Stabilization via dynamic output feedback for systems with output nonlinearities

The problem of asmptotically stabilizing a class of systems by means of continuous output feedback is considered. These systems are characterized by nonlinear terms, depending only on the ouputs. It is shown that for these systems stabilization via continuous state-feedback plus stabilization via output injection imply stabilization via continuous dynamic output-feedback. This generalizes a well-knwon result for linear systems.     

BFO optimized RLS algorithm for power system harmonics estimation

In this paper a new estimation approach combining both Recursive Least Square (RLS) and Bacterial Foraging Optimization (BFO) is developed for accurate estimation of harmonics in distorted power system signals. The proposed RLS–BFO hybrid technique has been employed for estimating the fundamental as well as harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive by using RLS that sequentially updates the unknown parameters of the signal. Simulation and experimental studies are included justifying the improvement in performance of this new estimation algorithm.     

Finite-dimensional nonlinear output feedback dynamic games andbounds for sector nonlinearities

In general, nonlinear output feedback dynamic games are infinite dimensional. The paper treats a class of minimax games when the nonlinearities enter the dynamics of the unobservable states. An information state approach is introduced to recast these games as one of full information in infinite dimensions. Explicit solutions of the first-order partial differential information state equation are derived in terms of a finite-number of sufficient statistics. When the nonlinearities are sector bounded, suboptimal finite-dimensional strategies are derived     

An algorithm for RLS identification parameters that vary quicklywith time

An alternative to the standard recursive least-squares (RLS) algorithm for fixed-order systems with exponential data weighting is presented. The approach uses Givens orthogonal transformations to update the Cholesky factor of the information matrix without ever needing to form it. The resulting algorithm gives higher-precision control and is less sensitive to ill-conditioning when compared to other reported approaches. It is demonstrated by an example that ill-conditioned problems with parameters that vary quickly can be modified to stabilize erratic parameter fluctuations     

Using a fast RLS adaptive algorithm for efficient speech processing

In this paper, a new method is presented that offers efficient computation of Linear Prediction Coefficients (LPC) via a new Recursive Least Squares (RLS) adaptive filtering algorithm. This method can be successfully used in speech coding and processing. The introduced algorithm is numerically robust, fast, parallelizable and has particularly good tracking properties. By means of this scheme, Linear Prediction Coefficients are obtained that offer an improvement in the reconstruction of the speech signal before coding, as compared to the signal obtained by various classical algorithm. An analogous improvement is observed in speech coding experiments too, while a subjective test confirms the improvement of the quality of synthesized speech. The overall processing time of the proposed method of speech coding is a bit greater, but comparable to the time the classical methods need.     

一种具有快速跟踪能力的改进RLS算法研究

为了改善固定遗忘因子递推最小二乘（RLS）算法在时变系统中的跟踪性能,提出一种改进的RLS算法。改进的可变遗忘因子RLS算法,不仅克服了固定遗忘因子RLS算法中跟踪速度和参数失调的矛盾,而且避免了当参数估值趋于参数真值时,卡尔曼增益趋于零,RLS算法失去对时变系统的跟踪能力的问题。最后,在MATLAB仿真平台下,对改进的RLS算法性能进行仿真验证。仿真结果表明,改进的算法能够获得快速的跟踪能力,也具有较快的收敛速度和较小的稳态误差。     

基于遗忘因子递推最小二乘的无人机在线磁补偿技术研究

针对无人机飞行过程中,由于载体非平稳磁场干扰造成磁测航向角不准确的问题,提出一种结合遗忘滤波的递推最小二乘在线磁补偿方法.在分析了软、硬磁干扰机理和建立磁干扰模型的基础上,对常规递推最小二乘法加入遗忘因子修正,从而抑制磁场变化对磁补偿参数估计效果的影响.仿真和试验结果表明,在磁干扰较强且干扰磁场存在突变的情况下,此算法可以将磁航向角RMSE减小至3.8220°.该方法计算量较小,能够有效适应机载条件下的变化磁场补偿,并达到较高的航向角精度要求.     

Fixed-architecture controller synthesis for systems with input–output time-varying nonlinearities

In this paper we develop a fixed-architecture controller analysis and synthesis framework that addresses the problem of multivariable linear time-invariant systems subject to plant input and plant output time-varying nonlinearities while accounting for robust stability and robust performance over the allowable class of nonlinearities. The proposed framework is based on the classical Luré problem and the related Aizerman conjecture concerning the stability of a feedback loop involving a sector-bounded nonlinearity. Specifically, we extend the classical notions of absolute stability theory to guarantee closed-loop stability of multivariable systems in the presence of input nonlinearities. In order to capture closed-loop system performance we also consider the minimization of a quadratic performance criterion over the allowable class of input nonlinearities. Our approach is directly applicable to systems with saturating actuators and provides full and reduced-order dynamic compensators with a guaranteed domain of attraction. The principal result is a set of constructive sufficient conditions for absolute stabilization characterized via a coupled system of algebraic Riccati and Lyapunov equations. The effectiveness of design approach is illustrated by several numerical examples. © 1997 John Wiley & Sons, Ltd.     

An FPGA-based parallel architecture for on-line parameter estimation using the RLS identification algorithm

A parallel architecture for an on-line implementation of the recursive least squares (RLS) identification algorithm on a field programmable gate array (FPGA) is presented. The main shortcoming of this algorithm for on-line applications is its computational complexity. The matrix computation to update error covariance consumes most of the time. To improve the processing speed of the RLS architecture, a multi-stage matrix multiplication (MMM) algorithm was developed. In addition, a trace technique was used to reduce the computational burden on the proposed architecture. High throughput was achieved by employing a pipelined design. The scope of the architecture was explored by estimating the parameters of a servo position control system. No vendor dependent modules were used in this design. The RLS algorithm was mapped to a Xilinx FPGA Virtex-5 device. The entire architecture operates at a maximum frequency of 339.156 MHz. Compared to earlier work, the hardware utilization was substantially reduced. An application-specific integrated circuit (ASIC) design was implemented in 180 nm technology with the Cadence RTL compiler.     

Iterative algorithm for parameter identification of Hammersteinsystems with two-segment nonlinearities

This paper deals with parameter identification of Hammerstein systems having two-segment polynomial nonlinearities. The application of a simple decomposition technique and using switching sequences provide a special form of a Hammerstein model that is linear-in-parameters. This model is used in an iterative algorithm, enabling simultaneous estimation of all of the model parameters. To demonstrate the feasibility of the identification method, more illustrative examples are included     

基于RLS的改进型盲多用户检测算法

针对传统的关于可变约束MOE盲多用户检测算法需直接进行特征值分解计算和不能实现二次约束限制的缺点,提出了一种基于RLS的改进型MOE盲多用户检测算法.该算法避免了矩阵求逆和大量的特征值分解运算,计算复杂度大大降低.仿真结果表明,该算法的误码率性能和收敛性均优于直接利用特征值分解的传统算法,且程序运行时间大大缩小.     

Global stabilisation via time-varying output feedback for the systems with inherent nonlinearities and coarse disturbance

ABSTRACT

This paper is devoted to the global output-feedback stabilisation for a class of nonlinear systems with inherent nonlinearities and coarse disturbance. Remarkably, the disturbance may be non-periodic, non-differentiable and/or of unknown magnitude, and hence some critical requirements on the disturbance in the existing related literature are successfully removed. Recognising that the strategies of disturbance compensation, such as internal model principle, sliding mode control method, learning method and active disturbance rejection control method, are quite hard or even impossible to apply, a time-varying design scheme is developed in the paper. Moreover, the serious unknowns and time-variations reflected in the growth rate of the system are also dealt with by the proposed scheme. By designing a time-varying output-feedback controller, all the signals of the resulting closed-loop system are globally bounded, and the original system state and the observer state ultimately converge to zero.     

Active noise control using the filtered-X RLS algorithm with sequential updates

In this paper, we develop a filtered-X recursive least square （FXRLS） algorithm with sequential updates. Although the standard FXRLS algorithm demonstrates faster convergence speed over the standard filtered-X least mean square （FXLMS） algorithm, its high computational cost hinders its applications in active noise control. The proposed method compromises its performance by using partitioned parallel sub-filters each with a reduced filter in order to achieve reduction of computational complexity. The FXRLS algorithm with sequential updates may have a potential application for nonlinear active noise control （ANC） to achieve an improved performance over the standard FXLMS algorithm.     

Adaptive output feedback stabilization for a class of nonlinear systems with inherent nonlinearities and uncertainties

This paper investigates the problem of adaptive stabilization by output feedback for a class of uncertain nonlinear systems. The distinguishing feature of such a class of systems is the presence of uncertain control coefficient and unmeasured states dependent growth with growth rate of polynomial‐of‐output multiplying an unknown constant. First, new high‐gain K‐filters with two dynamic gains are introduced, and an appropriate state observer is constructed based on the K‐filters. Then, motivated by the universal control idea, the backstepping scheme is successfully developed for the adaptive output feedback control design. By appropriate choice of the design parameters, the global stability of the closed‐loop system can be guaranteed. Finally, numerical simulations are provided to illustrate the correctness of the theoretical results. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive low-gain integral control of linear systems with input and output nonlinearities

An adaptive low-gain integral control framework is developed for tracking constant reference signals in a context of finite-dimensional, exponentially stable, single-input, single-output linear systems with positive steady-state gain and subject to locally Lipschitz, monotone input and output nonlinearities of a general nature: the input nonlinearity is required to satisfy an asymptotic growth condition (of sufficient generality to accommodate nonlinearities ranging from saturation to exponential growth) and the output nonlinearity is required to satisfy a sector constraint in those cases wherein the input nonlinearity is unbounded.     

压阻式压力传感器输出特性的补偿

在某型无人机高度传感器性能检测设备中,为解决压力的测量问题,设计了一种用于压阻式压力传感器(PRT)的信号调理电路,实现了对PRT传感器的零点输出、热零点漂移、满量程输出、热灵敏度漂移和满量程输出非线性等的高准确度校准和补偿。电路摒弃了以往传统的误差补偿形式,采用了一款新型信号调理芯片MAX1457,提出了软件补偿的方法,建立了误差校准补偿参数表,经过校准补偿传感器输出误差控制在了0.110%～0.158%。     

传感器输出特征线性化的稳健算法

利用最小一乘估计的稳健性 ,给出了传感器输出特征线性化的特征直线的稳健估计。该方法在稳健性方面明显优于最小二乘估计 ,更有效地刻划了传感性输出特征的本质     

The design of error-correcting output codes algorithm for the open-set recognition

Applied Intelligence - The Open-Set recognition is an important topic in the pattern recognition research field. Different from the close-set recognition task, in the open-set recognition problem,...     

基于RLS和EKF算法的全钒液流电池SOC估计

针对全钒液流电池的荷电状态(SOC)估计精度低、估计成本较高等问题,提出一种基于递推最小二乘算法(RLS)与扩展卡尔曼滤波算法(EKF)相结合的估计方法.该方法通过RLS算法辨识所建立的钒电池数学模型参数,通过EKF算法估计钒电池的SOC,将二者结合实现电池参数发生变化时准确估计钒电池的SOC.以5kW/ 30kWh的钒电池为对象,应用所提出的算法实现钒电池的SOC估计.结果表明,该算法可以准确估计钒电池的SOC,且可节省额外增加单片检测电池测量SOC的费用.