Model predictive control algorithms for active vibration control: a study on timing, performance and implementation properties

Takács, Gergely; Rohal’-Ilkiv, Boris

doi:10.1177/1077546313479993

Cited by 26 publications

(22 citation statements)

References 27 publications

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“…In other words, the theoretical approach behind Mobayen, 2014;Cortes et al, 2008;Liu et al, 2016). Such methods aim to provide on-line optimization and feedback correction according to the feedback prediction control models Takacs et al, 2014;Mobayen, 2015). These can solve the problem of parameters uncertainty in the analysis of the high-dimensional complex model, and hence enhance the robustness and anti-interference of the system to random disturbances (Mobayen, 2016;Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

State feedback predictive control for nonlinear hydro-turbine governing system

Yi¹,

Zhiwang²,

Chen

et al. 2017

Journal of Vibration and Control

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The present work introduces a novel predictive control strategy for the analysis of the dynamic performance of hydro-turbine governing systems based on fuzzy logic. Firstly, a six-dimensional non-linear dynamic model of the system is defined. The defined model is applied to a realistic case-study, aiming to investigate the dynamic behavior of the system. In order to deal effectively with the non-linearity of the system under study, the T-S fuzzy approach is adopted. The results demonstrated through the use of the discrete Lyapunov function and Schur complements of matrices suggest that the closed-cycle control system can achieve a global asymptotic stability state. The second part focuses on the quantification of the impact of sudden changes in operating conditions on the overall performance. The numerical results indicate that proposed predictive control method can ensure the performance of the system to be reliable and robust to external inferences. In addition to this, the approach proposed has unquestionable advantages over the traditional PID and model predictive controllers with regard to non-linear systems applications.

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Section: Introductionmentioning

confidence: 99%

State feedback predictive control for nonlinear hydro-turbine governing system

Yi¹,

Zhiwang²,

Chen

et al. 2017

Journal of Vibration and Control

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“…The MPC problem (3) was formulated with control horizon of N = 50 steps which practically amounts to the prediction over 1 second. The choice for this horizon is motivated by the stability guarantees assumed in the problem formulation and allows the expected range of positions and velocities to be covered by the controller's domain of attraction, see [25]. In addition, state and input penalties were chosen as Q = I and R = 1, respectively, P N set to the solution of DARE and C ∞ designed as the maximal control invariant set-rendering the controller response such that it attenuates the beam tip vibration efficiently and yet does not behave like an overly aggressive 'bang-bang'-like controller.…”

Section: A Active Beam Testmentioning

confidence: 99%

“…Table I illustrates both of these critical aspects. By implementing the proposed algorithms, cheap entrylevel MCU like the F100x can compute the same optimal constrained MPC moves with guaranteed stability and even long horizons that would otherwise need powerful hardware [25]. Conversely, high-end microcontrollers like the F407x can execute the benchmark vibration control problem in the microsecond range, suggesting that even faster applications or better mathematical models are within the realm of possibilities.…”

Section: B Cross-platform Comparisonmentioning

confidence: 99%

“…Many have suggested explicit MPC as the ideal formulation for cheap embedded hardware [9], [24]. Former trials with EMPC for vibration attenuation evaluated memory needs and algorithm timing, albeit on powerful hardware that is suitable only for laboratory tests [5], [25]. Explicit MPC without efficiency modifications was recently applied to AVC via embedded hardware, showing the limits of hardware and software on high-end microcontrollers [26].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Embedded Model Predictive Vibration Control via Convex Lifting

Gulan

Takács

Nguyễn

et al. 2019

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents an efficient real-time implementation of embedded model predictive control, adopted in the context of active vibration control with the objective of minimizing the tip deflection of lightly damped cantilever beams. In particular, we focus on memory and time-efficient explicit solutions to the associated constrained optimal control problem that are easily implementable on low-end embedded hardware. To this end, we exploit the concept of convex lifting and show how it can be used to devise low-complexity, regionless piecewise affine controllers without any loss of optimality and performance. Efficiency of this constructive procedure is quantified via an extensive complexity analysis, evidenced by a successful practical deployment and optimal vibration control performance using a family of 32-bit ARM Cortex-M based microcontroller platforms.

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“…This feature motivates tackling the AVC using MPC. [28][29][30] Despite the limiting requirement for fast sampling which is known as the main drawback of MPC in AVC applications, it is shown in this paper that the proposed method is fast enough to overcome this problem even in high frequencies of disturbance signal. Finally, the application of MPC in nonlinear systems is mostly neglected in literature.…”

Section: Introductionmentioning

confidence: 99%

Observer-based repetitive model predictive control in active vibration suppression

Oveisi

Hosseini-Pishrobat

Nestorović

et al. 2018

Struct Control Health Monit

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In this paper, an observer-based feedback/feedforward model predictive control (MPC) algorithm is developed for addressing the active vibration control (AVC) of lightly damped structures. For this purpose, the feedback control design process is formulated in the framework of disturbance rejection control (DRC) and a repetitive MPC is adapted to guarantee the robust asymptotic stability of the closed-loop system. To this end, a recursive least squares (RLS) algorithm is engaged for online estimation of the disturbance signal, and the estimated disturbance is feed-forwarded through the control channels. The mismatched disturbance is considered as a broadband energy bounded unknown signal independent of the control input, and the internal model principle is adjusted to account for its governing dynamics. For the sake of relieving the computational burden of online optimization in MPC scheme, within the broad prediction horizons, a set of orthonormal Laguerre functions is utilized. The controller design is carried out on a reduced-order model of the experimental system in the nominal frequency range of operation. Accordingly, the system model is constructed following the frequency-domain subspace system identification method. Comprehensive experimental analyses in both time-/frequencydomain are performed to investigate the robustness of the AVC system regarding the unmodeled dynamics, parametric uncertainties, and external noises. Additionally, the spillover effect of the actuation authorities and saturation of the active elements as two common issues of AVC systems are addressed.KEYWORDS disturbance estimation, model predictive control, orthonormal basis functions, piezoelectric material, vibration attenuation | INTRODUCTIONModel predictive control (MPC) is an optimization-based method in which, the control inputs are obtained by solving a set of finite-horizon optimal control problems successively over the sampling instants. The optimal control formulation of the MPC explicitly takes into account model-based predictions of the controlled system trajectories. The obtained solutions of the optimal control problems are concatenated in a receding horizon framework, to construct the MPC feedback law. By the advances of embedded computer processors along with the enhanced optimization solvers, the MPC methods are appearing more and more in the emerging industrial applications. [1] In comparison with the other industrial control

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Model predictive control algorithms for active vibration control: a study on timing, performance and implementation properties

Cited by 26 publications

References 27 publications

State feedback predictive control for nonlinear hydro-turbine governing system

State feedback predictive control for nonlinear hydro-turbine governing system

Efficient Embedded Model Predictive Vibration Control via Convex Lifting

Observer-based repetitive model predictive control in active vibration suppression

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