【摘 要】
：
Model predictive control is a widely used industrial technique to deal with trajectory tracking problems in many process industry applications, as well as i
【机 构】
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DepartmentofAutomation,ShanghaiJiaoTongUniversity,China
【出 处】
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The 24th International Workshop on Matrices and Statistics(第
论文部分内容阅读
Model predictive control is a widely used industrial technique to deal with trajectory tracking problems in many process industry applications, as well as in temporal logic and financial portfolio optimization. The technique relies on dynamic models of the system with manipulated vari-ables, for instance, dynamic linear models estimated out of past experimental data. This work proposes a control-oriented diagnostics method to detect influential observations in discrete-time dynamic linear models with open-loop experimental data. Not only on system parameter estimation, influence of individual observations on controller design are also measured. Through perturbing the data in their neighborhood, the sensitivity of model predictive control policies with respect to observations are studied.
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