422425 Integrating Lyapunov-Based Offset-Free Model Predictive Control with Subspace Identification Methods

Monday, November 9, 2015: 5:28 PM
Salon F (Salt Lake Marriott Downtown at City Creek)
Masoud Kheradmandi, Chemical Engineering, McMaster University, Hamilton, ON, Canada and Prashant Mhaskar, Chemical Engineering, McMaster University, Hamilton, Canada

Owing to their ubiquitous presence, control designs have to deal with the presence of constraints and nonlinearity. Lyapunov-Based Model Predictive Control (LMPC) is one control method that enables achieving stability from a well characterized region [1, 2]. LMPC method like other MPC methods utilizes a model of the plant. In several scenarios, a good first principles model is unavailable, however, sufficient historical data exists to possibly build a data driven model for the purpose of utilization within LMPC based approaches.

The subspace identification methods (SIM) approach uses a set of input and output data, to estimate linear time-invariant models in a state space form. These methods are exploit concepts such as geometrical projections and numerical linear algebra. Numerical robustness, fewer user parameters, MIMO systems identification, model order reduction make SIMs a good choice for industrial applications [3]. SIMs are usually non-iterative methods, which makes their application, computationally affordable, and, this simplicity would make the model update, affordable.

Motivated by the above, in this work we develop a framework to integrate LMPC with the model identified by SIMs. To this end, a data driven model is first identified and utilized within offset-free LMPC approaches. Simulation results demonstrate the effectiveness of the proposed method.

[1] Mahmood, M. and Mhaskar, P. (2012). Lyapunov-based model predictive control of stochastic nonlinear systems. Automatica, 48(9), 2271-2276.

[2] Mahmood, M. and P. Mhaskar, Constrained Control Lyapunov Function Based Model Predictive Control Design, Int. J. Rob. & Nonl. Contr., 24, 374–388, 2014.

[3] Trnka, P. (2007). Subspace identification methods. PhD thesis, Ph. D. Thesis, Czech Technical University in Prague.

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