A multi-input multi-output adaptive feed-forward controller for vibration alleviation on a large blended wing body airliner

Turbulent atmosphere, gusts, and manoeuvres significantly excite aircraft rigid body motions and structural vibrations, which leads to reduced ride comfort and increased structural loads. In particular BWB (Blended Wing Body) aircraft configurations, while promising a significant fuel efficiency improvement compared to wing-tube configurations, exhibit severe sensitivity to gusts. In general, a flexible aircraft represents a lightly damped structure involving a large variety of uncertainties due to fuel mass variations during flight, control system nonlinearities, aerodynamic nonlinearities, and structural nonlinearities, to name just a few. Especially at the beginning of flight testing of a newly developed aircraft type, plant models generally require a lot of verification and adjustment based on obtained flight test data, before they can be used reliably for control law design. Adaptive control already is a well-established method for many active noise and vibration control problems, and thus is proposed here for application to the problem of gust load alleviation. However, safety requirements are significantly higher for gust load alleviation systems than for most noise and vibration control systems. This paper proposes a MIMO (Multi-Input Multi-Output) adaptive feed-forward controller for the alleviation of turbulence-induced rigid body motions and structural vibrations on aircraft. The major contribution to the research field of active noise and vibration control is the presentation of a detailed stability analysis of the MIMO adaptive algorithm in order to support potential certification of this method for a safety-critical application. Finally, the proposed MIMO adaptive feed-forward vibration controller is applied to a longitudinal flight dynamics model of a large flexible BWB airliner in order to verify the derived vibration controller on a challenging control problem.     

Robust active noise control in the loadmaster area of a military transport aircraft

The active noise control (ANC) method is based on the superposition of a disturbance noise field with a second anti-noise field using loudspeakers and error microphones. This method can be used to reduce the noise level inside the cabin of a propeller aircraft. However, during the design process of the ANC system, extensive measurements of transfer functions are necessary to optimize the loudspeaker and microphone positions. Sometimes, the transducer positions have to be tailored according to the optimization results to achieve a sufficient noise reduction. The purpose of this paper is to introduce a controller design method for such narrow band ANC systems. The method can be seen as an extension of common transducer placement optimization procedures. In the presented method, individual weighting parameters for the loudspeakers and microphones are used. With this procedure, the tailoring of the transducer positions is replaced by adjustment of controller parameters. Moreover, the ANC system will be robust because of the fact that the uncertainties are considered during the optimization of the controller parameters. The paper describes the necessary theoretic background for the method and demonstrates the efficiency in an acoustical mock-up of a military transport aircraft.     

考虑模型不确定性和时延的静止无功补偿器自适应滑膜控制器设计

静止无功补偿器(static var compensator,SVC)不仅可以为电力系统提供无功支撑、稳定电压,其附加控制还可以有效提高系统暂态稳定性,但SVC模型参数的不确定性以及广域测量信号时延等外部干扰给附加控制器的设计带来很大的难度.提出了一种基于自适应滑模变结构理论的SVC鲁棒控制器设计方法,所设计控制器能有效提高系统暂态稳定性,并且其对于模型不确定性以及时延有较好的鲁棒性.首先根据区域惯量中心的运动方程建立了包含SVC的电力系统模型;然后将滑模变结构理论应用于电力系统模型中,求得SVC附加控制律,并通过自适应律优化控制器参数;最后通过四机两区域系统以及IEEE9节点系统对SVC控制器效果进行了仿真验证.结果表明,SVC自适应滑模控制器可以有效提升系统暂态稳定性,并且其性能优于传统的线性控制方法.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

Power oscillation suppression by robust SMES in power system with large wind power penetration

The large penetration of wind farm into interconnected power systems may cause the severe problem of tie-line power oscillations. To suppress power oscillations, the superconducting magnetic energy storage (SMES) which is able to control active and reactive powers simultaneously, can be applied. On the other hand, several generating and loading conditions, variation of system parameters, etc., cause uncertainties in the system. The SMES controller designed without considering system uncertainties may fail to suppress power oscillations. To enhance the robustness of SMES controller against system uncertainties, this paper proposes a robust control design of SMES by taking system uncertainties into account. The inverse additive perturbation is applied to represent the unstructured system uncertainties and included in power system modeling. The configuration of active and reactive power controllers is the first-order lead–lag compensator with single input feedback. To tune the controller parameters, the optimization problem is formulated based on the enhancement of robust stability margin. The particle swarm optimization is used to solve the problem and achieve the controller parameters. Simulation studies in the six-area interconnected power system with wind farms confirm the robustness of the proposed SMES under various operating conditions.     

The design of synthesized structural acoustic sensors for active control of interior noise with experimental validation

In this paper, the feasibility of using synthesized structural acoustic sensors (SSAS) for active noise control inside irregularly shaped enclosures is investigated. A SSAS consists of a cluster of inter-connected discrete PVDF elements, located on the surface of a vibrating structure enclosing a sound field. An optimal design ensures the sensor output to be directly related to the acoustical potential energy inside the enclosure. Hence, synthesized structural acoustic sensors can provide error signals for an active noise control system, and the use of microphones inside the enclosure can be avoided. A cylindrical shell with a floor partition, which can be used to model an aircraft cabin, is used as a test case. PZT actuators are used as control actuators. Both SISO (single input and single output) and MIMO (multi-input and multi-output) control systems are optimally designed using Genetic Algorithms and implemented with a Filtered-X Feedforward LMS (least-mean-square) controller. Their control performances are evaluated with different types of disturbances. To show the effectiveness of the optimal design approach, some non-optimal control systems are also tested and compared with the optimal one. It is shown that with optimally designed SSAS, an active structural acoustic control system can effectively reduce noise inside the enclosures without using any acoustic transducers.     

40Gbit/sOTDM系统中二阶偏振模色散自适应补偿技术研究

报导了一个40 Gbit/s OTDM系统中二阶偏振模色散(PMD)自适应补偿系统,此实验系统基于偏振度的反馈控制方法实现了二阶偏振模色散自动补偿.在中心波长1560.5 nm处,补偿后的DGD和二阶PMD效应改善明显.采用粒子群优化算法作为偏振模色散自适应补偿的搜索算法,补偿时间30 ms左右.     

Two-stage adaptive PMD compensation in 40-Gb/s OTDM optical communication system

An experiment of two-stage adaptive compensation for polarization mode dispersion (PMD) in a 40-Gb/s optical time-division multiplexed communication system is reported. The PMD monitoring technique based on degree of polarization was adopted. The particle swarm optimization (PSO) algorithm was introduced in adaptive PMD compensation. The comparison was made to estimate the effectiveness between PSO algorithms with global neighborhood structure (GPSO) and with local neighborhood structure (LPSO).The LPSO algorithm is shown to be more effective to search global optimum for PMD compensation than GPSO algorithm. The two-stage PMD compensator is shown to be effective for both first- and second order PMD, and the compensator is shown to be bit rate independent. The optimum searching time is within one hundred milliseconds.     

A comparative study between LMS and PSO algorithms on the optical channel estimation for radio over fiber systems

In this paper the particle swarm optimization (PSO) and least mean square (LMS) algorithms are comparatively studied to estimate the optical communication channel parameters for radio over fiber systems. It is observed that especially in low noise one tap optical channels, the convergence of LMS algorithm is approximately same with PSO algorithm. On the other hand, as a communication medium, selecting high noisy fiber optical channels or free space optical channels; PSO reaches better mean square error values. The computational complexity which is one of the most important features for optimization algorithms has also been taken into account.     

Robust chaos synchronization using input-to-state stable control

In this paper, we propose a new input-to-state stable (ISS) synchronization method for a general class of chaotic systems with disturbances. Based on Lyapunov theory and linear matrix inequality (LMI) approach, for the first time, the ISS synchronization controller is presented not only to guarantee the asymptotic synchronization but also to achieve the bounded synchronization error for any bounded disturbance. The proposed controller can be obtained by solving a convex optimization problem represented by the LMI. Simulation studies are presented to demonstrate the effectiveness of the proposed ISS synchronization scheme.     

GA-based optimization of a MIMO ANC system considering coupling of secondary sources in a telephone kiosk

In this paper a multi-input, multi-output (MIMO) active noise control system with the aim of global reduction of broadband noise in a telephone kiosk is addressed. The model selected for this optimization problem is the acoustic environment of an enclosure taking into account the effect of coupling of secondary sources used for control purpose. This optimization involves finding the best locations for loudspeakers and microphones inside the enclosure as well as optimizing the control signals considering secondary source coupling.Previous results show that in order to be able to reduce acoustic noise globally inside the enclosure, the frequency range of 50-300 Hz must be selected for control purpose. The mean of acoustic potential energy of the enclosure, when excited in this frequency range, is adopted as a performance measure. This performance index is penalized with the power of the signal required to excite secondary loudspeakers, in order to avoid placements that may need high voltage power amplifier for a desired performance. To find the solution of this problem, i.e. the global minimum of the performance index, several genetic algorithms are proposed and compared. In order to attain the best achievable performance in reaching the global minimum, the parameters of these genetic algorithms are tuned, and used for optimization purpose. Numerical simulations of the acoustical potential energy as well as the sound pressure at different heights of the kiosk, when active noise control (ANC) system operates, confirm the optimality of the locations proposed by the genetic algorithm.     

粒子群优化算法在自适应偏振模色散补偿中的性能研究

反馈控制算法是偏振模色散的自适应补偿器的关键组成部分,将粒子群优化算法(PSO)引入到偏振模色散自适应补偿系统中。该算法的优点是具有快速收敛到全局最佳值的能力、避免搜索陷入局部极值的能力、抗噪声能力和多自由度控制能力。理论上分析了粒子群优化算法的两个分类———全局邻居结构粒子群优化(GPSO)和局部邻居结构粒子群优化(LPSO)在搜索全局最佳值方面的能力优劣,给出了局部邻居结构粒子群优化算法成功率达100%的三种邻居拓扑结构。实验表明:在补偿一阶偏振模色散时,全局邻居结构和局部邻居结构搜索全局最佳的成功率都能满足要求,全局邻居结构算法收敛速度快。而在补偿二阶偏振模色散时,全局邻居结构成功率降低,而局部邻居结构仍可以满足要求。     

Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods

In this paper, the downlink of cell-free massive multiple-input multiple-output (MIMO) with zero-forcing processing is considered. To maximize the system energy efficiency (EE), we design power allocation algorithms taking into account imperfect channel state information, hardware, and backhaul power consumption. The total EE optimization problem is nonconvex, which traditionally is solved by the successive convex approximation framework which involves second order cone programs (SOCPs). As such methods have high complexity, the run time is extremely long, especially in large-scale systems with thousands of access points (APs) and users. To overcome this problem, in this paper, we propose to apply two computationally efficient methods, namely proximal gradient (PG) method and accelerated proximal gradient (APG) method to solve the considered problem. Numerical results show that, compared to the conventional SOCPs approximation methods, our proposed methods achieve the same performance while the run time is much smaller.     

基于粒子群优化的自适应模糊制冷控制算法

为解决空间斯特林制冷机和探测器热负载不确定及存在变化的问题,提出了自适应模糊PID制冷控制。在空间环境中使用的斯特林制冷机参数会随着时间的变化而发生改变,探测器负载也会随着工作模式和工作时间的变化而变化,整个制冷系统涉及的变量多,参数非线性。采用传统的控制方法,在固定的单一条件、环境下得到的控制参数,环境和负载发生变化后容易性能变差甚至不稳定,控制精度和稳定性不能满足使用要求。设计了一种自适应斯特林制冷机控制器,通过综合自适应模糊PID控制的方法,采用粒子群优化算法调整控制参数以减小代价函数。通过仿真和试验验证算法的有效性和鲁棒性。     

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi--Sugeno (T--S) fuzzy model is employed to represent the chaotic system dynamics. To acquire the good performance and produce less conservative analysis result, a new parameter-dependent Lyapunov--Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.     

An effective PSO-based power allocation for target tracking in MIMO radar with widely separated antennas

In MIMO radar with widely separated antennas, the antennas are spaced far from each other and the target is seen from different angles. In this type of radars, each receiver collects all transmit signals and transmits them to the central processor unit. Power allocation is an important part of military operations. Therefore, it is a primary factor that requires to be taken into account in the designing of target tracking problems in MIMO radar. In fact, the power allocation finds an optimum strategy to allot power to transmit antennas with the goal of minimizing the target tracking errors under specified transmit power constraints. In this paper, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. For this purpose, first, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is computed. This is applied as a power allocation problem objective function. Because a complex Gaussian model is considered for target radar cross-section (RCS), this function becomes complicated. Due to the nonlinearity of this objective function, the proposed power allocation problem is nonconvex. Therefore, a particle swarm optimization (PSO) -based power allocation algorithm is proposed to solve it. In simulation experiments, the performance of the proposed algorithm in different conditions such as a different number of antennas and antenna geometry configurations is evaluated. Results prove the validity of the proposed algorithm.     

Application of multi-phase particle swarm optimization technique to inverse radiation problem

The multi-phase particle swarm optimization (MPPSO) technique is applied to the inverse radiation problem in the present paper. The directional radiative intensities are served as the measurement data to estimate the radiative source term, optical thickness, scattering albedo, and phase function in one-dimensional semitransparent plane-parallel media by the inverse simulation. To check the performance and accuracy in retrieval, a comparison is presented between three PSO methods, i.e. the MPPSO, the standard PSO, and the Stochastic PSO. The results confirm the potential of the proposed approach MPPSO and show its effectiveness and superiority over the other two PSO algorithms. Furthermore, the effects of swarm size, searching space, phase change frequency, and velocity-reinitializing frequency on the convergence velocity and computational accuracy of MPPSO are also investigated.     

一类参数不确定混沌系统的延迟同步

针对一类混沌系统，研究了参数未知的混沌系统的延迟同步.基于Lyapunov稳定性定理，给出了延迟同步控制器和参数自适应律的解析表达式.该方法简单、适用范围广.以新混沌系统为例，数值模拟说明了该方法的有效性和可行性.通过研究有界噪声作用下该系统的控制效果，表明了该方法具有较强的鲁棒性和抗干扰能力. 关键词： 延迟同步 Lyapunov稳定性定理 新混沌系统 有界噪声     

Reduction of electronic delay in active noise control systems--a multirate signal processing approach

Electronic delay has been a critical problem in active noise control (ANC) systems. This is true whether a feedforward structure or a feedback structure is adopted. In particular, excessive delays would create a causality problem in a feedforward ANC system of a finite-length duct. This paper suggests a multirate signal-processing approach for minimizing the electronic delay in the control loop. In this approach, digital controllers are required in decimation and interpolation of discrete-time signals. The computation efficiency is further enhanced by a polyphase method, where the phases of low-pass finite impulse response (FIR) filters must be carefully designed to avoid unnecessary delays. Frequency domain optimization procedures based on H1, H2, and Hinfinity norms, respectively, are utilized in the FIR filter design. The proposed method was implemented by using a floating-point digital signal processor. Experimental results showed that the multirate approach remains effective for suppressing a broadband (200-600 Hz) noise in a duct with a minimum upstream measurement microphone placement of 20 cm.     

Measurement of thicknesses and optical properties of thin films from Surface Plasmon Resonance (SPR)

The inverse problem for thin film properties is a challenge for nanotechnology engineering. We propose the application of a simple and intuitive numerical scheme: a basic particle swarm optimization (PSO) method, to solve the inverse problem from surface plasmon resonance (SPR) experimental results. The purpose is to retrieve unknown parameters from the measurement of the fall in reflectivity due to the excitation of a surface plasmon polariton at a metal/dielectric interface. In this case, the PSO results reveal the possibility of fully exploring the measurement results of experimental angular exploration of the reflectivity by multilayers.