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%2C an advanced approach integrating Gaussian process (GP) with model predictive control (MPC) for enhanced control in complex systems. It begins with GP regression fundamentals%2C illustrating how it enriches MPC with enhanced predictive accuracy and robust handling of uncertainties. A central contribution of this tutorial is the first detailed%2C systematic mathematical formulation of GP-MPC in literature%2C focusing on deriving the approximation of means and variances propagation for GP multi-step predictions. Practical applications in robotics control%2C such as path-following for mobile robots in challenging terrains and mixed-vehicle platooning%2C are discussed to demonstrate the real-world effectiveness and adaptability of GP-MPC. This tutorial aims to make GP-MPC accessible to researchers and practitioners%2C enriching the learning-based control field with in-depth theoretical and practical insights and fostering further innovations in complex system control.?quality=80&w=800)
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