Advancing Robust Control: A Comparative Study of Model-Based, Data-Driven and Learning-Enhanced Predictive Strategies

The purpose of this article is to propose a novel machine learning–enhanced data-driven model predictive control (DD-ML-MPC) to improve robustness and performance under uncertainty. The article describes a DD-ML-MPC method, based on neural-network predictors trained on historical I/O data and integrated within an MPC framework, enabling more accurate forecasts and tighter constraint satisfaction when plant models are unavailable. Using numerical simulations on a MIMO LTI system corrupted by Gaussian noise, the authors compare three strategies—Model-Based MPC (MB-MPC), Data-Driven MPC (DD-MPC) and DD-ML-MPC—by assessing root mean square error, convergence time, constraint violation rate and computational overhead. The proposed DD-ML-MPC is demonstrated on a four-state, two-input, two-output plant subjected to stochastic disturbances. Our DD-ML-MPC achieves a 20 % reduction in constraint violations and a 15 % decrease in tracking error relative to MB-MPC. The evaluation methodology is validated through Monte Carlo analysis. The evaluation criteria also include stability margins and computational efficiency under real-time constraints. New research results bridge model-based and data-driven paradigms, offering practical applications in autonomous vehicles, process control and robotics. Results demonstrate the potential for field deployment and suggest future extensions to nonlinear and time-varying systems. This paper is novel because it introduces a unified control paradigm that integrates deep learning predictors into MPC, yielding robustness and scalability improvements.