Sampling variance update method in Monte Carlo Model Predictive Control. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Nakatani, Shintaro; Date, Hisashi
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1274
• Page End: 1281
• Publication Date: 2020
• Subjects: Optimal control theory -- Monte-Carlo methods -- Randomized methods -- Model predictive -- optimization-based control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1855
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This study describes the influence of user parameters on control performance in a Monte-Carlo model predictive control (MCMPC). MCMPC based on Monte-Carlo sampling depends significantly on the characteristics of sampling distribution. We quantified the effect of user determinable parameters on control performance uisng the relatonship between the algorithm of MCMPC and convergence to the optimal solution. In particular, we investigated the limitations associated with the variance of sampling distribution causing a trade-off relationship with the convergence speed and accuracy of estimation. To overcome this limitation, we proposed two variance updating methods and new MCMPC algorithm. Furthermore, the effectiveness of the numeriacl simulation was verified.