iteration reinforcement learning techniques to solve the continuous-time (CT) multi player non-zero-sum (NZS) game with infinite horizon for linear and nonlinear systems. NZS games allow for players to have a cooperative team component and an individual selfish component of strategy. The adaptive algorithm learns online the solution of coupled Riccati equations and coupled Hamilton–Jacobi equations for linear and nonlinear systems respectively. This adaptive control method finds in real-time approximations of the optimal value and the NZS Nash-equilibrium, while also guaranteeing closed-loop stability. The optimal-adaptive algorithm is implemented as a separate actor/critic parametric network approximator structure for every player, and involves simultaneous continuous-time adaptation of the actor/critic networks. A persistence of excitation condition is shown to guarantee convergence of every critic to the actual optimal value function for that player. A detailed mathematical analysis is done for 2-player NZS games. Novel tuning algorithms are given for the actor/critic networks. The convergence to the Nash equilibrium is proven and stability of the system is also guaranteed. This provides optimal adaptive control solutions for both non-zero-sum games and their special case, the zero-sum games. Simulation examples show the effectiveness of the new algorithm.     

700MW超超临界火电机组协调系统全工况多模型预测控制及其工程应用

针对700MW超超临界机组协调控制系统,提出全工况下的协调系统多模型预测控制策略。首先利用Gap metric理论分析机组非线性特征,指出低负荷范围内的局部模型密度应当高于高负荷范围内的局部模型密度,指导了机组运行空间的划分及全工况模型集的选取。提出使用第三方外挂实时控制平台以实现多模型预测控制算法,并给出了相应实施方案。工程应用表明,全工况下的协调系统多模型预测控制策略提升了机组协调控制系统性能,使机组具备了深调至30%Pe的能力。     

数据中心制冷系统非线性模型预测控制

数据中心制冷系统具有非线性、强耦合和大滞后特性,目前常用的PID方法无法实现系统整体能效提升,而现有非线性优化算法计算量大,不易工程实现.鉴于此,提出一种数据中心制冷系统模型预测控制策略,上层优化层设计预测控制器,其目标为在满足制冷要求的前提下降低系统能耗,优化层采用神经网络作为反馈控制器,将系统整体优化目标函数作为神经网络控制器优化性能指标,结合变分法与随机梯度下降法,通过滚动优化求取下层各回路被控变量最优设定值,算法占用存储区适中、计算量小;下层现场控制层通过实时控制使各回路被控变量跟踪最优设定值,可以在不破坏原有现场控制系统的情况下实现性能优化.构建Trnsys-Matlab联合仿真平台,针对系统夏季、过渡季和冬季的控制策略进行仿真实验.结果表明,所提出控制策略能够在满足数据中心安全运行的前提下,实现系统整体能效提升,且具有良好的鲁棒性.     

基于在线减法聚类的RBF神经网络结构设计

以设计最小径向基函数（RBF）神经网络结构为着眼点,提出一种在线RBF网络结构设计算法.该算法将在线减法聚类能实时跟踪工况的特性与RBF网络参数学习过程相结合,使得网络既能在线适应实时对象的变化又能维持紧凑的结构,有效地解决了RBF神经网络结构自组织问题.该算法只调整欧氏距离距实时工况最近的核函数,大大提高了网络的学习速度.通过对典型非线性函数逼近和混沌时间序列预测的仿真,表明所提出的算法具有良好的动态特性响应能力和逼近能力.     

在线VRFT数据驱动控制方法及其仿真研究*

虚拟参考反馈整定(VRFT)是一种离线的数据驱动控制器参数整定的方法,要求整定过程中对象特性保持不变。本文针对离线算法的不足,提出了一种在线VRFT数据驱动算法。首先利用滤波器改变了离线算法的时序,得到用于实时运算的有效数据。然后提出了基于带遗忘因子递推最小二乘法的VRFT控制器参数辨识方法,不依赖于对象模型,完全利用实时数据实现了在线控制器参数整定。仿真结果表明, 在对象特性变化较大的情况下,在线VRFT整定方法优于传统的离线VRFT方法,具有很好的自适应性。     

An intelligent online fault diagnostic scheme for nonlinear systems

An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions .     

An image-based feature tracking algorithm for real-time measurement of clad height

This paper presents a novel algorithm for real-time detection of clad height in laser cladding which is known as a layered manufacturing technique. A real-time measurement of clad geometry is based on the use of a developed trinocular optical detector composed of three CCD cameras and the associated interference filters and lenses. The images grabbed by the trinocular optical detector are fed into an algorithm which combines an image-based tracking protocol and a recurrent neural network to extract the clad height in real-time. The image feature tracking strategy is a synergy between a simple image selecting protocol, a fuzzy thresholding technique, a boundary tracing method, a perspective transformation and an extraction of elliptical features of the projected melt pool’s images. The proposed algorithm and the trained network were utilized in the process resulting in excellent detection of the clad height at various working conditions in which SS303L was deposited on mild steel. It was concluded that the developed system can detect the clad height independent from clad paths with about 12% maximum error.     

Optimal control strategy of working condition transition for copper flash smelting process

Because there are large state fluctuation of working conditions, excessive energy consumption caused by manual operation in the dynamic transient procedure for copper flash smelting process. Working conditions of copper flash smelting process must be adjusted to the complex and variable copper concentrate feeding to satisfy the smelting performance. Optimal control strategy based adjustment cost for copper flash smelting working condition transition is proposed, which can achieve the expected working condition by following the optimal working condition transition path. The Cauchy–Schwarz inequality-based two-level matching method is developed to set the expected working condition. Then with minimizing adjustment cost and restricting operation domain to optimize smelting performance indicators, the optimal control problem of working condition transition is converted into a multi-constraints optimization problem with two fixed ends. A Legendre pseudospectral-based optimization method is also presented to obtain the path of optimal working condition transition. The simulation results of actual production data collected are given to verify the effectiveness and feasibility of the proposed strategy.     

Estimation of all-terminal network reliability using an artificial neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability — that of artificial neural network (ANN) predictive models. Neural networks are constructed, trained and validated using the network topologies, the link reliabilities, and a network reliability upperbound as inputs and the exact network reliability as the target. A hierarchical approach is used: a general neural network screens all network topologies for reliability followed by a specialized neural network for highly reliable network designs. Both networks with identical link reliability and networks with varying link reliability are studied. Results, using a grouped cross-validation approach, show that the ANN approach yields more precise estimates than the upperbound, especially in the worst cases. Using the reliability estimation methods of the ANN, the upperbound and backtracking, optimal network design by simulated annealing is considered. Results show that the ANN regularly produces superior network designs at a reasonable computational cost.Scope and purposeAn important application area of operations research is the design of structures, products or systems where both technical and business aspects must be considered. One expanding design domain is the design of computer or communications networks. While cost is a prime consideration, reliability is equally important. A common reliability measure is all-terminal reliability, the probability that all nodes (computers or terminals) on the network can communicate with all others. Exact calculation of all-terminal reliability is an NP-hard problem, precluding its use during optimal network topology design, where this calculation must be made thousands or millions of times. This paper presents a novel computationally practical method for estimating all-terminal network reliability. Is shown how a neural network can be used to estimate all-terminal network reliability by using the network topology, the link reliabilities and an upperbound on all-terminal network reliability as inputs. The neural network is trained and validated on a very minute fraction of possible network topologies, and once trained, it can be used without restriction during network design for a topology of a fixed number of nodes. The trained neural network is extremely fast computationally and can accommodate a variety of network design problems. The neural network approach, an upper bound approach and an exact backtracking calculation are compared for network design using simulated annealing for optimization and show that the neural network approach yields superior designs at manageable computational cost.     

在线参数辨识和扩展卡尔曼算法的锂离子电池SOC估算研究

荷电状态(SOC)的准确估计对锂离子电池的在线实时监测和安全控制具有重要意义。以中航锂电池为研究对象,选择二阶阻容(RC)模型对电池工作特性进行表征,并结合多种工况情形对锂离子电池进行研究分析。考虑到参数辨识的初值对在线辨识修正效果的影响,搭建仿真模型与电池脉冲工况特性比较验证,仿真误差在0.05 V以内。在此基础上,构建含有遗忘因子的递推最小二乘法(FFRLS)的在线参数辨识系统,对电池动态应力测试工况(DST)进行仿真预测,相对误差在1.50%以内。针对离线参数辨识的不足,采用在线参数辨识结合扩展卡尔曼(EKF)算法对工况下电池SOC进行估计。试验结果表明,在线参数辨识下,EKF算法能够有效表征系统SOC估算,相对误差精度在0.3%以内。     

A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications

This paper presents a Wiener-type recurrent neural network with a systematic identification algorithm and a control strategy for the identification and control of unknown dynamic nonlinear systems. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our network include: (1) the two subsystems are integrated into a single network whose output is expressed by a nonlinear transformation of a linear state-space equation; (2) the characteristics of the trained network can be analyzed by its associated state-space equation using the well-developed theory of linear systems; and (3) the size of the network structure is determined by the number of state variables (or the system order) of the unknown systems to be identified. To effectively identify a given unknown system from its input–output data, we have developed a systematic identification algorithm that consists of an order determination procedure, a parameterization procedure, and an online learning procedure. The false nearest neighbors algorithm was adopted to acquire a minimal embedding dimension from the input–output data as the system order, and then the eigensystem realization algorithm (ERA) was used to initialize a best-fit state-space representation according to the acquired system order. To improve the overall identification performance, we have derived an online parameter learning algorithm based on an ordered derivatives and momentum terms. Subsequently, a simple feedback linear controller was designed to control the unknown dynamic nonlinear systems without much complexity. Computer simulations and comparisons with some existing recurrent networks have conducted to confirm the effectiveness and superiority of the proposed Wiener-type network, identification algorithm and control strategy.