Adaptive active inceptor design under shared control architecture for nonlinear pilot-induced oscillations

Abstract

This paper presents a Shared Control Architecture (SCA) between a human pilot and a smart inceptor for nonlinear Pilot Induced Oscillations (PIOs), e.g., category II or III PIOs. One innovation of this paper is that an intelligent shared control architecture is developed based on the intelligent active inceptor technique, i.e., Smart Adaptive Flight Effective Cue (SAFE-Cue). A deep reinforcement learning approach namely Deep Deterministic Policy Gradient (DDPG) method is chosen to design a gain adaptation mechanism for the SAFE-Cue module. By doing this, the gains of the SAFE-Cue will be intelligently tuned once nonlinear PIOs triggered; meanwhile, the human pilot will receive a force cue from the SAFE-Cue, and will consequently adapting his/her control policy. The second innovation of this paper is that the reward function of the DDPG based gain adaptation approach is constructed according to flying qualities. Under the premise of considering failure situation, task completion qualities and pilot workload are also taken into account. Finally, the proposed approach is validated using numerical simulation experiments with two types of scenarios: lower actuator rate limits and airframe damages. The Inceptor Peak Power-Phase (IPPP) metric is adopted to analyze the human-vehicle system simulation results. Results and analysis show that the DDPG based sharing control approach can well address nonlinear PIO problems consisting of Categories II and III PIO events.