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     

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     

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.     

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.     

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.     

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.     

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.     

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,...     

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%。     

A globally convergent algorithm for the optimal constant output feedback problem

A dual version of the Levine-Athans algorithm for solving the optimal constant output feedback gain for continuous-time systems is studied. Global convergence of the procedure to a stationary point of the loss function is proved. A global convergence property of the Levine-Athans algorithm is also obtained.     

Low-gain integral control of continuous-time linear systems subject to input and output nonlinearities

Continuous-time low-gain integral control strategies are presented for tracking of constant reference signals for finite-dimensional, continuous-time, asymptotically stable, single-input single-output, linear systems subject to a globally Lipschitz and non-decreasing input nonlinearity and a locally Lipschitz, non-decreasing and affinely sector-bounded output nonlinearity. Both non-adaptive (but possibly time varying) and adaptive integrator gains are considered. In particular, it is shown that applying error feedback using an integral controller ensures asymptotic tracking of constant reference signals, provided that (a) the steady-state gain of the linear part of the plant is positive, (b) the positive integrator gain is ultimately sufficiently small and (c) the reference value is feasible in a very natural sense. The classes of actuator and sensor nonlinearities under consideration contain standard nonlinearities important in control engineering such as saturation and deadzone.     

On the bias of the modified output error algorithm

In a recent work by Betser and Zeheb (1995), a modified adaptive output error algorithm for identification was proposed. In this paper, we prove that the solution is biased for most practical types of disturbance noise, including the white noise case. Bias can be avoided in certain cases, under the verification of a relation between the spectrum of the noise and the noise-free solution, specified in the form of a theorem     

An iterative algorithm for pole placement by output feedback

An iterative algorithm is proposed for solving the pole assignment by output feedback problem for an nth-order l-input m-output linear system. The algorithm uses a full rank feedback gain matrix and requires only linear equations of low dimension to be solved at each iteration. Application of the proposed algorithm allows all n closed-loop poles to be almost exactly assigned provided that the iterations converge so that deviations of the resulting closed-loop poles from the respective desired values become less than some user-specified tolerance, and m×t⩾n. Examples are provided to illustrate the applicability of the proposed method     

An output error algorithm for piecewise affine system identification

Within Hybrid systems, piecewise affine systems are a common class to be identified from input/output data. In this paper an improved algorithm for identifying piecewise affine systems is developed. The algorithm stems from clustering-based system identification. An affine output error algorithm is used to identify final models. The performance of the new Piecewise Affine Output Error (PWA-OE) algorithm is demonstrated using experimental data from a Radio Frequency MicroElectroMechanical Systems switch. Compared to the existing state-of-the-art, the PWA-OE algorithm generates a potential 62% improvement in model coefficient accuracy. Furthermore the PWA-OE algorithm is less sensitive to two additional input parameter selections.     

An effective algorithm of bit output management for real-time coders

Real-time video coders place significant restrictions on the computing resources, require one pass processing, and do not leave the possibility for full-fledged quality optimization. A series of approaches to the building of bit output algorithms is known, but the quality problem of encoding optimization leaves room for innovations. The authors propose a modification for one of these methods, which would make it possible to significantly improve the qualitative characteristics of real-time coders.