MACIEL, V. R. B.; http://lattes.cnpq.br/2455078520789514; MACIEL, Victor Rafael Bezerra.
Résumé:
This work proposes an algorithm for subspace predictive controllers
for networked systems, which can be extended to cloud control systems. O
Subspace predictive control is a data-based control methodology that
combines the advantages of model-based predictive control, such as robustness, capacity
of dealing with restrictions and simplicity in the treatment of multivariable systems, but
eliminating the need for a process model, which is an expensive step. The control
predictive by subspaces makes use of experimental data from the process to build the
its control law. To this end, the control algorithm uses projections from the
identification by subspaces, which only require QR decompositions of matrices
Hankel of input and output data. The proposed network control scheme consists of
in a predictive controller to generate a sequence of future control signals and a
network delay compensator on the actuator side. The architecture is all based on data,
which minimizes identification costs and enhances the practical use of the controller in
network. A new tuning method for subspace predictive controllers is proposed,
employing multi-objective optimization to obtain tuning parameters that meet
requirements expressed as desired time constants. Linked topics are presented
the identification by open-loop subspaces and the use of this data to construct the
prediction matrices. Aspects related to controller tuning and performance
predictions are discussed, with the application of the tuning method developed to models
generic univariables and a simplified model of a heavy oil fractionator.
By Ąm, the design of a predictive controller for network subspaces is presented
which is applied in a first-order univariable and multivariable models through
simulations in the Simulink® environment.