The papers on Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees, Deep Learning-Based Approximate Nonlinear Model Predictive Control with Offset-Free Tracking for Embedded Applications, and Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments have been accepted for presentation at the 2021 American Control Conference.
The papers on A Data-Driven Automatic Tuning Method for MPC Under Uncertainty Using Constrained Bayesian Optimization and An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-Based Economic MPC have been accepted for presentation at the 2021 IFAC Symposium on Advanced Control of Chemical Processes (ADChem).