Economic Model-Predictive Control for Aircraft Forced Landing: Framework and Two-Level Implementation

Abstract

This paper presents an optimization-based control framework for the autonomous forced landing of a fixed-wing Unmanned Aircraft (UA). A two-level MPC scheme is proposed to realize this framework, where an EMPC in a long piece-wise constant fashion is proposed at the high-level while a short fixedhorizon linear time-varying MPC at the low-level responds to fast dynamics of UA and tracks the optimal path provided by the high-level controller, alleviating computational burden compared to the high frequency single-layer MPC scheme. Comparing with single EMPC setup with high sampling frequency, this hierarchical EMPC controller can significantly reduce the computational complexity and make it feasible to be implemented in realtime. In addition, it also responds to disturbances (e.g. wind) and aircraft fast dynamics in a timely manner. The recursive feasibility and stability of the high and low-level MPC schemes are established. The performance of the proposed EMPC forced landing function is illustrated by simulation case studies on both Aerosonde and Skywalker X8, compared favorably with competing schemes.

Model Predictive Control With Wind Preview for Aircraft Forced Landing

“…In this paper, we adopt a partial cylinder with fixed radius shown in Fig. 1 as the terminal target region, where the aircraft will execute the final landing procedure, consume extra altitude and achieve the safe landing [2], [12], [29]. This terminal region can be formulated as the following set…”

“…Therefore, this paper focuses on the design of an optimization-based controller which maximizes the terminal height when it reaches the selected landing site by exploiting wind preview information. Rather than online planning a path using EMPC by ignoring wind condition as in our previous work [12], the favorable wind condition is considered as an opportunity in this goal-oriented operation. More specifically, the preview information of wind condition is considered in the receding horizon optimization framework to take the opportunity to maximize the terminal altitude of the aircraft system as it glides to a selected landing site.…”

“…Motivated by this goal-oriented operation, we may adopt an Economic MPC (EMPC) idea that provides an elegant way to undertake the economic optimization problem in real-time (see [7]- [11] and references therein). To mitigate real-time implementation issues of nonlinear optimization involved in EMPC, our previous work proposes a two-level control strategy for the forced landing task [12]. In the high-level path planner, a piecewise constant input EMPC approach is employed for a nonlinear model to generate a reference path for the low-level controller, which is not only able to alleviate the computational burden, but also increase the prediction time window of the forced landing process.…”

“…To the best of our knowledge, currently there is no work on exploiting real-time wind preview information for the benefit of maximizing the aircraft landing altitude and analyzing its impact on the aircraft forced landing behaviour, in particular how to take advantage of winds to achieve better objectives. Moreover, to take the advantages of real-time implementable two-level framework [12], this paper proposes an optimal controller design for the forced landing task under wind conditions. In summary, our main contributions include: (i) A DP-EMPC approach is proposed to take the opportunity raised by wind conditions to optimize high-level economic criteria, i.e., to maximize the altitude of an unmanned aircraft system as it reaches the target area of a forced landing site.…”

Efficiency-Oriented MPC: Using Nested Structure to Realize Optimal Operation and Control

“…Recently, researchers proposed economic model predictive control (EMPC), which combines the DRTO layer and the control layer into one framework to avoid the time-scale separation problem [19,20]; in this way, EMPC focuses on how to address economic optimization (the operation performance in industrial processes) directly in real time [21]. Unlike the quadratic objective function used in tracking MPC, EMPC incorporates a general cost function that directly accounts for process economics such as process yield or production rate, and this process economics-oriented general objective function gives EMPC the ability to generate optimal dynamic operation performance [22].…”