A Novel Physics‐Constrained and Nature‐Inspired Reinforcement Learning With Adaptive Model Predictive Control for Multi‐Machine Renewable Rich Microgrids

This research study proposes a physics‐constrained nature‐inspired (PCNI) reinforcement learning (RL) and model predictive control (MPC) strategy that simultaneously incorporates a trigonometric Lyapunov function into both RL's training loss functions and reward shaping, that is, dual role of physics. The proposed control synergy features a unique structure, that is, a physics‐informed RL supervisor acts as a meta‐optimiser, dynamically adjusts the MPC's cost function weights and innovatively its prediction horizon in real‐time. The RL agent is trained with a proposed custom loss function, incorporating Lyapunov constraints, system physics, and operational limits for innovative, safe, and physically plausible learning. This means that, unlike employing heuristic reward shaping, this paper introduces a concept where physics is explicitly integrated into the training process. Inspired by the natural species' adaptability, such as birds adjust their flight trajectories and reaction distance under disturbances, the proposed framework dynamically reshapes the prediction horizon and cost function weights to preserve system stability. The robustness of the proposed control architecture is validated on a multi‐machine nonlinear microgrid model under severe and recurring disturbances through a rigorous simulation environment with random perturbations every 4 seconds across seven distinct initial conditions. A conventional droop, MPC, and RL controllers are included as a baseline reference for comparative analysis. Simulation results illustrate consistently bounded frequency and voltage responses, and stringent compliance with operational limits for the proposed control scheme. The proposed control methodology achieves: (a) stability rate compared with for droop, for MPC, for RL, (b) average frequency RMSE of 0.041 pu compared with 0.029 pu for droop, 0.057 pu for MPC and 0.062 pu for RL, (c) voltage RMSE of 0.0062 pu compared with 0.018 pu for droop and 0.020 pu for both MPC and RL, and (d) average final Lyapunov value of 0.022 compared with 3.059 for droop, 2.036 pu for MPC, and 2.820 for RL, illustrating convergence to the stability criterion V(x) < 0.06 in all seven test conditions with PCNI RL‐MPC control only. These research findings highlight the potential of a hybrid learning based and adaptive model‐based control framework for stable operation of multi‐machine microgrids under consistent time‐varying and severe disturbances.