A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driven adaptation and performance improvement in the presence of uncertainty and model mismatch. Despite the rapid growth of research on RL--MPC integration, the literature remains fragmented, particularly for control architectures built on linear or linearized predictive models. This paper presents a comprehensive Systematic Literature Review (SLR) of RL--MPC integrations for linear and linearized...
| Authors | Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy et al. |
|---|---|
| Primary category | eess.SY |
| Published | 22 April 2026 |
| Last updated at source | 5 August 2026 |
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