新一代的辨识与控制

本文介绍新一代的辨识与控制理论理论及其方法，特别偏重新一代辨识方法的进展。主要内容有集员辨识、极小化极大控制、辨识误差的硬界估计以及集成辨识方法等。     

同步水轮发电机组的机械参数辨识

选取特定的数学模型进行同步水轮发电机组机械参数可辨识性分析，得出该模型下可惟一辨识的结论。通过辨识算例有效地辨识出了同步水轮发电机组的机械参数，验证了数学模型选择的可用性，为水轮机机械参数的辨识奠定了理论基础。     

神经网络辨识及控制研究

研究了前向多层神经网络及其BP算法，并利用此网络设计了辨识器和控制器，进行离线辨识、在线辨识和控制等。研究结果表明，这种控制结构能对复杂的非线性系统进行有效的控制，并且具有收敛速度快、抗干扰性好、鲁棒性强等特点，效果明显优于一般的PID控制方法。     

基于相关辨识法的发电机励磁系统参数辨识

励磁系统参数辨识是电力系统四大参数辨识之一,其准确性对于电力系统运行控制与仿真具有极其重要的意义。文章给出了相关辨识法估计励磁系统线性环节参数的详细步骤。在Matlab/Simulink中构造待辨识的励磁系统模型,利用程序产生M序列伪随机信号并加入待辨识系统中进行参数辨识,比较了不同噪声幅值情况下的辨识结果,证明了在输入信号满足一定条件情况下,相关辨识法在励磁系统参数估计中的有效性。     

基于相关辨识法的发电机励磁系统参数辨识

励磁系统参数辨识是电力系统四大参数辨识之一,其准确性对于电力系统运行控制与仿真具有极其重要的意义。文章给出了相关辨识法估计励磁系统线性环节参数的详细步骤。在Matlab／Simulink中构造待辨识的励磁系统模型,利用程序产生M序列伪随机信号并加入待辨识系统中进行参数辨识,比较了不同噪声幅值情况下的辨识结果,证明了在输入信号满足一定条件情况下,相关辨识法在励磁系统参数估计中的有效性。     

基于相关辨识法的发电机励磁系统参数辨识

励磁系统参数辨识是电力系统四大参数辨识之一,其准确性对于电力系统运行控制与仿真具有极其重要的意义.文章给出了相关辨识法估计励磁系统线性环节参数的详细步骤.在Matlab/Simulink中构造待辨识的励磁系统模型,利用程序产生M序列伪随机信号并加入待辨识系统中进行参数辨识,比较了不同噪声幅值情况下的辨识结果,证明了在输入信号满足一定条件情况下,相关辨识法在励磁系统参数估计中的有效性.     

小波分析在系统辨识中的应用

对小波分析的主要研究方向和特点进行了介绍，综述了小波分析在系统辨识中的应用。针对不同的辨识模型，讨论了小波分析不同的应用形式，研究了小波网络在系统辨识中的应用。最后探讨了小波分析在系统辨识中的研究方向。     

基于相关辨识法的发电机励磁系统参数辨识

励磁系统参数辨识是电力系统四大参教辨识之一,其准确性对于电力系统运行控制与仿真具有极其重要的意义.文章给出了相关辨识法估计励磁系统线性环节参数的详细步骤.在Matlab/Simulink中构造待辨识的励磁系统模型,利用程序产生M序列伪随机信号并加入待辨识系统中进行参数辨识,比较了不同噪声幅值情况下的辨识结果,证明了在输入信号满足一定条件情况下,相关辨识法在励磁系统参数估计中的有效性.     

电力系统综合负荷模型的辨识方法研究

针对电力系统综合负荷模型有些参数仅利用常规信息不可辨识的问题，提出增加利用扰动 后稳态条件的信息，从而解决了可辨识性的问题；同时将遗传算法(GA)引入到辨识方法中， 克服了传统辨识方法对初值要求高、鲁棒性差、容易陷于局部极值点的缺陷，对复杂模型 的辨识是行之有效的。     

异步电机矢量控制系统速度辨识研究

本文针对无速度传感器异步电机矢量控制系统，提出了一个基于卡尔曼滤波器的异步电机速度辨识方法。利用电机机端的定子电压和电流，运用卡尔曼滤波辨识速度。为提高算法的快速法，采用了降低观测器方法，该方法具有较高的精度，仿真结果表明，转速在全速度范围内都有较好的辨识精度。     

基于Hopfield神经网络的线性系统参数辨识方案及在鼠笼式电机传动系统参数辨识中的应用研究

将基于Hpofield神经网络的线性系统参数辨识方案^[1,2]作了扩展,得出了在辨识神经网络输入为经传感器检验延迟的系统状态变量的情况下,其辨识输出趋于正确的充分条件。通过在鼠笼式电机传动系统参数辨识中应用的仿真结果,验证了该辨识方案的正确性。     

参数在线跟踪的交流传动系统双神经网络模型参考自适应控制

该文研究的目的在于将一种具有优越的非线性并行处理特征的神经网络引入自适应控制器的设计中，将其并行收敛特性和便于实行的参数设计原则与模型参考自适应控制模式结合起来，进行具有很高自适应控制要求的交流传动系统控制器设计。该文将Hopfield神经网络引入交流传动系统的模型参考自适应控制，通过神经网络控制器来给出交流传动系统的励磁及速度控制器输出，使控制效果具有对某些参数变化的一定程度的鲁棒性。对于不可控的负载转矩分量，加入参数自动跟踪神经网络，构成上有参数在线跟踪功能的交流传动双神经网络模型参考自适应控制模式，进一步提高了系统的控制性能。结果充分证明了Hopfield神经网络在处理自适应交流传动系统控制问题中的适用特征。     

参数在线跟踪的交流传动系统双神经网络自适应规划控制

本文将Hopfield神经网络引入交流传动系统自适应控制，通过神经网络来规划交流调速系统的速度控制器动态输出，使速度控制具有对某些参数变化的一定程度的鲁棒性，对于不同可控的负载转矩分量，加入作者先前所提出的参数自动跟踪神经网络，构成具有参数在线跟踪功能的交流传动双神经网络自适应规划控制模式，进一步提高了系统的控制性能。     

基于Hopfield神经网络的直流传动系统模型参考自适应控制

本文将Hopfield神经网络引入直流传动系统的模型参考自适应控制,通过神经网络模型参考自适应控制器来给出常规双闭环调速系统的速度控制器外环动态输出,使速度控制具有对某些参数变化的一定程度的鲁棒性     

Locomotion Control of MEMS Microrobot Using Pulse‐Type Hardware Neural Networks

This paper presents the locomotion control of a microelectromechanical system (MEMS) microrobot. The MEMS microrobot demonstrates locomotion control by pulse‐type hardware neural networks (P‐HNN). P‐HNN generate oscillatory patterns of electrical activity like those of living organisms. The basic component of P‐HNN is a pulse‐type hardware neuron model (P‐HNM). The P‐HNM has the same basic features as biological neurons, such as the threshold, the refractory period, and spatiotemporal summation characteristics, and allows the generation of continuous action potentials. P‐HNN has been constructed with MOSFETs and can be integrated by CMOS technology. Like living organisms, P‐HNN has realized robot control without using software programs or A/D converters. The size of the microrobot fabricated by MEMS technology was 4 × 4 × 3.5 mm. The frame of the robot was made of a silicon wafer, equipped with rotary actuators, link mechanisms, and six legs. The MEMS microrobot emulated the locomotion method and the neural networks of an insect by rotary actuators, link mechanisms, and the P‐HNN. We show that the P‐HNN can control the forward and backward locomotion of the fabricated MEMS microrobot, and that it is possible to switch its direction by inputting an external trigger pulse. The locomotion speed was 19.5 mm/min and the step size was 1.3 mm. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 43–50, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22473     

Generation allocation problem using a Hopfield-bisection approach including transmission losses

The objective of the paper is to solve generation allocation problem by minimizing total production cost, including transmission losses using a Hopfield neural network (HNN) algorithm. The generation allocation problem is commonly known as economic dispatch (ED). The computation procedure of the proposed HNN method is direct and do not need training and has been developed and mapped to solve the generation allocation problem of thermal generators. The procedure employs a linear input-output model for the neurons instead of the sigmoidal function. Formulations for solving the ED problem are explored. Through the application of these formulations, direct computation instead of iterations for solving the problem becomes possible. Not like the usual Hopfield methods, which select the weighting factors of the energy function by trials, the proposed method determines the corresponding factors by calculations. To include transmission losses in ED solution, we propose a dichotomy solution combined to the HNN. The effectiveness of the developed method is identified through its application to the 15-unit system. Computational results manifest that the method has a lot of excellent performances.     

Application of Hopfield Neural Network for Harmonic Current Estimation and Shunt Compensation

Harmonics generated by nonlinear loads pollute the power system and affect the operation of equipment connected to it. Hence, harmonic mitigation is of prime concern to a power system engineer. Artificial Neural Network (ANN) is a nonlinear signal processing technique, which is built from interconnected elementary processors called neurons. In this article, a Hopfield Neural Network (HNN) based control algorithm for shunt compensator in a power distribution system is realized. The Hopfield network is modeled using energy minimization principle and consists of “n” interconnected neurons. The HNN is used to estimate different harmonic components present in distribution system operating with nonlinear loads. It also provides suitable control signals to the shunt compensator for compensation of various power quality issues such as power factor correction, load balancing, and harmonic reduction in the distribution system. Detailed experimental results are presented along with simulation studies on the prototype model developed in the laboratory and these results demonstrate the feasibility of the proposed method of control in DSTATCOM. The comparison of the HNN-based compensation technique with a popular and effective control algorithm based on Least Means Square (LMS) is also presented in this article.     

参数在线跟踪的直流传动双神经网络模型参考自适应控制

针对在“基于Hopfield神经网络的直流传动系统模型参考自适应控制“^[8]一文中指出的所提神经网络控制器在负载转矩未知和变化情况下的缺陷,将作者先前所提出的交流传动系统参数自动跟踪神经网络^[2]引入直流传动系统,构成具有参数在线跟踪功能的直流传动双神经网络模型参考自适应控制模式,进一步提高了系统的控制性能。     

A hybrid HNN-QP approach for dynamic economic dispatch problem

This paper introduces a solution of the dynamic economic dispatch (DED) problem using a hybrid approach of Hopfield neural network (HNN) and quadratic programming (QP). The hybrid algorithm is based on using enhanced HNN; to solve the static part of the problem; the QP algorithm for solving the dynamic part of the DED. This technique guarantees the global optimality of the solution due to its look-ahead capability. The new algorithm is applied and tested to an example from the literature and the solution is then compared with that obtained by some other techniques to prove the superiority and effectiveness of the proposed algorithm.     

An application of artificial neural networks to maintenance scheduling covering of thermal units over several consecutive years

Thermal units must be maintained periodically as prescribed by the electric utility industry law. As time to execute maintenance works increases with thermal unit capacity, maintenance scheduling has a great influence on the reliability and economy of a power system. In the recent amendment in the law, three inspection rankings have been introduced and scheduling over several consecutive years becomes mandatory, thus making maintenance scheduling extremely difficult. Reflecting a recent stringent supply capability, the emphasis is laid on security rather than a minimum operating cost, having been the primary objective in determining the schedules. Therefore, this study aims to level the spinning reserve at each period under study in the maintenance scheduling while taking into consideration all the amendments in the law. Although rigorous methods such as integer programming and branch and bound method can solve small scale problems, large size problems are beyond these techniques due to an exponential explosion in the number of possible combinations. The prime objective of this paper is to investigate the capability of the Hopfield neural network (HNN) in solving the newly formulated maintenance scheduling problem. The scheduling problem has been mapped on the HNN with slight problem relaxations to facilitate the implementation. A small scheduling problem that determines the maintenance schedules of 3 generators over 3 years (divided to 78 periods) has been solved by the neural network simulator. It has been made clear from simulation results that the proposed approach is very promising in handling a complicated combinatorial optimization problem.