Journal of Electrical Systems and Information Technology · 2014 · 18 citations · 14 references
EngineeringChemical EngineeringProcess ControlProcess Systems EngineeringWater TreatmentBioprocess ControlControl TheoryController DesignModel Predictive ControlMultivariable ControlNonlinear Process ControlChemical CoagulationCoagulationDisturbance Rejection ControlFuzzy ControlNonlinear Control Of Process SystemsNonlinear Model Predictive ControlOutput VariablesPredictive Control
In this paper, a fuzzy model predictive control (FMPC) strategy is proposed to regulate the output variables of a coagulation chemical dosing unit. A multiple-input, multiple-output (MIMO) process model in form of a linearised Takagi–Sugeno (T–S) fuzzy model is derived. The process model is obtained through subtractive clustering from the plant's data set. The MIMO model is described by a set of coupled multiple-input, single-output models (MISO). In the controller design, the T–S fuzzy model is applied in combination with the nonlinear model predictive control (MPC) algorithm. The results show that the proposed controller has good set-point tracking when compared with nonlinear MPC and adequate disturbance rejection ability required for efficient coagulation control and process optimisation in water treatment operations.
14
Fuzzy Model Identification Based on Cluster Estimation
S. Chiu · Journal of Intelligent & Fuzzy Systems · 1994 · 2.8K citations
Effective Optimization for Fuzzy Model Predictive Control
S. Mollov, Robert Babuška, János Abonyi et al. · IEEE Transactions on Fuzzy Systems · 2004 · 139 citations