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首页> 外文期刊>International journal of innovative computing, information and control >ROBUST MODEL PREDICTIVE CONTROL BASED ON SIGN-PERTURBED SUMS STRATEGY
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ROBUST MODEL PREDICTIVE CONTROL BASED ON SIGN-PERTURBED SUMS STRATEGY

机译：ROBUST MODEL PREDICTIVE CONTROL BASED ON SIGN-PERTURBED SUMS STRATEGY

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摘要

This paper studies the problem of robust model predictive control, whose state variable in its quadratic penalty function is related with an unknown parameter vector. After formulating the open loop system and closed loop system into their corresponding linear regressor forms, sign-perturbed sums is proposed to construct one guaranteed confidence region for that unknown parameter vector, while relaxing the strict limitation of the external noise, i.e., no probabilistic density function. Only each noise has a symmetric probability distribution about zero. Consider the controller design and confidence region for the parameter vector in model predictive control simultaneously, then robust model predictive control corresponding to one min-max optimization problem, whose max operation means the worst case performance. Generally, robustness is from the uncertainty of the parameter vector, as here this parameter vector is not a scalar vector, but one guaranteed confidence region, which includes the parameter vector with one chosen probability level. Finally, one simulation example is used to prove our considered theories.

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