Model Predictive control of multi-input multi-output systems
Model Predictive control of multi-input multi-output systems using reduced complexity ARX-Laguerre MIMO model
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In this work, a novel method is constructed for model predictive control (MPC) of Multi-Input Multi-Output (MIMO) systems. These latter are represented by a discrete-time MIMO ARX model expansion on Laguerre orthonormal bases. The resulting model entitled MIMO ARX-Laguerre model, provides a recursive representation with parameter number reduction. The recursive formulation of the MIMO ARX-Laguerre model is used to obtain the MPC strategy and to synthesizing an adaptive predictive controller of MIMO systems. The adaptive predictive control law is computed based on multi-step-ahead finite-elemen...
In this work, a novel method is constructed for model predictive control (MPC) of Multi-Input Multi-Output (MIMO) systems. These latter are represented by a discrete-time MIMO ARX model expansion on Laguerre orthonormal bases. The resulting model entitled MIMO ARX-Laguerre model, provides a recursive representation with parameter number reduction. The recursive formulation of the MIMO ARX-Laguerre model is used to obtain the MPC strategy and to synthesizing an adaptive predictive controller of MIMO systems. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithm of both model parameters and Laguerre poles.
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