Parametrized infinite-horizon model predictive control for linear time-invariant systems with input and state constraints

2016 ◽  
Author(s):  
Michael Muehlebach ◽  
Raffaello D'Andrea
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Constraints ◽  
Linear Time ◽  
Infinite Horizon ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Time Invariant ◽  
Input And State Constraints

scholarly journals Data-driven model predictive control: closed-loop guarantees and experimental results

2021 ◽  
Vol 69 (7) ◽  
pp. 608-618
Author(s):  
Julian Berberich ◽  
Johannes Köhler ◽  
Matthias A. Müller ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Linear Time ◽  
Data Driven ◽  
Practical Implementation ◽  
Behavioral Systems ◽  
Linear Time Invariant ◽  
Tank System ◽  
Time Invariant

Abstract We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge. Our approach relies on an implicit system parametrization from behavioral systems theory based on one measured input-output trajectory. The presented MPC schemes guarantee closed-loop stability for unknown linear time-invariant (LTI) systems, even if the data are affected by noise. Further, we extend this MPC framework to control unknown nonlinear systems by continuously updating the data-driven system representation using new measurements. The simple and intuitive applicability of our approach is demonstrated with a nonlinear four-tank system in simulation and in an experiment.

scholarly journals Control of Constrained Linear-Time Varying Systems via Kautz Parametrization of Model Predictive Control Scheme

2017 ◽  
Vol 1 (2) ◽  
pp. 65 ◽  
Author(s):  
Massoud Hemmasian Ettefagh ◽  
José De Doná ◽  
Mahyar Naraghi ◽  
Farzad Towhidkhah
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Time ◽  
Optimal Solution ◽  
Time Varying ◽  
Linear Time Invariant ◽  
Decision Variables ◽  
Time Varying Systems ◽  
Kautz Network ◽  
Time Invariant

Kautz parametrization of the Model Predictive Control (MPC) method has shown its ability to reduce the number of decision variables in Linear Time Invariant (LTI) systems. This paper devotes to extend Kautz network to be used in MPC Algorithm for linear time-varying systems. It is shown that Kautz network enables us to maintain a satisfactory performance while the number of decision variables are reduced considerably. Stability of the algorithm is studied under the framework of the optimal solution. The proposed method is validated by an illustrative example. In this regard, the performance of unconstrained systems as well as constrained ones is compared.

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