CR3BP with Low-Thrust (CR3BP-LT)
Definition
CR3BP-LT is an extension of the standard Circular Restricted Three-Body Problem (CR3BP) that adds continuous low-thrust acceleration terms to the spacecraft's equations of motion, used for studying orbit transfer problems with electric propulsion, ion propulsion, and other low-thrust propulsion systems[1]. This model was formally proposed and systematically applied in the A2PPO research by Ul Haq et al. in 2026.
Equations of Motion
In the normalized rotating coordinate system, the equations of motion for CR3BP-LT are:
where and are the distances from the spacecraft to Earth and Moon respectively, and are Coriolis force terms, and is the dimensionless thrust control vector satisfying .
Mass evolution follows the Tsiolkovsky rocket equation:
where is the dimensionless exhaust velocity.
Key Parameters
Characteristic normalization parameters for CR3BP-LT:
| Parameter | Symbol | Earth-Moon Value |
|---|---|---|
| Mass ratio | 0.01215 | |
| Characteristic length | m | |
| Characteristic time | 375,132 s | |
| Characteristic acceleration | 9.80665 m/s² |
CR3BP-LT vs. Standard CR3BP
| Property | Standard CR3BP | CR3BP-LT |
|---|---|---|
| Energy conservation | Jacobi constant conserved | Continuous low-thrust breaks conservation |
| Dynamics | Integrable (conservative) | Non-conservative, highly nonlinear |
| Orbit characteristics | Periodic/quasi-periodic orbits | Transfer trajectories freely designable |
| Computational cost | Lower | Higher (requires mass equation integration) |
The CR3BP-LT model degenerates to the standard CR3BP when thrust is zero (), at which point the Jacobi constant is conserved again.
Application in Trajectory Optimization
The CR3BP-LT model is the core environment for deep reinforcement learning methods such as A2PPO applied to low-thrust trajectory optimization. This model:
- Retains the essential complexity of three-body dynamics — chaotic characteristics, manifold structures, energy variations, etc.
- Introduces continuous thrust control — enabling continuous acceleration/deceleration through the dimensionless thrust vector
- Maintains computational affordability — compared to high-fidelity Ephemeris models, CR3BP-LT can support the millions of integrations required for large-scale RL training
References
- [1] Ul Haq I U, Dai H, Du C. Autonomous low-thrust trajectory optimization in cislunar space via attention-augmented reinforcement learning[J]. Aerospace Science and Technology, 2026.
- [2] Du C, Song L, Zhang J, et al. A novel calculation method for low-thrust transfer trajectories in the Earth-Moon restricted three-body problem[J]. Aerospace Science and Technology, 2024.
