2017/05/05 by Samira S. Farahani, Rupak Majumdar, Farahani, Samira S. +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Formal Methods in Verification #Logic in Computer Science (cs.LO) #Microbial Metabolic Engineering and Bioproduction #Optimization and Control (math.OC) #Probability (math.PR) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1705.02152
openalex publication_date 2017/05/05 · openalex created_date 2022/09/29 · openalex updated_date 2026/07/28
We present Shrinking Horizon Model Predictive Control (SHMPC) for\ndiscrete-time linear systems with Signal Temporal Logic (STL) specification\nconstraints under stochastic disturbances. The control objective is to maximize\nan optimization function under the restriction that a given STL specification\nis satisfied with high probability against stochastic uncertainties. We\nformulate a general solution, which does not require precise knowledge of the\nprobability distributions of the (possibly dependent) stochastic disturbances;\nonly the bounded support intervals of the density functions and moment\nintervals are used. For the specific case of disturbances that are independent\nand normally distributed, we optimize the controllers further by utilizing\nknowledge of the disturbance probability distributions. We show that in both\ncases, the control law can be obtained by solving optimization problems with\nlinear constraints at each step. We experimentally demonstrate effectiveness of\nthis approach by synthesizing a controller for an HVAC system.\n