Abstract
A safety-intelligent framework is presented for developing a multiple-input multiple-output (MIMO) risk-based explicit model predictive control (R-eMPC) for metal hydride storage systems (MHSS). These systems are susceptible to thermal runaway during the charging process as a result of the exothermic adsorption reaction within the metal alloy. To address this issue, deterministic and stochastic safety-intelligent control algorithms are designed and implemented by explicitly embedding a dynamic risk index (RI) or risk tolerance (() into the control law and decision-making. In closed-loop analysis, the deterministic R-eMPC regulates both core temperature and hydrogen storage capacity by forecasting fault occurrence, triggering alarms, and reducing the risk index by adjusting the optimal control actions, supply pressure and water flowrate. Meanwhile, the stochastic R-eMPC accounts for uncertainties in core temperature variation by incorporating risk tolerance through chance-constraints. When the temperature exceeds the strict operational limit, stochastic R-eMPC enables continued operation beyond conservative safety boundaries via dynamic updated probabilistic limits. Closed-loop responses demonstrate that the safety-intelligent controllers effectively minimize evolving thermal risks while maximizing storage capacity during charging operation of the MHSS. Overall, the risk-based controllers improve both safety and charging performance, demonstrating the potential of the proposed framework for real-time, safety-intelligent operation of metal hydride systems.