An indirect adaptive pole‐placement control for MIMO discrete‐time stochastic systems

In this paper, an indirect adaptive pole‐placement control scheme for multi‐input multi‐output (MIMO) discrete‐time stochastic systems is developed. This control scheme combines a recursive least squares (RLS) estimation algorithm with pole‐placement control design to produce a control law with self‐tuning capability. A parametric model with a priori prediction outputs is adopted for modelling the controlled system. Then, a RLS estimation algorithm which applies the a posteriori prediction errors is employed to identify the parameters of the model. It is shown that the implementation of the estimation algorithm including a time‐varying inverse logarithm step size mechanism has an almost sure convergence. Further, an equivalent stochastic closed‐loop system is used here for constructing near supermartingales, allowing that the proposed control scheme facilitates the establishment of the adaptive pole‐placement control and prevents the closed‐loop control system from occurring unstable pole‐zero cancellation. An analysis is provided that this control scheme guarantees parameter estimation convergence and system stability in the mean squares sense almost surely. Simulation studies are also presented to validate the theoretical findings. Copyright © 2005 John Wiley & Sons, Ltd.     

Rapid prototyping of MIMO‐OFDM based on parity bit selected and permutation spreading

In this paper, a novel MIMO‐OFDM transmission scheme is developed to effectively enable multi‐access by joint code design across multiple antennas, subcarriers, OFDM frames, and users. It achieves better spectrum efficiency while improving bit error rate performance. The proposed scheme uses either parity bit selected or permutation techniques to assign spreading codes at the transmitter side. As a result, the detection at the receiver is greatly improved because of the fact that identifying the spreading code(s) directly yields the transmitted data symbols. The paper also investigates the field‐programmable gate array implementation of the proposed algorithms; optimization techniques are proposed to reduce area, power, and time. These techniques include a pipelined architecture for inverse FFT/FFT blocks, an efficient low complexity algorithm for despreading based on counters and comparators and an optimized architecture for complex matrix inversion using Gauss–Jordan elimination (GJ‐elimination). Finally, the fixed‐point optimized field‐programmable gate array architecture for MIMO‐OFDM transceiver is developed, where the maximum allowed performance loss because of quantization is defined, the tradeoffs between BER performance and area reduction are investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

A rapidly converging filtered‐error algorithm for multichannel active noise control

In this paper, a multichannel adaptive control algorithm is described which has good convergence properties while having relatively small computational complexity. This complexity is similar to that of the filtered‐error algorithm. In order to obtain these properties, the algorithm is based on a preprocessing step for the actuator signals using a stable and causal inverse of the minimum‐phase part of the transfer path between actuators and error sensors, the secondary path. The latter algorithm is known from the literature as postconditioned filtered‐error algorithm, which improves convergence rate for the case that the minimum‐phase part of the secondary path increases the eigenvalue spread. However, the convergence rate of this algorithm suffers from delays in the adaptation path because adaptation rates have to be reduced for larger delays. The contribution of this paper is to modify the postconditioned filtered‐error scheme in such a way that the adaptation rate can be set to a higher value. Consequently, the scheme also provides good convergence if the system contains significant delays. Furthermore, a regularized extension of the scheme is given which can be used to limit the actuator signals. Copyright © 2006 John Wiley & Sons, Ltd.     

基于稀疏近似逆预处理的牛顿－广义极小残余潮流计算方法

研究了潮流迭代求解中的雅可比矩阵预处理方法。利用矩阵分裂以及矩阵求逆运算的松弛方法,提出了两种新的稀疏近似逆预条件子或预处理方法,这两种预处理方法与牛顿－广义极小残余算法相结合,可以改进潮流计算的收敛性。最后用IEEE 300节点系统的分析计算结果验证了所提方法的有效性。     

Learning‐based iterative modular adaptive control for nonlinear systems

In this paper, we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed‐loop input‐to‐state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed‐loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data‐driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a data‐driven multiparametric extremum seeking method, which guarantees local convergence results, and the second is a Bayesian optimization‐based method called Gaussian Process Upper Confidence Bound, which guarantees global results in a compact search set. The combination of the ISS feedback and the data‐driven learning algorithms gives a learning‐based modular indirect adaptive controller. We show the efficiency of this approach on a two‐link robot manipulator numerical example.     

Iterative learning control for non‐repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults

In this work, we present a novel iterative learning control (ILC) scheme for a class of joint position constrained robot manipulator systems with both multiplicative and additive actuator faults. Unlike most ILC literature that requires identical reference trajectory from trail to trail, in this work the reference trajectory can be non‐repetitive over the iteration domain without assuming the identical initial condition. A tan‐type Barrier Lyapunov Function is proposed to deal with the constraint requirements which can be both time and iteration varying, with ILC update laws adopted to learn the iteration‐invariant system uncertainties, and robust methods used to compensate the iteration and time varying actuator faults and disturbances. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraint requirements on the joint position vector will not be violated during operation. An illustrative example on a two degree‐of‐freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Fault‐tolerant control of linear multivariable controllers using iterative feedback tuning

This paper introduces the iterative feedback tuning (IFT) into a Youla parameterization scheme for fault‐tolerant control. By off‐line IFT‐experiments of tuning Youla parameters, the proposed algorithm deals with a number of conditional failures that are described by the dual Youla parameter. The main contribution of this paper is to show how Youla scheme‐based IFT can be constructed for multivariable linear time‐invariant systems. Particular attention is given to the issue of the structure of the Youla parameter (filter), in which both finite impulse response and infinite impulse response filters are presented and compared. As an illustration, the method is applied to a simulation model of a continuous stirred tank heater system. Copyright © 2014 John Wiley & Sons, Ltd.     

An adaptive iterative learning algorithm for boundary control of a flexible manipulator

In this study, we consider the boundary control problem of a flexible manipulator in the presence of system parametric uncertainty and external disturbances. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). Based on the Lyapunov method, we propose an adaptive iterative learning control scheme for trajectory tracking and vibration suppressing of a flexible manipulator. The proposed control scheme is designed using both a proportional‐derivative feedback structure and an iterative term. The learning convergence of iterative learning control is achieved through rigorous analysis without any simplification or discretization of the PDE dynamics. Finally, the results are illustrated using numerical simulations for control performance verification. Copyright © 2016 John Wiley & Sons, Ltd.     

Parallel FETI‐DP algorithm for efficient simulation of large‐scale EM problems

An efficient parallelization of the dual‐primal finite‐element tearing and interconnecting (FETI‐DP) algorithm is presented for large‐scale electromagnetic simulations. As a nonoverlapping domain decomposition method, the FETI‐DP algorithm formulates a global interface problem, whose iterative solution is accelerated with a solution of a global corner problem. To achieve a good load balance for parallel computation, the original computational domain is decomposed into subdomains with similar sizes and shapes. The subdomains are then distributed to processors based on their close proximity to minimize inter‐processor communication. The parallel generalized minimal residual method, enhanced with the iterative classical Gram‐Schmidt orthogonalization scheme to reduce global communication, is adopted to solve the global interface problem with a fast convergence rate. The global corner‐related coarse problem is solved iteratively with a parallel communication‐avoiding biconjugate gradient stabilized method to minimize global communication, and its convergence is accelerated by a diagonal preconditioner constructed from the coarse system matrix. To alleviate neighboring communication overhead, the non‐blocking communication approach is employed in both generalized minimal residual and communication‐avoiding biconjugate gradient stabilized iterative solutions. Three numerical examples are presented to demonstrate the accuracy, scalability, and capability of the proposed parallel FETI‐DP algorithm for electromagnetic modeling of general objects and antenna arrays. Copyright © 2016 John Wiley & Sons, Ltd.     

Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.     

基于快速投影LANDWEBER法的电容层析成像图像重建算法研究

提出一种新的基于快速投影Landweber法的ECT图像重建算法。算法采用两部分迭代，初期采用预置矩阵加速迭代，该矩阵通过优化步长广义逆预迭代得到；后期采用常规投影Landweber法稳定迭代。探讨了ECT应用该算法的收敛条件，该算法易满足收敛条件且预置矩阵参数选取简单。仿真和实验结果表明，与LBP、OIOR、投影Landweber算法相比，该算法兼备收敛稳定性好及收敛速度快等优点。     

An improved decision feedback equalization using a priori information

This paper addresses the exhaustive computational complexity of the maximum‐a‐posteriori equalizer and the inefficiency of the conventional decision feedback equalizer (DFE) algorithm in iterative equalization, especially when the higher‐level modulation is used with severely distorted Inter Symbol Interference channels. The new method proposed here improves the bit error rate (BER) performance by computing the extra metric r_n+1 using the feedback symbols from previous iteration and combining it with a priori information of the symbols. After each iteration, the hard‐detected symbols are saved in the memory as a priori data for next iteration. We verified the proposed algorithm for Binary Phase Shift Keying and 8‐phase shift keying modulation. The promising simulation results show that the BER performance given by the proposed low complexity DFE algorithm improved dramatically throughout the iterations when the conventional DFE has only insignificant improvement in the process of iterative equalization. Copyright © 2009 John Wiley & Sons, Ltd.     

基于逆系统理论的感应电动机解耦控制的研究

应用多变量非线性控制的逆系统理论,对感应电动机变频调速这一多变量、非线性、强耦合的控制对象进行解耦控制,从而将其分解为转速和转子磁链两个线性子系统,并应用线性系统理论对其进行综合。研究表明,这种新的控制策略实现了感应电动机转速与转子磁链的动态解耦。论文的最后给出了仿真结果。     

Cellular wave computer algorithms with spatial semantic embedding for handwritten text recognition

The recognition of cursive handwritten texts is a complex, in some cases unsolvable, task. One problem is that in most cases it is difficult or impossible to identify each letter, even if the words are separated. In our new method, the identification of letters is not needed due to the extensive and iterative use of semantic and morphological information of a given language. We are using a spatial feature code, generated by a cellular nonlinear network (CNN) based cellular wave computer algorithm, and combine it with the linguistic properties of the given language. Most general‐purpose handwriting recognition systems lack the ability to integrate linguistic background knowledge because they use it only for post‐processing recognition results. The high‐level a priori background knowledge is, however, crucial in human reading and similarly it can boost recognition rates dramatically in case of recognition systems. In our new system we do not treat the visual source as the only input: geometric and linguistic information are given equal importance. On the geometric side we use word‐level holistic feature detection without letter segmentation by analogic CNN algorithms designed for cellular wave computers (IEEE Trans. Circuits Syst. 1993; 40 :163–173; Cellular Neural Networks and Visual Computing, Foundations and Applications. Cambridge University Press: Cambridge, U.K., New York, 2002). The linguistic side is based on a morpho‐syntactic linguistic system (Proceedings of COLING‐2002, vol. II, Taipei, Taiwan, 2002; 1263–1267). A novel shape coding method is used to interface them, and their interaction is enhanced via an inverse filtering technique based on features that are global or of a low confidence value. A statistical context selection method is also applied to further reduce the output word lists. Copyright © 2008 John Wiley & Sons, Ltd.     

A Computer-Aided Design Toolkit For Continuous/Discrete Systems Based On Continued Fractions

This paper presents new algorithms (1) to generate complete mathematical symbolic expressions of continued fraction expansion coefficients in the s-domain, (2) to determine their numeric values and (3) to perform continued fraction inversion in the s-domain. A software package in PASCAL and LISP to implement these algorithms is developed. In addition, a simple z-domain inversion algorithm used in the computer implementation of bilinear s-z transformation is also included in the software package. These algorithms play an important role in the analysis and synthesis of complex electrical networks and control systems. Especially, the s-domain expansion and inversion algorithms have potential applications in model simplification and system order reductions. the paper also shows that the package, as such, serves as a comprehensive computer-aided analysis and design (CAD) toolkit for both continuous and discrete systems. the software is interactive and runs on computers equipped with a PASCAL or LISP compiler. It is noted that the iterative implementation of these methods using the new continued fraction algorithms saves considerable memory space and processing time. Numerical examples and computer data are given to demonstrate the development of the new algorithms and the usefulness of the software toolkit in the CAD design of continuous and discrete systems.     

基于逆模型区间优化的神经网络预测控制

针对神经网络预测控制中，在线滚动优化计算量大、算法稳定性难以保证的问题，提出一种确定黄金分割优化算法初始搜索区间的方案，即初始搜索区间的宽度与神经网络逆模型输出和上一时刻系统输入的误差成正比，二者越接近，搜索宽度就越小，从而黄金分割优化算法的在线计算量就越小；该方案有效地降低了在线滚动优化计算量，同时又使控制系统具有神经网络预测控制和神经网络逆控制的双重特性，在模型匹配稳态工况下，神经网络预测控制转化为神经网络逆控制，具有逆控制快速性的优点，而在模型失配或动态过程中，神经网络预测控制起主导作用，具有模型的宽容性和鲁棒性强的特点。采用区间套定理对该算法的收敛性给予了严格的数学证明。通过对某300MW机组仿真表明，提出的方案在控制品质和降低计算量方面均获得满意的效果。     

Adaptive inverse control of unmodeled stable SISO and MIMO linear systems

Adaptive‐signal‐processing techniques have been employed with great success in such applications as: system identification, channel equalization, statistical prediction and noise/echo cancellation. From a mathematical point of view, there is little difference between these applications and the types of operations required by control systems to control a dynamical system. This paper presents an approach to control systems called adaptive inverse control in which adaptive‐signal‐processing techniques are used throughout. Adaptive inverse control comprises three simultaneous processes. The plant is automatically modeled using adaptive system identification techniques. The dynamic response of the system is adaptively controlled using the resulting model and methods related to channel equalization. Adaptive disturbance canceling is performed using methods similar to noise canceling. The method applies directly to stable single‐input single‐output (SISO) and multi‐input multi‐output (MIMO) plants, and does not require an a priori model of the system. If the plant is unstable, it must first be stabilized using conventional feedback. This implies that at least a rudimentary model need be made if the plant is unstable. Once the plant is stabilized, adaptive inverse control may be applied to the stabilized system. Copyright © 2002 John Wiley & Sons, Ltd.     

Parallelized multilevel fast multipole algorithm for scattering by objects with anisotropic impedance surfaces

A parallelized multilevel fast multipole algorithm (MLFMA) is presented for simulating electromagnetic scattering from complex targets with anisotropic impedance surfaces. By employing both surface electric and magnetic currents as unknowns and weakly enforcing the anisotropic impedance boundary condition, a combined integral equation is formulated to generate a set of well‐conditioned linear systems to be solved by MLFMA. To further improve the iterative convergence of the linear systems, a parallel sparse approximate inverse preconditioner is constructed from the near‐field interaction of the system matrix. The MLFMA is parallelized to enable computation on a large number of processors for large‐scale problems. Several numerical examples are presented to validate the algorithm and demonstrate its accuracy, scalability, and capability in handling large complex objects with anisotropic impedance surfaces. Copyright © 2014 John Wiley & Sons, Ltd.     

Mixed norm regularized recursive total least squares for group sparse system identification

A mixed l_p,0‐regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS‐based algorithms deteriorates when both input and output are contaminated by noise (the error‐in‐variables problem). We propose an l_p,0‐RTLS algorithm to handle group sparse system identification with errors‐in‐variables. The proposed algorithm is an RLS‐like solution that utilizes l_p,0‐regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l_p,0‐regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Nonlinear reference tracking control of a gas turbine with load torque estimation

Input–output linearization‐based adaptive reference tracking control of a low‐power gas turbine model is presented in this paper. The gas turbine is described by a third‐order nonlinear input‐affine state‐space model, where the manipulable input is the fuel mass flowrate and the controlled output is the rotational speed. The stability of the one‐dimensional zero dynamics of the controlled plant is investigated via phase diagrams. The input–output linearizing feedback is extended with a load torque estimator algorithm resulting in an adaptive feedback scheme. The tuning of controller parameters is performed considering three main design goals: appropriate settling time, robustness against environmental disturbances and model parameter uncertainties, and avoiding the saturation of the actuator. Simulations show that the closed‐loop system is robust with respect to the variations in uncertain model and environ‐mental parameters and its performance satisfies the defined requirements. Copyright © 2007 John Wiley & Sons, Ltd.