Information-Theoretic Framework for Self-Adapting Model Predictive Controllers
Model Predictive Control (MPC) is a vital technique for autonomous systems, like Unmanned Aerial Vehicles (UAVs), enabling optimized motion planning. However, traditional MPC struggles to adapt to real-time changes such as dynamic obstacles and shifting system dynamics, lacking inherent mechanisms for self-monitoring and adaptive optimization. Here, we introduce Entanglement Learning (EL), an information-theoretic framework that enhances MPC adaptability through an Information Digital Twin (IDT). The IDT monitors and quantifies, in bits, the information flow between MPC inputs, control actions, and UAV behavior. By introducing new information-theoretic metrics we call entanglement metrics...
| Authors | Wael Hafez, Amir Nazeri |
|---|---|
| Primary category | cs.AI |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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