We present some preliminary ideas on a data-driven Model Predictive Control framework for continuous-time systems. We use Chebyshev polynomial orthogonal bases to represent system trajectories and subsequently develop a data-driven continuous-time version of the classical Model Predictive Control algorithm. We investigate the effects of the parameters in our framework with two numerical examples and draw comparison to model-driven MPC schemes.
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Model Predictive Control,Continuous-time Systems,Data-driven Model Predictive Control,Trajectories Of System,Orthogonal Basis,Chebyshev Polynomials,Model Predictive Control Strategy,Model Predictive Control Algorithm,Mathematical Model,Control Strategy,System State,Input Signal,Control Input,Rise Time,Tracking Error,Matrix M,State-space Model,Set Of Matrices,Sequence Space,Prediction Horizon,Output Trajectory,Linear Time-invariant Systems,Iterative Learning Control,Input Trajectory,Linear Time-invariant,Model Predictive Control Problem,Compatible Dimensions,Input Output Data,Input Space,Adaptive Framework
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