Active Noise Control with Reduced-Complexity Kalman Filter

Fabry, Johannes; Liebich, Stefan; Vary, Peter; Jax, Peter

doi:10.1109/iwaenc.2018.8521391

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…In (24), 𝜙, 𝜃, 𝜓 are the Euler angles, I is the moment of inertia, and T ux , T dx are control and disturbances torques. To express (24) in the form of state-space, the following equation is used…”

Section: Model Estimation Simulationmentioning

confidence: 99%

“…where A ′ and B ′ are open-loop state-space matrices, k ′ is the control gain matrix obtained by the pole placement method, and A is the closed-loop matrix. If q = [𝜙, 𝜃, 𝜓] T , u = [T ux , T uy , T uz ] T = k ′ x, d = [T dx , T dy , T dz ] T , and the state vectors are x = [q T , qT ] T , according to Equation (24), A ′ and B ′ are defined as follows,…”

Section: Model Estimation Simulationmentioning

confidence: 99%

“…The least-squares error (LS) method has been shown to be an effective and accurate method for identifying and estimating the model [19][20][21][22]. However, for some models or operating conditions, it can be demonstrated that recursive least squares methods or the Kalman filter are more accurate and stable in estimating the model and attitude [23,24]. But LS methods were developed using iterative and recursive methods that significantly improve the LS estimation method performance [19,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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An online intelligent robust adaptive LSQR estimation method for LTI state space model

Hosseini

Navabi

Hajarian

2022

IET Control Theory & Appl

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Regarding the low accuracy and instability of common online methods for estimating dynamic models in the time domain, in the presence of uncertainty in system dynamics, sensor noise and environmental disturbances, this area is still open for further research. In this paper, a new estimation method is proposed based on a new online robust metaheuristic adaptive LSQR (ORALSQR) for simultaneous estimation of a multi input/output linear dynamic model and system state variables. This new adaptive LSQR algorithm is used to solve the output matrix equations of the least squares error problem. The presented algorithm, based on its iterative nature, searches the answer subspace by using a new meta-heuristic logic. In addition, the algorithm solving steps and the search domain size in each iteration are intelligently determined by the method. In an identification maneuver, this method estimates the state variables using an estimated model in the Kalman filter, then estimates the model online for the next iteration using the state variables. In addition the stability proof of this method is presented. Numerical results show more accuracy and robustness of this method compared to the other methods mentioned in this paper which contain LS and RLS based estimation methods.

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Section: Model Estimation Simulationmentioning

confidence: 99%

Section: Model Estimation Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An online intelligent robust adaptive LSQR estimation method for LTI state space model

Hosseini

Navabi

Hajarian

2022

IET Control Theory & Appl

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“…11 and 13] are able to reduce the total number of arithmetical operations, but with complexity still O(M 2 ). Although the hardware technology for embedded systems is quite powerful, a KF with reduced complexity is important to deal with real-time systems that require high sampling rates and low latencies [40]. In the case of active noise control (ANC) [40] and multi-channel linear-prediction (MCLP) for blind speech dereverberation [41], the KF tends to outperform most adaptive algorithms in terms of convergence speed and robustness.…”

Section: Introductionmentioning

confidence: 99%

“…Although the hardware technology for embedded systems is quite powerful, a KF with reduced complexity is important to deal with real-time systems that require high sampling rates and low latencies [40]. In the case of active noise control (ANC) [40] and multi-channel linear-prediction (MCLP) for blind speech dereverberation [41], the KF tends to outperform most adaptive algorithms in terms of convergence speed and robustness. By enforcing a band-matrix structure for the covari-ance matrix P i in the case of [40] and block-diagonal matrix in the case of [41], the authors developed low complexity, i.e O(M ), approximations for the KF.…”

Section: Introductionmentioning

confidence: 99%

Low-complexity approximations for the Kalman filter.

Claser¹

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This dissertation is dedicated to my father João Carlos Claser (in memoriam) and to my mother Giuseppina Gattuso Claser, who always stood by my side and gave me strength to overcome my limits and to complete this thesis. ACKNOWLEDGMENTSFirst and foremost, I would like to thank my supervisor Vítor Heloiz Nascimento for his constant support, guidance, constructive criticism and for the infinite patience over these years.My thanks to the University of São Paulo for giving me the opportunity to pursue my studies.A boundless thank you to my mother Giuseppina Gattuso Claser and my sister Carla Claser, who supported me throughout these tough years, for being strict when needed, for nursing me with love and encouraging me to complete my dissertation.A special thank you to my girlfriend Sirlene Maria Hillesheim for being patient and understanding during this long and arduous journey.

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