Neural Extended State Observer Based Intelligent Integrated Guidance and Control for Hypersonic Flight

Abstract

Near-pace hypersonic flight has great potential in civil and military use due to its high speed and low cost. To optimize the design and improve the robustness, this paper focuses on the integrated guidance and control (IGC) design with nonlinear actuator dynamics in the terminal phase of hypersonic flight. Firstly, a nonlinear integrated guidance and control model is developed with saturated control surface deflection, and third-order actuator dynamics is considered. Secondly, a neural network is introduced using an extended state observer (ESO) design to estimate the complex model uncertainty, nonlinearity and disturbance. Thirdly, a command-filtered back-stepping controller is designed with flexible designed sliding surfaces to improve the terminal performance. In this process, hybrid command filters are implemented to avoid the influences of disturbances and repetitive derivation, meanwhile solving the problem of unknown control direction caused by nonlinear saturation. The stability of the closed-loop system is proved by the Lyapunov theory, and the controller parameters can be set according to the relevant remarks. Finally, a series of numerical simulations are presented to show the feasibility and validity of the proposed IGC scheme.

A Time Cooperation Guidance for Multi-Hypersonic Vehicles Based on LSTM Network and Improved Artificial Potential Field Method

“…r * f ,i and v * f ,i denote the expected vehicle state about height and velocity, while (λ * f ,i , φ * f ,i ) represent the longitude and latitude position of the target. Equation (9) means that as long as the deviation of the terminal state δh f ,i , δv f ,i , and s * f ,i are within the tolerance value, the terminal constraints are met. s * f ,i is the terminal distance between the target and the vehicles.…”

“…There is a further problem with estimating the residual time of vehicles, whose numerical solution caused heavy computation while the analytical solution leads to unexpected, huge errors. Hereby, lots of studies have paid attention to real-time trajectory planning based on intelligent methods [8,9]. Chai et al [10] built and trained a DNN network with a pre-generated trajectory that could drive a controller in real time and improve the reliability of path planning.…”