MPC for tracking with optimal closed-loop performance

Closed-loop Subspace Predictive Control for Fault Tolerant MPC Design

Subspace predictive control (SPC) is recently seen in the literature for joint system identification and control design. This combination enables automatically tuning the parameters in conventional model predictive control (MPC); and therefore provides a solution to the problem of fault tolerant MPC design. The existing SPCs either deal with open-loop data or depend on the information of the co...

Closed-Loop Adaptation for Robust Tracking

Model updating is a critical problem in tracking. Inaccurate extraction of the foreground and background information in model adaptation would cause the model to drift and degrade the tracking performance. The most direct but yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel closed-loop model adaptation f...

On hybrid systems and closed-loop MPC systems

The following five classes of hybrid systems were recently proved to be equivalent: linear complementarity (LC) systems, extended linear complementarity (ELC) systems, mixed logical dynamical (MLD) systems, piecewise affine (PWA) systems, and maxmin-plus-scaling (MMPS) systems. Some of the equivalences were obtained under additional assumptions, such as boundedness of system variables. In this ...

Closed-Loop PARSIMonious Subspace Identification: Theory and Application to MPC

We propose in this paper a novel subspace identification method, based on PARSIMonious parameterization (Qin et al., 2005), and we show that such algorithm guarantees consistent estimates of the Markov parameters with open-loop and closed-loop data. The method uses the predictor form and it effectively exploits in all steps the Toeplitz structure of the Markov parameters’ matrices. After evalua...

Time Optimal Closed - Loop Fuzzy - Control Strategy for Nonlinear Lunar Lander Mission