scholarly journals Modified Kalman filter-based model predictive control for ship heading control with probabilistic constraints

2021 ◽  
Vol 9 (1) ◽  
pp. 109-116
Author(s):  
Subchan Subchan ◽  
Ahmad Maulana Syafii ◽  
Tahiyatul Asfihani ◽  
Dieky Adzkiya
Keyword(s):  
Kalman Filter ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Probabilistic Constraints ◽  
Heading Control ◽  
Modified Kalman Filter

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

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