Robust Constrained Model Predictive Control for Discrete-Time Uncertain System in Takagi-Sugeno's Form

2017 ◽  
Vol 20 (4) ◽  
pp. 1566-1581 ◽  
Author(s):  
Haofei Xie ◽  
Jun Wang ◽  
Xiaoming Tang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Uncertain System ◽  
Constrained Model ◽  
Constrained Model Predictive Control

scholarly journals Robust Constrained Model Predictive Control for T-S Fuzzy Uncertain System with Data Loss and Data Quantization

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Hongchun Qu ◽  
Yu Li ◽  
Wei Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Infinite Horizon ◽  
Uncertain System ◽  
Linear Matrix ◽  
Data Loss ◽  
Worst Case ◽  
Constrained Model ◽  
Constrained Model Predictive Control

This paper addresses the robust constrained model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy uncertain quantized system with random data loss. To deal with the quantization error and the data loss over the networks, the sector bound approach and the Bernoulli process are introduced, respectively. The fuzzy controller and new conditions for stability, which are written as the form of linear matrix inequality (LMI), are presented based on nonparallel distributed compensation (non-PDC) control law and an extended nonquadratic Lyapunov function, respectively. In addition, slack and collection matrices are provided for reducing the conservativeness. Based on the obtained stability results, a model predictive controller which explicitly considers the input and state constraints is synthesized by minimizing an upper bound of the worst-case infinite horizon quadratic cost function. The developed MPC algorithm can guarantee the recursive feasibility of the optimization problem and the stability of closed-loop system simultaneously. Finally, the simulation example is given to illustrate the effectiveness of the proposed technique.

scholarly journals Delay-Range-Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Huiyuan Shi ◽  
Ping Li ◽  
Limin Wang ◽  
Chengli Su ◽  
Jingxian Yu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Upper And Lower Bounds ◽  
Discrete Time System ◽  
Time System ◽  
Stable Conditions ◽  
Unknown Disturbances ◽  
Constrained Model ◽  
Constrained Model Predictive Control

A delay-range-dependent robust constrained model predictive control is proposed for discrete-time system with uncertainties and unknown disturbances. The dynamic characteristic of the discrete-time system is established as a new extended state space model in which state variables and output tracking error are integrated and regulated independently. It is used as the design of control law of system, which cannot only guarantee the convergence and tracking performance but also offer more degrees of freedom for designed controller. Unlike the traditional robust model predictive control (RMPC), the novel, less conservative, and more simplified delay-range-dependent stable conditions are derived by linear matrix inequality (LMI) theory and some relaxed technologies, which make use of the information of the upper and lower bounds of the time-varying delay. Meanwhile, the H∞ performance index is introduced in the RMPC controller design, which can reject any unknown bounded disturbances. As a result, the design controller has better abilities of both tracking and disturbance rejection. The control results on the liquid level of tank system show that the proposed control method is effective and feasible.

scholarly journals On Modeling and Constrained Model Predictive Control of Open Irrigation Canals

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lihui Cen ◽  
Ziqiang Wu ◽  
Xiaofang Chen ◽  
Yanggui Zou ◽  
Shaohui Zhang
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Predictive Control ◽  
Hyperbolic Partial Differential Equations ◽  
Irrigation Canal ◽  
Irrigation Canals ◽  
Optimal Control Algorithms ◽  
Saint Venant Equations ◽  
Constrained Model ◽  
Constrained Model Predictive Control

This paper proposes a model predictive control of open irrigation canals with constraints. The Saint-Venant equations are widely used in hydraulics to model an open canal. As a set of hyperbolic partial differential equations, they are not solved explicitly and difficult to design optimal control algorithms. In this work, a prediction model of an open canal is developed by discretizing the Saint-Venant equations in both space and time. Based on the prediction model, a constrained model predictive control was firstly investigated for the case of one single-pool canal and then generalized to the case of a cascaded canal with multipools. The hydraulic software SICC was used to simulate the canal and test the algorithms with application to a real-world irrigation canal of Yehe irrigation area located in Hebei province.

Constrained model predictive control: Stability and optimality

Automatica ◽  
2000 ◽  
Vol 36 (6) ◽  
pp. 789-814 ◽  
Author(s):  
D.Q. Mayne ◽  
J.B. Rawlings ◽  
C.V. Rao ◽  
P.O.M. Scokaert
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Constrained Model ◽  
Constrained Model Predictive Control ◽  
Control Stability
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