MPC_LAB: A Conceptual Framework for Simulation, Validation, and Comparison of MPC Controllers on Heterogeneous Mechatronic Platforms with Structured Metrics and ROS2-Based Integration

MPC_LAB is a conceptual framework for the simulation, validation, and comparison of Model Predictive Control (MPC) controllers on heterogeneous mechatronic platforms.As a conceptual system, MPC_LAB defines methodological foundations, shared abstractions, and reference structures that guide the development and evaluation of MPC controllers across diverse environments. The framework supports multi-language implementations (e.g., Python, MATLAB, C, Julia) and enables real-time interfacing with simulation and robotic ecosystems such as ROS2, PyBullet, and Gazebo.Within this conceptual architecture, MPC_LAB provides the basis for structured logging of performance metrics and environmental stimuli, promoting reproducibility and cross-controller benchmarking. This Zenodo deposit includes descriptive materials, diagrams, and documentation, outlining the conceptual foundations and intended usage of MPC_LAB as a methodological reference. MPC_LAB aims to standardize testing environments for MPC controllers, enhance reproducibility of experimental studies, and support research into hybrid MPC–AI control architectures — while remaining implementation-independent at its core.