复数型扩展卡尔曼滤波算法在感应电机 直接转矩控制系统中的应用

针对传统算法中存在的数字信号处理器(DSP)运算速度要求高因而容易产生较大的延迟的问题.提出一种复数型扩展卡尔曼滤波观测器(ECKF)对感应电机进行状态估计,将得到的定子磁链和电机转速应用于直接转矩控制系统中,实现感应电机的无速度传感器控制.采用感应电机复数模型进行滤波器设计可以简化感应电机状态方程的维数并有效减少滤波算法计算量.由于复数型扩展卡尔曼滤波器在实现过程中没有矩阵求逆的运算,并且与常规扩展卡尔曼滤波器相比具有更低的维数,因此DSP的运算时间得到了有效的降低,提高了滤波器状态估计的快速性.仿真和实验结果验证了所提出的复数型扩展卡尔曼滤波器有效性和可行性.     

Synchronisation control for a class of complex-valued fractional-order memristor-based delayed neural networks

In this paper, the problem of drive-response synchronisation of complex-valued fractional-order memristor-based delayed neural networks is discussed via linear feedback control method. By separating complex-valued system into two equivalent real-valued systems, and using the comparison theorem, algebraic criteria are given to ascertain the synchronisation of the considered system with single delay. Meanwhile, for the case of model with multiple delays, the corresponding sufficient conditions are also presented. Because complex-valued system can reduce to real-valued ones when the imaginary part is ignored, the proposed results of this paper generalise existing works on relevant real-valued system. Finally, the effectiveness of the obtained theoretical results is testified by two numerical examples.     

基于滑模与自适应观测器的感应电机非线性控制新策略

提出一种结合滑模变结构和自适应观测技术的感应电机非线性控制新方法. 以定子电流与定子磁链为状态变量建立感应电机模型, 采用非线性分析方法建立转矩与磁链误差方程, 使用自适应滑模技术设计转矩与磁链控制器, 推导出定子电压控制量. 基于模型参考技术设计自适应观测器, 向控制器提供准确的转速辨识与磁链观测值,并给出了控制系统的稳定性证明. 该方法具有转矩脉动小、定子磁链畸变不明显的优点, 低速时也具有良好的控制性能, 且对参数与负载变化有较强的鲁棒性. 仿真与实验结果证明了该控制策略的正确性与有效性.     

An adaptive tracking control from current measurements for induction motors with uncertain load torque and rotor resistance

The problem of controlling sensorless induction motors with uncertain constant load torque and rotor resistance on the basis of stator current measurements only is addressed. A new eighth-order dynamic nonlinear adaptive control algorithm is designed, which relies on a closed loop adaptive observer for the unmeasured state variables (rotor speed and fluxes) and for the uncertain parameters and is not based on non-robust open loop integration of flux dynamics. Local exponential stability of the closed loop tracking and estimation error dynamics is achieved under persistency of excitation conditions which restrict the reference signals and may be interpreted in terms of motor observability and rotor resistance identifiability.     

Dynamical behaviour analysis of delayed complex-valued neural networks with impulsive effect

This paper investigates the problem of the dynamical behaviours of a class of complex-valued neural networks with mixed time delays and impulsive effect. By separating the complex-valued neural networks into the real and the imaginary parts, the corresponding equivalent real-valued systems are obtained. Some sufficient conditions are derived for assuring the exponential stability of the equilibrium point of the system based on the vector Lyapunov function method and mathematical induction method. The obtained results generalise the existing ones. Finally, two numerical examples with simulations are given to demonstrate the feasibility of the proposed results.     

Complex-valued multistate neural associative memory

A model of a multivalued associative memory is presented. This memory has the form of a fully connected attractor neural network composed of multistate complex-valued neurons. Such a network is able to perform the task of storing and recalling gray-scale images. It is also shown that the complex-valued fully connected neural network may be considered as a generalization of a Hopfield network containing real-valued neurons. A computational energy function is introduced and evaluated in order to prove network stability for asynchronous dynamics. Storage capacity as related to the number of accessible neuron states is also estimated.     

Complex neutrosophic set

Complex fuzzy sets and complex intuitionistic fuzzy sets cannot handle imprecise, indeterminate, inconsistent, and incomplete information of periodic nature. To overcome this difficulty, we introduce complex neutrosophic set. A complex neutrosophic set is a neutrosophic set whose complex-valued truth membership function, complex-valued indeterminacy membership function, and complex-valued falsehood membership functions are the combination of real-valued truth amplitude term in association with phase term, real-valued indeterminate amplitude term with phase term, and real-valued false amplitude term with phase term, respectively. Complex neutrosophic set is an extension of the neutrosophic set. Further set theoretic operations such as complement, union, intersection, complex neutrosophic product, Cartesian product, distance measure, and δ-equalities of complex neutrosophic sets are studied here. A possible application of complex neutrosophic set is presented in this paper. Drawbacks and failure of the current methods are shown, and we also give a comparison of complex neutrosophic set to all such methods in this paper. We also showed in this paper the dominancy of complex neutrosophic set to all current methods through the graph.

     

A Split-Complex Valued Gradient-Based Descent Neuro-Fuzzy Algorithm for TS System and Its Convergence

In order to broaden the study of the most popular and general Takagi–Sugeno (TS) system, we propose a complex-valued neuro-fuzzy inference system which realises the zero-order TS system in the complex-valued network architecture and develop it. In the complex domain, boundedness and analyticity cannot be achieved together. The splitting strategy is given by computing the gradients of the real-valued error function with respect to the real and the imaginary parts of the weight parameters independently. Specifically, this system has four layers: in the Gaussian layer, the L-dimensional complex-valued input features are mapped to a Q-dimensional real-valued space, and in the output layer, complex-valued weights are employed to project it back to the complex domain. Hence, split-complex valued gradients of the real-valued error function are obtained, forming the split-complex valued neuro-fuzzy (split-CVNF) learning algorithm based on gradient descent. Another contribution of this paper is that the deterministic convergence of the split-CVNF algorithm is analysed. It is proved that the error function is monotone during the training iteration process, and the sum of gradient norms tends to zero. By adding a moderate condition, the weight sequence itself is also proved to be convergent.

     

基于DST的实值离散Gabor变换

Gabor变换已被公认为是通信和信号处理中信号与图像表示的最好的方法之一,一直以来对Gabor变换的研究和应用实际上是基于Fourier变换的复值Gabor变换,因此这里对实值Gabor变换进行了研究。采用双正交分析方法,定义了一种基于离散正弦变换(DST)的实值离散Gabor变换(RDGT),该变换不仅适用于临界抽样条件而且适用于过抽样条件,并证明了变换的完备性条件(即该变换中综合窗与分析窗的双正交条件),该实验结果也验证了变换的完备性。针对实值信号,该变换由于仅涉及实值运算,并可利用快速DSTI、DST算法来加速变换,因此比传统复值离散Gabor变换在计算、实现方面更为简单。在实际应用中,将更方便于软件和硬件的实现。     

Dynamic feedback linearization of the induction motor

Dynamic state feedback is used to achieve feedback linearization of the induction motor. It is shown that the sixth-order nonlinear dynamical model of the induction motor consisting of rotor speed, two stator currents, two rotor fluxes and an integrator in the feedback controller can be made equivalent to two third-order decoupled linear systems by nonlinear state feedback through the stator input voltages. It is further shown that a nonsingular dynamic feedback linearizing transformation exists as long as the (electromagnetic) torque put out by the motor is nonzero     

Analysis and design of complex-valued linear systems

This paper studies a class of complex-valued linear systems whose state evolution dependents on both the state vector and its conjugate. The complex-valued linear system comes from linear dynamical quantum control theory and is also encountered when a normal linear system is controlled by feedback containing both the state vector and its conjugate that can provide more design freedom. By introducing the concept of bimatrix and its properties, the considered system is transformed into an equivalent real-representation system and a non-equivalent complex-lifting system, which are normal linear systems. Some analysis and design problems including solutions, controllability, observability, stability, eigenvalue assignment, stabilisation, linear quadratic regulation, and state observer design are then investigated. Criterion, conditions, and algorithms are provided in terms of the coefficient bimatrices of the original system. The developed approaches are also utilised to investigate the so-called antilinear system which is a special case of the considered complex-valued linear system. The existing results on this system have been improved and some new results are established.     

A global robust iterative learning position control for current-fed permanent magnet step motors

The tracking control problem via state feedback for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque is addressed: a periodic reference signal (of known period) for the rotor position is required to be tracked. A robust iterative learning control algorithm is designed which, for any motor initial condition and without requiring any resetting procedure, guarantees, despite system uncertainties: exponential convergence of the rotor position tracking error to a residual ball (centered at the origin) whose radius can be made arbitrarily small by properly setting the learning gain; asymptotic convergence of the rotor position tracking error to zero. A sufficient condition for the asymptotic estimation of the uncertain reference input achieving, for compatible initial conditions, perfect tracking is derived. Robustness with respect to a finite memory implementation of the control algorithm based on the piecewise linear approximation theory is shown to be guaranteed; satisfactory performances of a discrete-time implementation of the control algorithm are obtained in realistic simulations for the full-order voltage-fed motor.     

A fully complex-valued radial basis function classifier for real-valued classification problems

In this paper, we investigate the decision making ability of a fully complex-valued radial basis function (FC-RBF) network in solving real-valued classification problems. The FC-RBF classifier is a single hidden layer fully complex-valued neural network with a nonlinear input layer, a nonlinear hidden layer, and a linear output layer. The neurons in the input layer of the classifier employ the phase encoded transformation to map the input features from the Real domain to the Complex domain. The neurons in the hidden layer employ a fully complex-valued Gaussian-like activation function of the type of hyperbolic secant (sech). The classification ability of the classifier is first studied analytically and it is shown that the decision boundaries of the FC-RBF classifier are orthogonal to each other. Then, the performance of the FC-RBF classifier is studied experimentally using a set of real-valued benchmark problems and also a real-world problem. The study clearly indicates the superior classification ability of the FC-RBF classifier.     

Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems

In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’ for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like (‘sech’) activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently.Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced.     

Subspace identification of MIMO LPV systems using a periodic scheduling sequence

A novel subspace identification method is presented which is able to reconstruct the deterministic part of a multivariable state-space LPV system with affine parameter dependence, in the presence of process and output noise. It is assumed that the identification data is generated with the scheduling variable varying periodically during the course of the identification experiment. This allows to use methods from LTI subspace identification to determine the column space of the time-varying observability matrices. It is shown that the crucial step in determining the original LPV system is to ensure the obtained observability matrices are defined with respect to the same state basis. Once the LPV model has been identified, it is valid for other nonperiodic scheduling sequences as well.     

State Estimation of Quaternion-Valued Neural Networks with Leakage Time Delay and Mixed Two Additive Time-Varying Delays

In this paper, the state estimation of quaternion-valued neural networks (QVNNs) with leakage time delay, both discrete and distributed two additive time-varying delays is studied. By considering the QVNNs as a whole, instead of decomposing it into two complex-valued neural networks or four real-valued neural networks. Via constructing suitable Lyapunov–Krasovskii functionals, combining free weight matrix, reciprocally convex approach, and matrix inequalities, the sufficient criteria for time delays are given in the form of quaternion-valued linear matrix inequalities and complex-valued linear matrix inequalities. Some observable output measurements are used to estimate the state of neurons, which ensures the global asymptotic stability of the error-state system. Finally, the effectiveness of theoretical analysis is illustrated by a numerical simulation.

     

电机矢量控制按转子磁链定向的参数辨识方法

就异步电机旋转变换和转子磁链定向进行了分析,采用了同步旋转模型到按转予磁链定向的变换的方法,建立了新的电机旋转变换参数模型。由于这个模型包含了磁链定向的参数信息,利用该模型进行参数辨识通过计算可以得到固定的磁链定向角。分析变换和计算机仿真结果说明该方案是有效的,为交流异步电机矢量控制磁链定向实现提供了新的途径。     

一种基于锁相环原理的参考模型自适应感应电机转速估计方法

针对超低速及零定子频率运行条件下感应电动机转速的不可观测性导致基于电机模型的传统速度估计方案无法实现速度估计,引入了高频信号注入法来获得转子磁链矢量位置角并得到转子磁链的参考模型,并以转子磁链的电流模型作为调节模型,在此基础上,提出了基于锁相环原理的参考模型自适应速度估计方案．仿真结果进一步验证了该方案的有效性．     

A theoretical approach to observability analysis of the SDINS/GPS in maneuvering with horizontal constant velocity

A theoretical method for analyzing the observability of a strapdown inertial navigation system (SDINS) integrated with the global positioning system (GPS) is proposed. The analysis is performed based on two types of maneuvers for a vehicle on a horizontal trajectory: level flight with constant north velocity and level flight with constant east velocity. The observability also is analyzed using the convergence theorem, stationary state observability analysis results, and Kalman filter measurement information to rearrange the SDINS error model equation. The state variables are divided into observable and unobservable parts, and determine which state variables are observable and estimable with some errors from the relationship of observable and unobservable state variables. Our results have shown that the north and east axes accelerometer bias errors were unobservable, and that attitude errors, and east and down axes gyro bias errors were estimable with some unknown bias errors. It has been shown that horizontal maneuvering improves the observability of down axis gyro bias error compared with the stationary state, and the estimation errors of the heading error state and east axis gyro bias error are dependent on the magnitude of north velocity. The results of the theoretical observability analysis are confirmed through computer simulation.     

The Intermittent Control Synchronization of Complex-Valued Memristive Recurrent Neural Networks with Time-Delays

In this paper, the intermittent control synchronization of complex-valued memristive recurrent neural networks with time-delays is investigated. As a generalization on the real-valued memristive recurrent neural networks, complex-valued memristive recurrent neural networks own more complicated properties. In complex-valued domain, bounded and analytic complex-valued activation functions do not exist. Some assumptions about activation functions in real-valued domain cannot be applied directly to complex-valued fields. By appropriate transformation, complex-valued memristive recurrent neural networks can be divided into real parts and imaginary parts, which can avoid discussing the bounded and analytic. In the framework of differential inclusion theory and Lyapunov method, sufficient criteria of intermittent control synchronization are established. Finally, a simulation is given to verify the validity and feasibility of the sufficient conditions.