This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.
A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.
The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.
The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.
This work is supported by National Natural Science Foundation of China under Grant 61720106010, Grant 61836001.
Cao, Q., Xia, Y., Sun, Z. and Dai, L. (2022), "Fusion event-triggered model predictive control based on shrinking prediction horizon", Assembly Automation, Vol. 42 No. 6, pp. 721-729. https://doi.org/10.1108/AA-02-2022-0022
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