Fuel-Optimal Landing Guidance for Reusable Launch Vehicles using Model Predictive Control

Core Concepts

This paper proposes a landing guidance strategy for reusable launch vehicles (RLVs) that utilizes a model predictive approach based on sequential convex programming (SCP) to generate a fuel-optimal landing trajectory while accommodating practical path constraints.

Abstract

The paper introduces a landing guidance strategy for reusable launch vehicles (RLVs) that employs a model predictive approach based on sequential convex programming (SCP). The proposed approach involves two distinct optimal control problems (OCPs):

Planning a fuel-optimal landing trajectory that considers practical path constraints specific to RLVs.
Determining real-time optimal tracking commands.

This dual optimization strategy allows for reduced computational load through adjustable prediction horizon lengths in the tracking task, achieving near closed-loop performance. The authors enhance the model fidelity for the tracking task by using an alternative rotational dynamics representation, enabling a more stable numerical solution of the OCP and accounting for vehicle transient dynamics. Furthermore, modifications to the aerodynamic force representation in both planning and tracking phases are proposed, tailored for thrust-vector-controlled RLVs, to reduce the fidelity gap without adding computational complexity. Extensive 6-DOF simulation experiments validate the effectiveness and improved guidance performance of the proposed algorithm.

Stats

This paper does not contain any explicit numerical data or statistics. The key insights are derived from the problem formulation, algorithm design, and simulation results.

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Key Insights Distilled From

Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles

by Ki-Wook Jung... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01264.pdf

Deeper Inquiries

How can the proposed guidance algorithm be extended to handle greater uncertainties in the vehicle dynamics and environmental conditions during the landing phase

To extend the proposed guidance algorithm to handle greater uncertainties in vehicle dynamics and environmental conditions during the landing phase, several strategies can be implemented:

Robust Optimization: Introduce robust optimization techniques that account for uncertainties in the system parameters, such as varying atmospheric conditions, aerodynamic properties, or mass variations. By formulating the optimization problem to minimize the impact of uncertainties on the performance metrics, the algorithm can generate more reliable and robust trajectories.

Adaptive Control: Implement adaptive control algorithms that can adjust the control inputs in real-time based on the observed discrepancies between the predicted and actual system behavior. By continuously updating the control laws to adapt to changing conditions, the algorithm can maintain optimal performance even in the presence of uncertainties.

Kalman Filtering: Incorporate Kalman filtering or other estimation techniques to estimate the states of the system and the uncertainties in real-time. By fusing sensor measurements with the system model, the algorithm can improve the accuracy of state estimation and adjust the control inputs accordingly to compensate for uncertainties.

Scenario-based Planning: Develop scenario-based planning strategies that consider multiple possible scenarios of uncertainties and plan trajectories that are robust across these scenarios. By optimizing trajectories that perform well under various uncertain conditions, the algorithm can ensure a higher likelihood of success during the landing phase.

Sensitivity Analysis: Conduct sensitivity analysis to identify the parameters or variables that have the most significant impact on the performance metrics. By focusing on these critical factors, the algorithm can prioritize robustness measures for these aspects and improve the overall resilience of the guidance system.

What are the potential challenges in implementing the model predictive control-based landing guidance on-board an actual RLV, and how can they be addressed

Implementing the model predictive control-based landing guidance on-board an actual RLV poses several challenges that need to be addressed:

Computational Complexity: The on-board processors of RLVs may have limited computational capabilities, making real-time optimization challenging. To address this, the algorithm should be optimized for efficiency, possibly by reducing the complexity of the optimization problem or implementing specialized hardware for faster computations.

Real-time Updates: Ensuring that the guidance algorithm can provide timely updates based on changing conditions during the landing phase is crucial. Strategies such as parallel processing, pre-computation of potential trajectories, or adaptive prediction horizons can help improve the responsiveness of the algorithm.

Actuator Constraints: The physical limitations of the actuators on the RLV, such as thrust vector control systems, must be considered in the control design. Implementing constraints on control inputs that adhere to the actuator limits is essential to prevent infeasible solutions and ensure safe operation.

Sensor Noise and Delays: Sensor noise and communication delays can affect the accuracy of state estimation and control inputs. Incorporating robust estimation techniques, feedback control strategies that account for delays, and sensor fusion methods can help mitigate the impact of these challenges.

Validation and Testing: Extensive simulation testing and hardware-in-the-loop validation are essential to verify the performance of the algorithm under realistic conditions. Iterative testing and refinement are necessary to ensure the algorithm's reliability and effectiveness in actual flight scenarios.

Could the concepts and techniques presented in this work be applied to guidance and control problems in other aerospace applications, such as planetary landing or aerial vehicle operations

The concepts and techniques presented in this work can be applied to various guidance and control problems in aerospace applications beyond RLV landing guidance:

Planetary Landing: The model predictive control and trajectory optimization methods can be adapted for planetary landing missions, where precise and fuel-efficient landing trajectories are crucial. By incorporating specific planetary dynamics and constraints, the algorithm can generate optimal landing trajectories for spacecraft.

Aerial Vehicle Operations: The approach can be extended to aerial vehicle operations, such as UAVs or drones, for tasks like autonomous navigation, obstacle avoidance, and mission planning. By customizing the optimization problem to suit the dynamics and constraints of aerial vehicles, the algorithm can enable efficient and safe autonomous operations.

Spacecraft Rendezvous and Docking: The model predictive control framework can be utilized for spacecraft rendezvous and docking maneuvers, where precise control and coordination are essential. By formulating the optimization problem to account for relative dynamics and docking constraints, the algorithm can facilitate smooth and accurate docking procedures in space missions.