A combined first‐ and second‐order approach for model predictive control. (30th March 2021)

• Record Type: Journal Article
• Authors: Deng, Haoyang; Ohtsuka, Toshiyuki
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 10(2021)
• Page Start: 4553
• Page End: 4569
• Publication Date: 2021-03-30
• Subjects: first‐order algorithm -- majorization minimization principle -- model predictive control -- real‐time optimization -- second‐order algorithm; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5497
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This article presents a simple iterative method that combines first‐ and second‐order approaches for linear model predictive control (MPC). Approximate value functions requiring only first‐order derivatives and incorporating fixed second‐order information are employed, which leads to a method that splits the MPC problem into subproblems along the prediction horizon, and only the states and costates (Lagrange multipliers corresponding to the state equations) are exchanged between consecutive subproblems during iteration. The convergence is guaranteed under the framework of the majorization minimization principle. For efficient implementation, practical details are discussed, and the performance was assessed against both first‐ and second‐order methods with two numerical experiments. The results indicate that the proposed method can obtain a moderately accurate solution with a small number of cheap iterations.