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Gene

Model predictive control

Quantum mechanics

Benchmark (surveying)

Control (management)

Robust control

Robustness (evolution)

Probabilistic logic

Biochemistry

Computer science

Mathematical optimization

Physics

Nonlinear system

Trajectory

Mathematics

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Geography

Lyapunov function

Artificial intelligence

Constraint satisfaction

Chemistry

Control theory (sociology)

IEEE Transactions on Automatic Control

In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighborhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state-of-the-art robust MPC approaches with a nonlinear benchmark example.

A Computationally Efficient Robust Model Predictive Control Framework for Uncertain Nonlinear Systems