Decision transformer-based tuning for model predictive control

In this work, we propose a novel Decision Transformer-based framework for tuning the parameters of Model Predictive Control (MPC). First, we show that an MPC scheme with quadratic cost, linear constraints, and a nominal linear model can reproduce the optimal solution of an infinite-horizon nonlinear regulation problem when the cost and constraint parameters are tuned based on the history of states and control inputs. Then, we formulate parameter tuning as a sequence modeling problem and develop a Decision Transformer-based framework, referred to as MPC-Decisioner, which leverages the attention mechanism of Decision Transformers to exploit historical and contextual information and generate MPC parameters online, conditioned on trajectories of costs, states, and past parameters. The resulting framework offers interpretability through the attention scores of the Transformer and achieves improved closed-loop performance compared to baseline MPC parameter tuning methods. Its effectiveness is demonstrated through simulation studies.