Gene

Model predictive control

Algorithm

Control (management)

Engineering

Computational complexity theory

Robust control

Control system

Robustness (evolution)

Convergence (economics)

Economics

Biochemistry

Computer science

Mathematical optimization

Robust optimization

Mathematics

Artificial intelligence

Geometry

Chemistry

Economic growth

Convex optimization

Class (philosophy)

Optimization problem

Regular polygon

Electrical engineering

IEEE Transactions on Automatic Control

Two stochastic model predictive control algorithms, which are referred to as distributionally robust model predictive control algorithms, are proposed in this article for a class of discrete linear systems with unbounded noise. Participially, chance constraints are imposed on both of the state and the control, which makes the problem more challenging. Inspired by the ideas from distributionally robust optimization (DRO), two deterministic convex reformulations are proposed for tackling the chance constraints. Rigorous computational complexity analysis is carried out to compare the two proposed algorithms with the existing methods. Recursive feasibility and convergence are proven. Simulation results are provided to show the effectiveness of the proposed algorithms.

A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control