Towards Robust Fault-Tolerant Model Predictive Control with Constraints for Takagi–Sugeno Systems

  • Piotr WitczakEmail author
  • Marcin Witczak
Part of the Mathematical Engineering book series (MATHENGIN)


This chapter deals with the problem of a robust fault-tolerant model predictive control (RFT-MPC) for discrete-time nonlinear systems described by Takagi-Sugeno models. The RFT-MPC is a mixture of the \(\mathscr {H_\infty }\)-based parallel distributed controllers and the fast model Predictive Control. The approach proposed in the paper is based on a series of offline and online computations. For the given Takagi-Sugeno system, PDC is designed without considering input and state constraints. Moreover, the idea of robust invariant sets is employed to deal with both the input and state constraints. This also provides an efficient way to introduce the MPC algorithm. Therefore, enhancing the invariant set enlarges the domain of attraction. As the robustness is achieved offline, the MPC is not employed until large enough faults occur. Otherwise, it serves as a fault-tolerant control distributing any compensation actions between actuators to avoid their saturation if possible. Finally, an illustrative example is provided, proving the efficiency and quality of the proposed multi-stage RFT-MPC.


Fault Diagnosis Linear Matrix Inequality Model Predictive Control Robust Controller Main Rotor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work was financed as a research project with the science funds from the National Science Centre in Poland. The authors would like to thank project-related colleagues, who have a significant impact on the shape of this contribution.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Control and Computation Engineering, University of Zielona GóraZielona GóraPoland

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