Risk-Constrained MPC With Application in Safety-Critical Control of Turbofan Engines

Several research have been adopted to design
robust
or
stochastic
model predictive control (MPC) strategies to tackle the model uncertainty. However, the worst-case events do not occur frequently, and the statistical information is neglected, which may render robust MPC strategies conservative. In addition, the stochastic MPC controller design, which computes the probability of constraint satisfaction or performance improvement using expected indicators, cannot rule out the incident of less probability to reduce the possibly catastrophic consequences. For instance, severe variations in wind speed can affect the airflow into the engine, impacting the compression and combustion processes, which may cause a surge or choke phenomenon. In this paper, we take risk-aware strategies into account for MPC design to tackle the tails of a probability distribution that can contribute to moderating the risks. Utilizing the proposed risk measurement method, we establish a risk-constrained MPC framework to seek tradeoffs between improving the control performance of interest and restricting the risk of catastrophic incidents, and we reformulate the proposed risk-constrained MPC in a favorable form that is computationally tractable. Moreover, to ensure the safety requirement of the turbofan engine operating processes, we explicitly analyze the closed-loop properties, e.g., recursive feasibility and stability, of the proposed risk-constrained MPC method. Lastly, we demonstrate the efficiency of the proposed risk-constrained MPC algorithm on the safety-critical control of turbofan engines using real-world data to verify the availability of the proposed algorithm.
Note to Practitioners
—Model predictive control (MPC) is fundamental for several applications in the process industry, transportation, and robotics. However, due to the existence of the model uncertainty, there may occur severe constraint violations or even instability for certain safety-critical systems, which regard normally given safety constraints as a priority in the deployment of control algorithm. For instance, in the safety-critical control of turbofan engine, the latent force of the dynamical system can be affected by some external surrounding factors, such as inlet wind speed, ambient temperature, barometric pressure, inlet conditions, etc., which may cause surge or choke phenomenon. To tackle the model uncertainty, several pieces of research have been adopted to design MPC strategies in the presence of uncertainties. Nonetheless, the worst-case events do not occur frequently, and the statistical information is neglected, which renders
robust
MPC strategies extremely conservative. In addition, the
stochastic
MPC controller design, which computes the probability of constraint satisfaction or performance improvement using expected indicators, does not rule out the incident of less probability to reduce the possibly catastrophic consequences. In view of the threat of the safety-critical control of turbofan engines, a risk measurement method and a risk-constrained model predictive control strategy are proposed. Meanwhile, to guarantee the safety and reliability of turbofan engines, we explicitly analyze the proposed algorithm’s closed-loop properties.