Abstract
AbstractIn this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that the outputs of low-dimensional temporal model reach the set points. Thus, it makes the control design easily and reduces the computational burden. The low-dimensional model is obtained by principal component analysis (PCA) method, and the state of the low-dimensional model is estimated based on spatially-distributed output. The terminal constraints are used to transform the cost function along an infinite prediction horizon into finite prediction horizon. The simulations demonstrated show the accuracy and efficiency of the proposed method.
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