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Model Predictive Control (MPC) has emerged as a highly attractive control strategy due to its ability to explicitly accommodate all of the multivariable (multiple-input, multiple-output) and nonlinear aspects of a system in the subsequent control signal design. However, a fundamental difficulty of this MPC approach is that the implementation platform must be capable of solving a constrained optimization problem within the sampling period, which decreases as the speed of the dynamics to be controlled increases. As a result, the implementation of MPC has generally been limited to plants with slow, or otherwise very simple dynamics so that the time constraints in computing a solution are relaxed.
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