Onboard guidance system design for reusable launch vehicles in the terminal area energy management phase

Tsourdos

IEEE Trans. Neural Netw. Learning Syst.

et al. 2020

113

This paper presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory dataset containing optimal system control and state trajectories is generated, while in the lower-level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.

Section: Dnn-based Control Performancementioning

confidence: 99%

Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach

Tsourdos

IEEE Trans. Neural Netw. Learning Syst.

et al. 2020

113

“…The phase of DE is the reentry phase. EF phase is TAEM, and FG phase is the approach landing phase [33]. This paper mainly investigates the MPI planning and guidance problem of the flight phase from A to E under time constraint.…”

Section: A Dynamics Model Developmentmentioning

confidence: 99%

“…A feasible trajectory can be quickly generated, and then the linear quadratic regulator (LQR) was used to track trajectory. With the advance in processor performance, many scholars have studies the guidance algorithm based on predictor-corrector method [11][12][13][14][15][16]. Brunner and Lu [17] conducted a large number of simulations and found that the predictive-correction reentry guidance exhibited a good robustness even under aerodynamic deviation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Process Integrated Planning and Guidance for Return of Reusable Aircraft Under Time Constraint

Wei

Zhang

et al. 2020

IEEE Access

As the launch frequency of reusable launch vehicle (RLV) increases, the requirement of landing site management is increasing. Especially, the return flight time of RLV should be constrained to improve the utilization of landing site. In this paper, a multi-process integrated (MPI) planning and guidance algorithm for deorbit phase and reentry phase is proposed to meet the mission requirement of RLV. Firstly, the states of RLV at reentry point and deorbit point are given by MPI planning algorithm through inverse calculation. To be specific, by taking the terminal state as the initial value, the reentry point is obtained by the backward calculation based on a cosine-quadratic bank angle profile. According to the relationship among the reentry point, the deorbit time and the deorbit range, the parameters of deorbit phase can be calculated by efficient interpolation based on deorbit ability analysis. The bank angle profile is adjusted to make estimated total flight range approximate the predefined total range by particle swarm optimization algorithm. After that, guidance algorithm considering the terminal position and time constraints of the whole flight phase is given, and the trajectory optimization problem related to brake angle and start-up time is transformed into a series of convex optimization problems in deorbit phase, which can be solved by primal-dual interior-point method with better computational performance and satisfactory accuracy. In the reentry phase, the bank angle profile is designed and updated with improved predictorcorrector algorithm to correct the error during the flight. Finally, the feasibility and robustness of the propose algorithm are verified by multi-mission planning guidance simulation and Monte Carlo simulation.

International Journal of Aerospace Engineering

“…Landing footprints provide key information for the mission planning, such as the terminal area energy management (TAEM) guidance for a shuttle entry [1]. Due to a vehicle's high lift-drag ratio and the strong maneuverability, it forms a large range of landing footprints.…”

Section: Introductionmentioning

confidence: 99%

Rapid Algorithm for Generating Entry Landing Footprints Satisfying the No-Fly Zone Constraint

Liu

Yin³

et al. 2021

An online estimation algorithm of landing footprints based on the drag acceleration-energy profile is proposed for an entry hypersonic vehicle. Firstly, based on the Evolved Acceleration Guidance Logic for Entry (EAGLE), drag acceleration-energy profiles are designed. To track the drag acceleration-energy profile obtained by the interpolation, a drag acceleration tracking law is designed. Secondly, based on the constraint model of the no-fly zone, flying around strategies are proposed for different conditions, and a reachable area algorithm is designed for no-fly zones. Additionally, by interpolating the minimum and maximum drag acceleration profiles, the terminal heading angle constraint is designed to realize the accurate calculation of the minimum and maximum downrange ranges by adjusting the sign of the bank angle. In this way, the distribution of landing footprints is more reasonable, and the boundary of a reachable area is more accurate. The simulation results under typical conditions indicate that the proposed method can calculate landing footprints for different situations rapidly and with the good adaptability.