Dual MPC for quasi-Linear Parameter Varying systems
This addresses control of uncertain parameter-varying systems for applications like robotics or process control, but appears incremental as it builds on existing MPC and estimation techniques.
The authors tackled simultaneous identification and control of quasi-Linear Parameter Varying systems by developing a dual Model Predictive Control framework that combines online estimation with robust tube-based control. They demonstrated improved tracking performance while identifying system models through a numerical example.
We present a dual Model Predictive Control (MPC) framework for the simultaneous identification and control of quasi-Linear Parameter Varying (qLPV) systems. The framework is composed of an online estimator for the states and parameters of the qLPV system, and a controller that leverages the estimated model to compute inputs with a dual purpose: tracking a reference output while actively exciting the system to enhance parameter estimation. The core of this approach is a robust tube-based MPC scheme that exploits recent developments in polytopic geometry to guarantee recursive feasibility and stability in spite of model uncertainty. The effectiveness of the framework in achieving improved tracking performance while identifying a model of the system is demonstrated through a numerical example.