Analysis and implementation of active noise control strategies using Piezo and EAP actuators

Currently noise cancellation, which affects the lives of people and in the workplace is achieved through the active noise reduction. This measure is not expensive as passive or semi active measures also permits adequate air conduction in duct ventilation systems. The system control is achieved through a suitable location of the phase in the cancelling noise signal relative to the signal primary noise. Algorithms have been developed and strategies for active noise reduction and its implementation and experimental testing on duct ventilation. The actives elements used are Piezo Actuators and EAP as speakers; Individual and collective operation of the aforementioned actuators is examined. The work was evaluated as follows: Analysis of previous research on existing algorithms for active noise reduction. Study the strategies of simulation and implementation for active noise control algorithms designed.Tesi

A robust loop-shaping approach to fast and accurate nanopositioning

Peer reviewedPreprin

New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

A robust subspace based approach to feedforward control of broadband disturbances on a six-degrees-of-freedom vibration isolation set-up

The contribution of this paper is twofold. First, the paper introduces a novel hybrid vibration isolation approach which uses a combination of passive and active vibration control techniques to provide additional design freedom. The approach can be used to meet higher design requirements with respect to vibration isolation. To illustrate the feasibility of the approach, a stiff hybrid sixdegrees-of-freedom vibration isolation set-up will be presented. The objective of the set-up is to investigate if the receiver structure can be isolated from the source structure by six hybrid vibration isolation mounts, such that disturbances induced by the source structure are isolated from the receiver structure. Vibration isolation is established by minimizing signals from six acceleration sensor outputs and by steering six piezo-electric actuator inputs. Our second contribution is that a state space based fixed gain H2 controller is designed, implemented and validated. Real-time broadband feedforward control results are presented (between 0 - 1 kHz) which show that an average reduction of 8.0 dB is achieved in the error sensor outputs in real-time

Filtered-X Radial Basis Function Neural Networks for Active Noise Control

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response

Active noise hybrid time-varying control for motorcycle helmets

Recent noise at work regulations in the EU (2003) have been established to prevent noise induced hearing loss (NIHL). This imposes better performance results to traditional feedback active noise control (ANC) in motorcycle helmets, which suffer from well known limitations. Here two new ideas are applied to this problem. First, an hybrid (feedforward/feedback) linear time invariant (LTI) controller is designed for a motorcycle helmet ANC, which improves the resulting attenuation. This is achieved by adding an extra pair of microphones which measure the external noise that is then used as the feedforward input signal. In addition and to increase even more the resulting performance, the air velocity is measured in real-time and used as the parameter which schedules a linear parameter varying (LPV) feedback (FB) controller. This is combined with the previous feedforward (FF) controller, resulting in a time-varying hybrid controller. Both hybrid, LTI and LPV controllers are designed using linear matrix inequality (LMI)-based optimization. Two experiments have been carried out to measure the relation between external noise spectra and velocity: a wind tunnel test and a freeway ride experience. The resulting controllers are tested in a simulation which uses actual data obtained from the freeway experiment. The resulting attenuations in this motivating study seem promising for future controller tests to be performed in real-time, with the adequate hardware.Fil: Castañé Selga, Rosa. Universitat Technical Zu Munich; AlemaniaFil: Sanchez Peña, Ricardo Salvador. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Optimal control algorithm design for a prototype of active noise control system

High-level noise can represent a serious risk for the health, industrial operations often represent continuous exposure to noise, thus an important trouble to handle. An alternative of solution can be the use of passive mechanisms of noise reductions, nonetheless its application cannot diminish low-frequency noise. Active Noise Control (ANC) is the solution used for low-frequency noise, ANC systems work according to the superposition principle generating a secondary anti-noise signal to reduce both. Nevertheless, the generation of an anti-noise signal with same oppose characteristics of the original noise signal presupposes the utilization of special techniques such as adaptive algorithms. These algorithms involve computational costs. The present research present the optimization of a specific ANC algorithm in the step-size criteria. Delayed Filtered-x LMS (FxLMS) algorithm using an optimal step-size is evaluated in a prototype of ANC system.Tesi