Explicit non-linear model predictive control for autonomous helicopters
journal contributionposted on 2012-12-12, 14:52 authored by Cunjia Liu, Wen-Hua Chen, J.D. Andrews
Trajectory tracking is a basic function required for autonomous helicopters, but it also poses challenges to control design due to the complexity of helicopter dynamics. This article introduces an explicit model predictive control (MPC) to solve this problem, which inherits the advantages of non-linear MPC but eliminates time-consuming online optimization. The explicit solution to the non-linear MPC problem is derived using Taylor expansion and exploiting the helicopter model. With the explicit MPC solution, the control signals can be calculated instantaneously to respond to the fast dynamics of helicopters and suppress disturbances immediately. On the other hand, the online optimization process can be removed from the MPC framework, which can accelerate the software development and simplify onboard hardware. Due to these advantages of the proposed method, the overall control framework has a low complexity and high reliability, and it is easy to deploy on small-scale helicopters. The proposed explicit non-linear MPC has been successfully validated in simulations and in actual flight tests using a Trex-250 small-scale helicopter.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
CitationLIU, C., CHEN, W-H. and ANDREWS, J.D., 2012. Explicit non-linear model predictive control for autonomous helicopters. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226 (9), pp. 1171 - 1182
PublisherSage Publications © Institution of Mechanical Engineers (IMechE).
- AM (Accepted Manuscript)
NotesThis article was published in Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering [© Sage and IMechE]. The definitive version is available from: http://dx.doi.org/10.1177/0954410011418585