1993
DOI: 10.1115/1.2899195
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A Modern Control Approach to Active Noise Control

Abstract: Active noise control systems currently in use and/or described in the research literature are typically based on adaptive signal processing theory or, equivalently, adaptive feedforward control theory. This paper presents a modern control approach to the problem of active noise cancellation in a three-dimensional space. The controller is designed based on a direct self-tuning regulator. Two forms of adaptive control, namely, pole placement and minimum variance controls are considered and compared in simulation… Show more

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Cited by 15 publications
(17 citation statements)
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“…By construction, therefore, is a stable matrix polynomial, establishing that the regulator is stabilizing. Moreover, in view of (27), is strictly proper and is proper, i.e., and is finite. It then follows from (23) that and are strictly proper, and hence the regulator is realizable.…”
Section: Linear Stabilizing and Realizable Regulatorsmentioning
confidence: 98%
See 2 more Smart Citations
“…By construction, therefore, is a stable matrix polynomial, establishing that the regulator is stabilizing. Moreover, in view of (27), is strictly proper and is proper, i.e., and is finite. It then follows from (23) that and are strictly proper, and hence the regulator is realizable.…”
Section: Linear Stabilizing and Realizable Regulatorsmentioning
confidence: 98%
“…(We note that if is not stable, also the latter parameterization requires an observer-based prestabilization, increasing the dimension of the regulator; see, e.g., [32, p. 226]. ) Theorem 2.1: Let be a stable matrix with being its characteristic polynomial, and let and be the matrix polynomials (26) Moreover, let be an arbitrary stable scalar polynomial and let and be arbitrary matrix polynomials of dimensions and , respectively, such that (27) Then the regulator (28) with (29) is stabilizing and realizable, and for this regulator (30) and (31) where is given by (17). Conversely, any stabilizing and realizable regulator (28) is equivalent to one constructed in this way.…”
Section: Linear Stabilizing and Realizable Regulatorsmentioning
confidence: 99%
See 1 more Smart Citation
“…Feedback-based controllers, such as linear quadratic Gaussian (LQG) and H ∞ optimal controllers, achieve good results for suppressing transient disturbances. 7 Recent research into adaptive feedforward controllers, including filtered-x and filtered-u leastmean-square (LMS) algorithms, has demonstrated that robust performance in persistent disturbance rejection, such as driving the residual error to zero, can be achieved with little prior knowledge of plant dynamics. [8][9][10] Snyder et al noted that the convergence of these algorithms depends on the plant dynamics and the model error.…”
Section: Introductionmentioning
confidence: 99%
“…This work can be applied in a variety of settings, for example in active noise control which was studied in [9] for scalar-input scalar-output systems using a minimum variance performance measure. The suppression of harmonic disturbances also arises in the active control of vibrations in helicopters [6], where a continuous-time model is used in the context of LQ control and Kaiman filtering.…”
Section: Robust Adaptive Nonlinear Output Regulation -An Application mentioning
confidence: 99%