Autonomous parafoil precision landing using convex real-time optimized guidance and control

Antoine, Leeman,; Preda, Valentin; Huertas, Irene; Bennani, Samir

doi:10.1007/s12567-021-00406-z

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…Robust nonlinear optimal control addresses a ubiquitous challenge in various safety-critical applications, such as drones, spacecraft, and robotic systems [1]- [4]. These systems are complex and difficult to model accurately due to uncertainties from measurement errors, unmodeled dynamics, or environmental disturbances.…”

Section: A Motivationmentioning

confidence: 99%

Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis

Leeman,

Sieber,

Bennani

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The proposed approach jointly optimizes a nominal nonlinear trajectory and an error feedback, requiring minimal offline design effort and offering low conservatism. This is achieved by decomposing the affine-in-the-parameter uncertain nonlinear system into a nominal nonlinear system and an uncertain linear time-varying system. Using this decomposition, we can apply established tools from system level synthesis to convexly over-bound all uncertainties in the nonlinear optimization problem. Moreover, it enables tight joint optimization of the linearization error bounds, parametric uncertainties bounds, nonlinear trajectory, and error feedback. With this novel controller parameterization, we can formulate a convex constraint to ensure robust performance guarantees for the nonlinear system. The presented method is relevant for numerous applications related to trajectory optimization, e.g., in robotics and aerospace engineering. We demonstrate the performance of the approach and its low conservatism through the simulation example of a post-capture satellite stabilization. I. INTRODUCTION A. MotivationRobust nonlinear optimal control addresses a ubiquitous challenge in various safety-critical applications, such as drones, spacecraft, and robotic systems [1]- [4]. These systems are complex and difficult to model accurately due to uncertainties from measurement errors, unmodeled dynamics, or environmental disturbances. As a result, there is often a mismatch between the predictive model and the actual system. It is therefore crucial to guarantee robust constraint satisfaction to ensure the safety of these systems. However, achieving such guarantees often comes at the cost of conservatism, non-trivial system-specific design, or substantial computational effort (either during operation or during controller design), originating from trading-off performance, robustness, and flexibility, as evidenced by a wealth of prior research on the topic outlined below. B. Related WorkTraditionally, robust optimal control has been divided into two main steps: (1) the optimization of a nominal trajectory (also called reference trajectory, guidance, or feedforward) [5] and (2) the offline design of a stabilizing

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Section: A Motivationmentioning

confidence: 99%