Research Paper#Control Systems, Reinforcement Learning, Nonlinear Systems🔬 ResearchAnalyzed: Jan 3, 2026 19:46
IRL-Based SDRE for Nonlinear Control
Published:Dec 27, 2025 18:03
•1 min read
•ArXiv
Analysis
This paper presents a novel approach to control nonlinear systems using Integral Reinforcement Learning (IRL) to solve the State-Dependent Riccati Equation (SDRE). The key contribution is a partially model-free method that avoids the need for explicit knowledge of the system's drift dynamics, a common requirement in traditional SDRE methods. This is significant because it allows for control design in scenarios where a complete system model is unavailable or difficult to obtain. The paper demonstrates the effectiveness of the proposed approach through simulations, showing comparable performance to the classical SDRE method.
Key Takeaways
- •Proposes an Integral Reinforcement Learning (IRL) based approach for solving the State-Dependent Riccati Equation (SDRE) in nonlinear systems.
- •The method is partially model-free, eliminating the need for explicit drift dynamics knowledge.
- •Simulation results show comparable performance to the classical SDRE method.
- •Offers a viable alternative for nonlinear system control when a complete model is unavailable.
Reference
“The IRL-based approach achieves approximately the same performance as the conventional SDRE method, demonstrating its capability as a reliable alternative for nonlinear system control that does not require an explicit environmental model.”