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Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes

Despite the success of Gaussian process based Model Predictive Control (MPC) in robotic control, its applicability scope is greatly hindered by multimodal disturbances that are prevalent in real-world settings. Here we propose a novel Mixture of Gaussian Processes based Distributionally Robust MPC (MoGP-DR-MPC) framework for linear time invariant systems subject to potentially multimodal state-dependent disturbances. This framework utilizes MoGP to automatically determine the number of modes from disturbance data. Using the mean and variance information provided by each mode-specific predictive distribution, it constructs a data-driven state-dependent ambiguity set, which allows for flexible and fine-grained disturbance modeling. Based on this ambiguity set, we impose Distributionally Robust Conditional Value-at Risk (DR-CVaR) constraints to effectively achieve distributional robustness against errors in the predictive distributions. To address the computational challenge posed by these constraints in the resulting MPC problem, we equivalently reformulate the DR-CVaR constraints into tractable second-order cone constraints. Furthermore, we provide theoretical guarantees on the recursive feasibility and stability of the proposed framework. The enhanced control performance of MoGP-DR-MPC is validated through both numerical experiments and simulations on a quadrotor system, demonstrating notable reductions in closed-loop cost by 17% and 4% respectively compared against Gaussian process based MPC.

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