A Multi-Model Approach to Model-Free Adaptive Predictive Control for Discrete Nonlinear Systems Subject to Measurement Noise

GP-based Offset-free MPC using Disturbance Models and MHE

Data-Driven Hybrid Framework for Dynamic System Control

Nonlinear electrical and electromechanical systems pose significant challenges for observer-based control design. Conventional observer approaches require accurate mathematical models, which often fail under physical irregularities such as sensor noise, measurement delays, and parameter variations. These changes decrea...

Model-free predictive control for PMSM based on a nonlinear autoregressive exogenous model and an adaptive recursive least squares algorithm

This paper proposes a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for permanent magnet synchronous motor (PMSM) drives. The core challenge addressed is the performance degradation of conventional model predictive cont...

Data-Driven Koopman-Based Model Predictive Control for a Three-Tank Hydraulic System

This paper presents an applied study of data-driven Model Predictive Control (MPC) based on the Koopman operator framework for a three-tank hydraulic benchmark. The central contribution is a compact, physics-informed lifting strategy: observable functions are chosen directly from Torricelli’s law governing turbulent or...

Koopman-based model predictive control for nonlinear systems with bounded model uncertainty

Tube-based model predictive control for wind power system using adaptive just-in-time learning modeling methodology

The wind power generation process exhibits strong nonlinearity and multiple constraints, making it difficult to establish an accurate global model for model predictive control. In practical applications, model mismatch often leads to a decline in control performance. To address this, a tube-based model predictive contr...