E. van Kampen
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151 records found
1
Incremental Dual Heuristic Programming (IDHP) is a successor to the Dual Heuristic Programming (DHP) algorithm that uses an online identified incremental system model, this algorithm showed promising online learning and fault tolerance in simulated flights. This paper studies the potential for extending IDHP through augmenting the computation of agent updates and returns, more specifically, by using eligibility trace updates and multi-step temporal difference error. This results in the IDHP, multi-step IDHP (MIDHP), and MIDHP variants, which are compared against IDHP in simulated flight scenarios with faults introduced mid-flight. The results demonstrate that flight controllers derived from the proposed variants have improved reference tracking & fault tolerance over the baseline IDHP, with the most improvement observed in MIDHP.
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.
This paper develops an intelligent flight controller for a fixed-wing aircraft model in the longitudinal plane, using a Reinforcement Learning (RL)-based control method, namely Deep Deterministic Policy Gradient (DDPG). The neural net-work controller is fed the values of aircraft position, velocity, pitch angle and pitch rate, and outputs the elevator deflection. Artificial Neural Network (ANN)s are used to approximate the nonlinear state-action value function and the policy function. Simulation results show that the flight controller learns from the experienced data to fly over an obstacle wall with constrained pitch angle.
This paper provides a convergence and stability analysis of the incremental value iteration algorithm under the influence of various errors. Incremental control is firstly used to linearize the continuous-time nonlinear system, recursive least squares (RLS) identification is then introduced to identify the incremental model online. Based on the incremental model, the value iteration algorithm is used to design an optimal adaptive controller, with an analytical optimal control law. Moreover, the convergence of the developed incremental value iteration algorithm is proved. The stability of the controller is analyzed using Lyapunov stability theory. Finally, a flight control simulation verifies the robustness of the controller to various initial conditions, as well as adaptation to actuator faults.