首页|Dynamic Programming in Data Drivenmodel Predictive Control?
Dynamic Programming in Data Drivenmodel Predictive Control?
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In this short note, one data driven model predictive control is studied to design the optimal control sequence. The idea of data driven means the actual output value in cost function for model predictive control is identified through input-output observed data in case of unknown but bounded noise and martingale difference sequence. After substituting the identified actual output in cost function, the total cost function in model predictive control is reformulated as the other standard form, so that dynamic programming can be applied directly. As dynamic programming is only used in optimization theory, so to extend its advantage in control theory, dynamic programming algorithm is proposed to construct the optimal control sequence. Furthermore, stability analysis for data drive model predictive control is also given based on dynamic programming strategy. Generally, the goal of this short note is to bridge the dynamic programming, system identification and model predictive control. Finally, one simulation example is used to prove the efficiency of our proposed theory.
Model predictive controlData drivenDynamic programmingNonlinear estimationStability analysis
Wang Jianhong
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School of Electronic Engineering and Automation, Jiangxi University of Science and Technology
NSTL
