Singularly perturbed implicit control law for linear time varying SISO systems

2013 ◽  
Vol 24 (10) ◽  
pp. 1530-1549 ◽  
Author(s):  
S. Puga ◽  
M. Bonilla ◽  
M. Malabre ◽  
R. Lozano
Keyword(s):  
Linear Time ◽  
Singularly Perturbed ◽  
Control Law ◽  
Time Varying ◽  
Siso Systems

Singularly perturbed implicit control law for linear time-varying delay MIMO systems

2015 ◽  
Vol 26 (7) ◽  
pp. 1395-1421 ◽  
Author(s):  
Saul Puga ◽  
Moises Bonilla ◽  
Michel Malabre ◽  
Sabine Mondié ◽  
Rogelio Lozano
Keyword(s):  
Mimo Systems ◽  
Linear Time ◽  
Singularly Perturbed ◽  
Control Law ◽  
Time Varying ◽  
Time Varying Delay ◽  
Varying Delay

scholarly journals Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-29 ◽  
Author(s):  
Pedro M. Vallejo LLamas ◽  
Pastora Vega
Keyword(s):  
Wastewater Treatment ◽  
Predictive Control ◽  
Treatment Process ◽  
Linear Time ◽  
Real Data ◽  
Control Law ◽  
Time Varying ◽  
Wastewater Treatment Process ◽  
Starting Point ◽  
Fuzzy Predictive Control

A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

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