The invention is methods for landing rockets with precision using deep reinforcement learning for control. Embodiments of the invention are comprised of three steps. First, sensors collect data about the rocket's physical landing environment, passing information to rocket's database and processors. Second, the processors manipulate the information with a deep reinforcement learning program to produce instructions. Third, the instructions command the rocket's control system for optimal performance during landing.

A space-based circuit-replacing robotic system and method include a satellite grasper configured to grasp the satellite having a printed circuit onto which an integrated circuit is soldered and the integrated circuit is to be replaced; an access mechanism configured to remove the printed circuit and/or to provide access to the printed circuit; a printed circuit orientation device configured to orient a printed circuit such that sunlight is incident on the printed circuit; one or more temperature sensors configured to measure a temperature of the solder on the printed circuit; a processor configured to adjust a rate of heating to match a desired heating rate; a circuit grasping device configured to position the circuit for replacement; and an optical shield that is configured to be adjusted to allow light to pass substantially only to a desired area of the printed circuit.

Systems and methods for describing, simulating and/or optimizing spaceborne systems and missions. Configurations for spaceborne systems are generated and validated based on simulation output.

A constellation of many satellites provide communication between devices such as user terminals (UTs) and ground stations that are connected to other networks, such as the Internet. A constellation management system (CMS) facilitates management and operation of the satellites in the constellation and facilitates information exchange with other authorized systems to provide for situationally aware operation. The CMS may ingest data such as satellite telemetry, space weather data, object ephemeris data about other orbital objects, and so forth. The CMS uses the ingested data to automatically operate satellites to perform routine activities such as station keeping maneuvers, maintenance activities, interference mitigation, and so forth. Confirmation from a human operator may be obtained before performing some activities. Activities may be planned and coordinated to minimize resource consumption for the individual satellite as well as the constellation. Output, such as ephemeris data, may be provided to other parties as well.

Rocket control is a difficult and unpredictable task in environments with inclement weather. As a result, launch missions are often strictly limited based on weather conditions. The present invention provides methods for controlling a rocket to account for environmental uncertainties and maintain optimal mission performance. First, sensors collect data about the rocket's environment, passing the information to storage in the rocket's database. Second, the rocket's processor manipulates the database with an optimization algorithm producing instructions. Third, the instructions command the rocket's control system for optimal end-to-end trajectory.

A satellite includes a plurality of thrusters disposed about the satellite, each of the plurality of thrusters having a minimum thruster firing time, and a control circuit connected to the plurality of thrusters. The control circuit is configured to identify violations of the minimum thruster firing time in a non-compliant thruster firing pattern selected to achieve a specified movement, generate a plurality of compliant thruster firing patterns by replacing each of the violations of the non-compliant thruster firing pattern by zero and a minimum time in different combinations, select a compliant thruster firing pattern from the plurality of compliant thruster firing patterns to produce a satellite movement that is within a predetermined range of the specified movement, and cause the plurality of thrusters to fire according to the compliant thruster firing pattern.

For power-enhanced slew maneuvers, a method determines a power collection function for a satellite. The method determines a power cost function for the satellite. The method calculates a power enhanced slew maneuver based on the power collection function and the power cost function.

The disclosure describes a low-orbit satellite deorbit control method and system based on a particle swarm algorithm. The method includes: constructing an objective function according to a position parameter and a perturbation acceleration of each of particles in a particle swarm as well as a preset Lagrangian multiplier and a penalty factor; determining an objective fitness of each particle and a population fitness of the particle swarm based on the objective function, updating the position parameter and the velocity of each particle, and obtaining a population optimal fitness at a maximum number of iterations; updating the Lagrangian multiplier and the penalty factor according to the population optimal fitness, comparing the objective deviation value with a preset convergence condition, and determining an objective population fitness and an objective position parameter according to a comparison result to obtain an objective deorbit mode.

The disclosure describes a low-orbit satellite deorbit control method and system based on a particle swarm algorithm. The method includes: constructing an objective function according to a position parameter and a perturbation acceleration of each of particles in a particle swarm as well as a preset Lagrangian multiplier and a penalty factor; determining an objective fitness of each particle and a population fitness of the particle swarm based on the objective function, updating the position parameter and the velocity of each particle, and obtaining a population optimal fitness at a maximum number of iterations; updating the Lagrangian multiplier and the penalty factor according to the population optimal fitness, comparing the objective deviation value with a preset convergence condition, and determining an objective population fitness and an objective position parameter according to a comparison result to obtain an objective deorbit mode.

For model predictive control for a spacecraft formation, a method calculates a virtual point that represents a plurality of spacecraft orbiting in a spacecraft formation. The method calculates an outer polytope boundary and an inner polytope boundary relative to the virtual point for a given spacecraft of the plurality of spacecraft. The method maneuvers the given spacecraft to within the inner polytope boundary using model predictive control (MPC) to minimize fuel consumption.