Model-Free Predictive Control
Model-Free Predictive Control This book presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model…
Specifikacia Model-Free Predictive Control
Model-Free Predictive Control
This book presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. These features include constraint handling, zero-offset set-point tracking and non-stationary disturbance rejection. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry.
Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterisation with Laguerre polynomials. Parameterisation with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. The discussed algorithms have also been presented in Matlab code.
Model-Free Predictive Control
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