Optimal Guidance Command Generation And Tracking For Reusable Launch Vehicle Reentry Preprint
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Optimal Guidance Command Generation and Tracking for Reusable Launch Vehicle Reentry (Preprint).
The objective of this work is to develop a robust guidance and control architecture for autonomous reusable launch vehicles that incorporates elements of recent advances in the areas of optimal trajectory generation and reconfigurable control. This work integrates three separately developed methods to form a coherent architecture with the potential to manage control effector failures, vehicle structural/aerodynamic degradation, uncertainty, and external disturbances. Outer-loop guidance commands in the form of body-frame angular rates (roll, pitch, and yaw) are generated from an optimal reference trajectory that is computed off-line with a direct pseudospectral method and then tracked by a reconfigurable inner-loop control law. The appropriate open-loop state histories from the pseudo-four-degree-of-freedom reference trajectory are converted using a modified backstepping approach that complements the inner-loop control law in a six-degree-of-freedom simulation. The inner-loop control law is capable of reacting and compensating for off-nominal conditions by employing nonlinear reconfigurable control allocation, dynamic inversion, and model-following/anti-windup prefilters. The results show that the inner loop control can adequately track the desired optimal guidance commands; thus, confirming that applicability of this control architecture for future development involving on-line, optimal trajectory generation and high-fidelity guidance and control for reentry vehicles.
Optimal Guidance Command Generation and Tracking for the Reentry of a Reusable Launch Vehicle (PREPRINT).
In this work, optimal outer-loop guidance commands are generated from an off-line reference trajectory and then tracked by a reconfigurable inner-loop control law. The primary motivation for this work is a "stepping-stone" towards online, optimal trajectory generation, footprint determination, and retargeting capabilities in the presence of control effector failures or vehicle structural/ aerodynamic degradation, uncertainty, and external disturbances. The presented guidance and control architecture uses a 6-degree-of-freedom simulation and an inner-loop controller to track body-name angular rates (roll, pitch, and yaw), generated from an optimal psuedo-4-degree-of-freedom reference trajectory that is computed using a direct pseudo spectral method. The innerloop control law is capable of reacting and compensating for off-nominal conditions by employing its nonlinear control allocation, dynamic inversion, and model-following/anti-windup prefilters. The results show that the inner-loop control can adequately track the desired optimal guidance commands; thus, confirming the applicability of this control architecture for future development involving on-line, optimal trajectory generation and high-fidelity footprint determination for reentry vehicles.
Neural Dynamic Trajectory Design for Reentry Vehicles
The next generation of reentry vehicles is envisioned to have onboard autonomous capability of real-time trajectory planning to provide capability of responsive launch and delivering payload anywhere with precise flight termination. This capability is also desired to overcome, if possible, in-flight vehicle damage or control effector failure resulting in degraded vehicle performance. An aerial vehicle is modeled as a nonlinear multi-input-multi-output (MIMO) system. An ideal optimal trajectory control design system generates a series of control commands to achieve a desired trajectory under various disturbances and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle. Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control parameters online. The advantage of the NDO system is that it is very fast and gives the trajectory almost instantaneously. The bulk of the time consuming computation is required only during off-line training. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP are the near optimal control parameters.