Erik-Jan Van Kampen

Commercial applications of flying wing aircraft present a unique opportunity to improve fuel efficiency in aviation but also pose significant challenges in stability and control. This paper improves the existing flight control laws of a commercial flying wing concept called the Flying V. The paper designs and evaluates normal law flight controls through piloted flight simulations on a moving base flight simulator, assessing handling qualities, certification compliance, and flight envelope protection capabilities. Industry-inspired outer guidance loops include envelope protections for angle of attack, bank, pitch, and load factor, using command-limiting exponential potential functions to smoothly enforce limits and prevent longitudinal instability. The inner loop employs incremental nonlinear dynamic inversion. Results demonstrate that the Flying V model, augmented with the proposed controller, achieves Level 1 handling qualities and meets nearly all certification requirements. Flight envelope protection maneuvers confirm that exponential potential functions deliver protection performance comparable to commercial aircraft and successfully prevent unstable behavior.

We unify Hamilton-Jacobi (HJ) reachability and Reinforcement Learning (RL) through a proposed running cost formulation. We prove that the resultant travel-cost value function is the unique bounded viscosity solution of a time-dependent Hamilton-Jacobi Bellman (HJB) Partial Differential Equation (PDE) with zero terminal data, whose negative sublevel set equals the strict backward-reachable tube. Using a forward reparameterization and a contraction inducing Bellman update, we show that fixed points of small-step RL value iteration converge to the viscosity solution of the forward discounted HJB. Experiments on a classical benchmark compare learned values to semi-Lagrangian HJB ground truth and quantify error.

A cascaded online learning flight control system has been developed and enhanced with respect to action smoothness. In this paper, we investigate the convergence performance of the control system, characterized by the increment of a Lyapunov function candidate. The derivation of this metric accounts for discretization errors and state prediction errors introduced by the incremental model. Comparative results are presented through flight control simulations.

Commercial applications of flying wing aircraft, such as the Flying-V considered herein, can contribute to reducing carbon and nitrogen emissions produced by the aviation sector. However, because of the lack of a tail, all flying wing aircraft have reduced controllability. For this reason, the placement and sizing of the control surfaces along the wing is a nontrivial problem. The paper focuses on solving this problem using offline handling quality simulations based on certification requirements. In different flight conditions, the aircraft must be able to perform a set of maneuvers as defined by the certification specifications. First, offline simulations calculate the minimum control authority required from the elevator, aileron, and rudder to perform each maneuver. Then, based on the global minimum for all maneuvers, the control surfaces are sized and placed along the wings. The aerodynamic model employed uses a combination of Reynolds-averaged Navier–Stokes (RANS) and vortex lattice method (VLM) simulations. The control authority of the control surfaces is estimated with VLM and VLM calibrated with RANS simulations, showing significant differences between the two.

Recent research in artificial intelligence potentially provides solutions to the challenging problem of fault-tolerant and robust flight control. This paper proposes a novel Safety-Informed Evolutionary Reinforcement Learning algorithm (SERL), which combines Deep Reinforcement Learning (DRL) and neuroevolution to optimize a population of nonlinear control policies. Using SERL, the work has trained agents to provide attitude tracking on a high-fidelity nonlinear fixed-wing aircraft model. Compared to a state-of-the-art DRL solution, SERL achieves better tracking performance in nine out of ten cases, remaining robust against faults and changes in flight conditions, while providing smoother action signals.