RBF-ARX model-based MPC approach to inverted pendulum: An event-triggered mechanism

Binbin Tian and Hui Peng

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: The event-triggered predictive control based on RBF-ARX model (state-independent autoregressive with exogenous inputs constructed by radial basis function network) is designed in this paper, which is aiming at the issue of accumulating computational burden associated with solving the optimization problems at each sampling time. To alleviate the computational burden while the performance of model predictive control (MPC) cannot be simultaneously sacrificed, from the perspective of model building, the data-driven RBF-ARX model presented by a new state space form is combined into the event-triggered model predictive control (ETMPC) method instead of the mechanistic model. Therefore, we attempt to expand the results of ETMPC to the complex industrial processes rather than the restricted model described by mechanism in terms of proposing a new event-triggering condition (ETC) with a data-driven model. Additionally, the stability analysis of RBF-ARX model-based ETMPC is demonstrated with a focus on the boundedness of the model’s output. The simulation results presented in this paper serve to illustrate the effectiveness of the proposed method by acting on an inverted pendulum system in this paper, which can be showed that the computational burden is significantly reduced, and the predictive control performance of ETMPC is as well as the MPC roughly.

Keywords: Event-triggered strategy; RBF-ARX model; Model predictive control; Inverted pendulum system (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077923009827
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923009827

DOI: 10.1016/j.chaos.2023.114081

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().