Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Analysis and design for a class of complex control systems part II: fuzzy controller design
Automatica (Journal of IFAC)
Stabilizing controller design for uncertain nonlinear systems using fuzzy models
IEEE Transactions on Fuzzy Systems
Robust stability constraints for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
Stability analysis of discrete-time fuzzy dynamic systems based on piecewise Lyapunov functions
IEEE Transactions on Fuzzy Systems
Effective optimization for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Fuzzy Constrained Min-Max Model Predictive Control Based on Piecewise Lyapunov Functions
IEEE Transactions on Fuzzy Systems
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Quasi-Min-Max MPC algorithms for LPV systems
Automatica (Journal of IFAC)
Brief Optimizing the end-point state-weighting matrix in model-based predictive control
Automatica (Journal of IFAC)
Input-to-state stability for discrete-time nonlinear systems
Automatica (Journal of IFAC)
Robust output feedback model predictive control of constrained linear systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI
Automatica (Journal of IFAC)
Rapid load following of an SOFC power system via stable fuzzy predictive tracking controller
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
T-S model-based nonlinear moving-horizon H∞ control and applications
Fuzzy Sets and Systems
Hi-index | 22.14 |
This paper develops an efficient offset-free output feedback predictive control approach to nonlinear processes based on their approximate fuzzy models as well as an integrating disturbance model. The estimated disturbance signals account for all the plant-model mismatch and unmodeled plant disturbances. An augmented piecewise observer, constructed by solving some linear matrix inequalities, is used to estimate the system states and the lumped disturbances. Based on the reference from an online constrained target generator, the fuzzy model predictive control law can be easily obtained by solving a convex semi-definite programming optimization problem subject to several linear matrix inequalities. The resulting closed-loop system is guaranteed to be input-to-state stable even in the presence of observer estimation error. The zero offset output tracking property of the proposed control approach is proved, and subsequently demonstrated by the simulation results on a strongly nonlinear benchmark plant.