Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
Modeling pH neutralization processes using fuzzy-neural approaches
Fuzzy Sets and Systems
Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Engineering Applications of Artificial Intelligence
MIMO model predictive control with local linear models
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
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This paper proposes multi-model modelling and predictive control based on Local Model Networks (LMN). An LMN modelling method using Satisfying Fuzzy c-Mean (SFCM) clustering algorithm is introduced. SFCM is designed to determine a satisfactory number of local models, and an identification algorithm based on weighted performance index is used to generate multiple models with good trade-off between global fitting and local interpretation. Considering that each local model is valid only in each local regime, different predictive controllers are designed for different local models with different local constraints, and Multi-model Predictive Control with Local Constraints (MMPCLC) is presented using Parallel Distribution Compensation (PDC) method. The presented modelling and controller design procedures are demonstrated on a Multi-Inputs Multi-Outputs (MIMO) simulated pH neutralization process.