Active Noise Control for Harmonic and Broadband Disturbances Using RLS-Based Model Predictive Control

Mohseni, Nima; Nguyen, Tam W.; Islam, Syed Aseem Ul; Kolmanovsky, Ilya; Bernstein, Dennis S.

doi:10.23919/acc45564.2020.9147440

Cited by 12 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key goal of the numerical examples is thus to explore manifestations of persistency, consistency, and exigency within OFMPCOI. This article extends the preliminary investigation of OFMPCOI given in [60], [61] through refinements of the control algorithm, more extensive numerical examples, and investigation of persistency, consistency, and exigency.…”

mentioning

confidence: 74%

See 1 more Smart Citation

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

et al. 2021

Self Cite

View full text Add to dashboard Cite

Among the multitude of modern control methods, model predictive control (MPC) is among the most successful [1]- [3]. As noted in "Summary," this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as LQR and LQG, and nonlinear control methods, such as feedback linearization and sliding mode control. Although MPC is computationally intensive, it is more broadly applicable than Hamilton-Jacobi-Bellman-based control, and more suitable for feedback control than the minimum principle. In many cases, the constrained optimization problem for MPC is convex, which facilitates computational efficiency [4].

show abstract

mentioning

confidence: 74%

“…Stabilization of the SISO, chain-of-integrators plants (61) and (62) using u min = −0.1 and u max = 0.1 with and without the move-size control constraint(26), where ∆u min = −0.05 and ∆u max = 0.05. (a) Discrete-time plant(61). Note that, both with and without the move-size control constraint(26), the plant is stabilized by OFMPCOI.…”

mentioning

confidence: 99%

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…where is the moment of inertia relative to c about the ˆ E axis. The longitudinal dynamics of the aircraft are given by ( 13), (14), and (15).…”

Section: Longitudinal Aircraft Dynamicsmentioning

confidence: 99%

“…This paper develops an adaptive flight control system which consists of a nominally tuned total energy control system and an adaptive controller updated by retrospective cost optimization to automate and accelerate the tuning of TECS. The retrospective cost optimization based adaptive control has previously been applied to develop an adaptive autopilot for multicopters [10], [11], adaptive flight control system [12], [13], and active noise control system [14]. The main contribution of this paper is the development of the adaptive TECS and numerical investigation of potential performance improvements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Energy Control of Longitudinal Aircraft Dynamics

Anandakumar

Bernstein

Goel

2022

AIAA SCITECH 2022 Forum

View full text Add to dashboard Cite

The Total Energy Control System (TECS) is a method to control airplane longitudinal flight dynamics by regulating energy and energy balance. This multiple input, multiple output method accounts for the highly coupled nature of aircraft dynamics and allows for control of airspeed and altitude with a proportional-integral controller. This paper reviews the heuristic TECS control law and then validates it in a MATLAB simulation. Next, an adaptive TECS is designed by augmenting the fixed-gain controllers in a nominal TECS with retrospective cost optimization-based adaptive controllers. It is shown through simulations that adaptive augmentation improves the closed-loop performance and accelerates the tuning process.

show abstract

“…Therefore, let us assume in the following that a different equalization factor, β k , is desired for each sensor in Figure 2, where the "LMS" block performs the MEFxLMS algorithm. This algorithm selection is not exclusive, so different, and generally more complex, alternative adaptive strategies could be explored, as the recently proposed Recursive Least Squares based Model Predictive Control (RLSMPC) [36] or noiserobust low complexity adaptive schemes like described in [37], [38]. The system needs to know all the pseudo-error noise signals and all the signals that result from filtering the in-phase and quadrature components of the reference signal through their corresponding estimated frequency response of the secondary paths, C jk .…”

Section: Multi-channel Ane For Single-frequency Noisementioning

confidence: 99%

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Ferrer¹,

Diego²,

Hassani

et al. 2022

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

show abstract

Active Noise Control for Harmonic and Broadband Disturbances Using RLS-Based Model Predictive Control

Cited by 12 publications

References 16 publications

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

Adaptive Energy Control of Longitudinal Aircraft Dynamics

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Contact Info

Product

Resources

About