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 consid-ering 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 anal-ysis show that the DDPG based sharing control approach can well address nonlinear PIO problems consisting of Categories Ⅱ and Ⅲ PIO events.
基金项目
Fundamental Research Funds for the Central Universities of China(YWF-23-SDHK-L-005)
1912 Project,China()
Aeronautical Science Foundation of China(20220048051001)
NETL
NSTL
万方数据