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

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.

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system.The intelligent human pilot model includes three modules:perception module,decision and adaptive module,and execution module.Intelligent and adaptive features,including a neural network receptor,fuzzy decision and adaptation,are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events.Furthermore,an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases.The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method.The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain,which is capable of reducing the PIO tendencies considerably.The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.