3 edition of Performance characteristics of an adaptive controller based on least-mean-square filters found in the catalog.
Performance characteristics of an adaptive controller based on least-mean-square filters
by National Aeronautics and Space Administration, Ames Research Center, For sale by the National Technical Information Service in Moffett Field, Calif, [Springfield, Va
Written in English
|Statement||Rajiv S. Mehtra, Shmuel J. Merhav.|
|Series||NASA technical memorandum -- 88359.|
|Contributions||Merhav, Shmuel J., Ames Research Center.|
|The Physical Object|
In the fourth chapter, adaptive lattice filters and RLS algorithms for this type of filter are treated. The chapter on frequency-domain filtering deals with FFT-based filters and also with filter-bank techniques• The chapter on adaptive filter realization is devoted to full digital as well as to CCD realizations of adaptive FIR filters based. The key innovations lie in using the identified model to generate the gradient descent used in the iterative learning control, encoding the result from the learning control in a finite impulse response filter and adapting the finite impulse response coefficients during operation using the least-mean-square update based on position, velocity.
Adaptive Systems have been used in a wide range of applications for almost four decades. Examples include adaptive equalization, adaptive noise-cancellation, and adaptive control. The design of adaptive filters/controllers is a difficult nonlinear problem for which good systematic synthesis procedures have proven a difficult : Bijan Sayyarrodsari. Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier.
Aimed at multiple-point adaptive control strategy, a distributed multi-channel adaptive control algorithm is proposed, in which coupling between channels can be compensated on each control loop. Influence of secondary path on active control is analyzed, put forward the improved least mean square algorithm to identify the secondary. Adaptive Filtering: Active Noise Control. An adaptive filter responds to changes in its parameters like its resonance frequency, input signal or transfer function that varies with time, for example. This behavior is possible since the adaptive filter coefficients vary over time and are updated automatically by an adaptive algorithm.
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Performance Characteristics of an Adaptive Controller Based on Least - Mean -Square Filters Rajiv S. Mehta, Shmuel J. Merhav, Ames Research Center, Moffett Field, California September National Aeronautics and Space Administration Ames Research Center Moffett Field, California Get this from a library.
Performance characteristics of an adaptive controller based on least-mean-square filters. [Rajiv S Mehtra; Shmuel J Merhav; Ames Research Center.]. Performance characteristics of an adaptive controller based on least-mean-square filters. By R. Mehta and S. Merhav.
Abstract. A closed-loop, adaptive-control scheme that uses a least-mean-square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme.
Author: R. Mehta and S. Merhav. The median least-mean-square (MLMS) adaptive filter alleviates the problem of degradation of performance when inputs are corrupted by impulsive noise by protecting the filter. An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square.
Filter. Based on in-depth study of adaptive filter based on the least mean square (LMS) algorithm and recursive least squares .(RLS) are applied to the adaptive filter technology to the noise, and through the simulation results prove that its performance is usually much better than using conventional methods designed to filter fixed.
Abstract— In this paper five different Least Mean Square (LMS)-based adaptive filter algorithms are presented. Then, a modified version of the LMS algorithm is proposed which combines the step size adaptation mechanisms of the "pure" LMS algorithm and that of the Normalized Least Mean Square (NLMS) algorithm.
Characteristics of adaptive filters: They can automatically adapt (self-optimize) in the face of changing environments and changing system requirements They can be trained to perform specific filtering and decision-making tasks according to some updating equations (training rules).
Adaptive Filter Features Adaptive ﬁlters are composed of three basic modules: Filtering strucure Determines the output of the ﬁlter given its input samples Its weights are periodically adjusted by the adaptive algorithm Can be linear or nonlinear, depending on the application Linear ﬁlters can be FIR or IIR Performance criterion Deﬁned according to application and mathematical tractability.
controller adaptive?”. A short possible answer was oﬀered by G. Zames during a presentation made at the 35th Conference in Decision and Control,Kobe, Dec. ”a non-adaptive controller is based solely on a-priori information whereas an adaptive controller is based.
Abstract. The least mean-square (LMS) is a search algorithm in which a simplification of the gradient vector computation is made possible by appropriately modifying the objective function –.The LMS algorithm, as well as others related to it, is widely used in various applications of adaptive filtering due to its computational simplicity –.
The area of adaptive control has grown to be one of the richest in terms of algorithms, design techniques, analytical tools, and modiﬂcations.
Several books and research monographs already exist on the topics of parameter estimation and adaptive control. Despite this rich literature, the ﬂeld of adaptive control may easily appear.
An adaptive filter is a digital filter that has self-adjusting characteristics. It is capable of adjusting its filter coefficients automatically to adapt the input signal via an adaptive algorithm. Adaptive filters play an important role in modern digital signal processing (DSP) products in areas such as telephone echo cancellation, noise cancellation, equalization of communications channels.
Characteristics of the LMS Adaptive Filter of adaptation and performance of adaptive systems. In general, faster adaptation leads to more noisy adaptive It can also be used as the adaptive portion of certain learning control TLU ) for use in adaptive logic and pattern-recognition systems.
systems [. Adaptive filters - Adaptive filters, on the other hand, have the ability to adjust their impulse response to filter out the correlated signal in the input. They require little or no a priori knowledge of the signal and noise characteristics.
(If the signal is narrowband and noise broadband, which is usually the case, or. A landmark text in LMS filter technology–– from the field’s leading authorities In the field of electrical engineering and signal processing, few algorithms have proven as adaptable as the least-mean-square (LMS) algorithm.
Devised by Bernard Widrow and M. Hoff, this simple yet effective algorithm now represents the cornerstone for the design of adaptive transversal (tapped-delay-line. A self-contained introduction to adaptive inverse controlNow featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of Adaptive Inverse Control takes a novel approach that is not available in any other n by two pioneers in the field, Adaptive Inverse Control presents methods of adaptive signal processing that are.
The linear optimum discrete time filtering technique namely Wiener filtering-based control algorithm is developed for the extraction of reference supply currents from distorted load currents.
The performance of Wiener filter is compared with least mean square (LMS) adaptive filter-based control algorithm. Many adaptive filter structures and adaptation algorithms have been developed for different applications.
This chapter presents the most widely used adaptive filters based on the FIR filter with the least-mean -square (LMS) algorithm. These adaptive filters are relatively simple to design and implement. Introduction to Adaptive Filters Scott C. Douglas University of Utah What is an Adaptive Filter.
The Adaptive Filtering Problem Filter Structures The Task of an Adaptive Filter Applications of Adaptive Filters SystemIdentiﬁcation InverseModeling LinearPrediction Feedforward Control Gradient-Based Adaptive Algorithms. But it is possible to chart its behavior in a stationary and nonstationary environment.
Least-Mean-Square Adaptive Filters puts these defining characteristics into sharp focus, and–more than any other source–brings you up to speed on everything that the LMS filter has to cturer: Wiley-Interscience.come using adaptive control to modify an active con-trol structure, function, or form to improve perfor-mance based on a new understanding of the system dynamics or properties (See Table 1).
Adaptive con-trol makes possible the full performance benefits of an active isolation system. The application of adaptive control to vibration sup.Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters.