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This paper presents a novel Nonlinear Model Predictive Control (NMPC) strategy based on a Self-Organizing Recurrent Radial Basis Function (SR-RBF) Neural Network. The key innovation lies in the adaptive architecture—both the structure and parameters of the neural network evolve in real-time using a biologically-inspired spiking-based growing and pruning algorithm. What’s new? A self-organizing recurrent neural model that dynamically adjusts its complexity. A spiking strength criterion mimicking biological neurons to decide when to add or remove neurons. An improved gradient-based optimization method for real-time control decisions under constraints. Formal Lyapunov-based stability proof for the whole control framework. Method: The SR-RBF neural network models nonlinear system dynamics. Network structure is updated in real-time using spike-based activity thresholds. An improved gradient method solves the NMPC optimization problem under constraints. Experiments: The...