Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
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
On Stability of Constrained Receding Horizon Control with Finite Terminal Weighting Matrix
Automatica (Journal of IFAC)
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)
Brief Implementation of stabilizing receding horizon controls for time-varying systems
Automatica (Journal of IFAC)
Uniting bounded control and MPC for stabilization of constrained linear systems
Automatica (Journal of IFAC)
Robust hybrid predictive control of nonlinear systems
Automatica (Journal of IFAC)
Experimental application of predictive controllers
Journal of Control Science and Engineering - Special issue on Model Predictive Control
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For model predictive control (MPC) of constrained systems, enlarging the feasible region is usually in conflict with improving the dynamic performance. To resolve the conflict, we proposed an efficient model predictive controller with pole placement for a class of discrete-time linear systems. By specifying a group of circular regions that contain the desired closed-loop poles, appropriate terminal weighting matrices and local controllers are calculated to construct a time-varying terminal convex set, which is a significant constraint for the online optimization problem. During the online optimization, the size of the terminal convex set can adjust itself according to the actual state at each sampling time. In this way, a large initial feasible region can be achieved while maintaining the good dynamic performance. An illustrative example is used to show the effectiveness of the proposed approach.