Model predictive control when a local control Lyapunov function is not available

2004 ◽  
Author(s):  
G. Grimm ◽  
M.J. Messina ◽  
A.R. Teel ◽  
S. Tuna
Keyword(s):  
Lyapunov Function ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Local Control ◽  
Control Lyapunov Function

Fusion of inverse optimal and model predictive control strategies

2019 ◽  
Vol 42 (6) ◽  
pp. 1122-1134
Author(s):  
Lütfi Ulusoy ◽  
Müjde Güzelkaya ◽  
İbrahim Eksin
Keyword(s):  
Optimal Control ◽  
Lyapunov Function ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Initial Conditions ◽  
Big Bang ◽  
Receding Horizon ◽  
Control Lyapunov Function ◽  
Candidate Control ◽  
Function Matrix

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

A Control Lyapunov Approach to Finite Control Set Model Predictive Control for Permanent Magnet Synchronous Motors

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Gideon Prior ◽  
Miroslav Krstic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Harmonic Distortion ◽  
Search Space ◽  
Control Lyapunov Function ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Finite Control ◽  
Error System ◽  
Novel Model

In this paper, we present a novel model predictive control (MPC) scheme that incorporates stability information derived from a control Lyapunov function (CLF) to dynamically prune suboptimal sequences from the search space and decrease the computational burden placed on the controller. The CLF used for pruning is then incorporated into a cost function that penalizes energy in the error system as well as energy loss due to switching. Despite the very small control periods allowed due dynamic pruning, experimental results are given, showing the resulting controller generates low switching frequencies and low total harmonic distortion.

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