Robust memory state feedback model predictive control for discrete-time uncertain state delayed systems

2009 ◽  
Vol 215 (6) ◽  
pp. 2035-2044 ◽  
Author(s):  
D.H. Ji ◽  
Ju H. Park ◽  
W.J. Yoo ◽  
S.C. Won
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
State Feedback ◽  
Memory State ◽  
Delayed Systems ◽  
Feedback Model ◽  
Memory State Feedback

scholarly journals Optimal guaranteed cost control of uncertain 2-D discrete state-delayed systems described by the Roesser model via memory state feedback

2018 ◽  
Vol 41 (1) ◽  
pp. 285-294
Author(s):  
Akshata Tandon ◽  
Amit Dhawan ◽  
Manish Tiwari
Keyword(s):  
Cost Function ◽  
Upper Bound ◽  
State Feedback ◽  
Closed Loop ◽  
Roesser Model ◽  
Memory State ◽  
Delayed Systems ◽  
Discrete State ◽  
Guaranteed Cost ◽  
Memory State Feedback

This paper is concerned with the problem of optimal guaranteed cost control via memory state feedback for a class of uncertain two-dimensional (2-D) discrete state-delayed systems described by the Roesser model with norm-bounded uncertainties. A linear matrix inequality (LMI)-based sufficient condition for the existence of memory state feedback guaranteed cost controllers is established and a parameterized representation of such controllers (if they exist) is given in terms of feasible solutions to a certain LMI. Furthermore, a convex optimization problem with LMI constraints is formulated to select the optimal guaranteed cost controllers that minimize the upper bound of the closed-loop cost function. The proposed method yields better results in terms of least upper bound of the closed-loop cost function as compared with a previously reported result.

Robust model predictive control synthesis for state-delayed systems with randomly occurring input saturation nonlinearities

2016 ◽  
Vol 40 (1) ◽  
pp. 179-190 ◽  
Author(s):  
Langwen Zhang ◽  
Wei Xie ◽  
Zhaozhun Zhong ◽  
Jingcheng Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Controller Design ◽  
Input Saturation ◽  
Time Instant ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Model Parameters ◽  
Delayed Systems

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms.

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