Gradient-based model-predictive control for green urban mobility in traffic networks

2016 ◽  
Author(s):  
Anahita Jamshidnejad ◽  
Ioannis Papamichail ◽  
Hans Hellendoorn ◽  
Markos Papageorgiou ◽  
Bart De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Traffic Networks ◽  
Urban Mobility ◽  
Gradient Based

scholarly journals Stochastic Model Predictive Control for Urban Traffic Networks

2017 ◽  
Vol 7 (6) ◽  
pp. 588 ◽  
Author(s):  
Bao-Lin Ye ◽  
Weimin Wu ◽  
Huimin Gao ◽  
Yixia Lu ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Predictive Control ◽  
Urban Traffic ◽  
Traffic Networks ◽  
Stochastic Model Predictive Control ◽  
Urban Traffic Networks

scholarly journals Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum

2004 ◽  
Vol 126 (3) ◽  
pp. 666-673 ◽  
Author(s):  
Sooyong Jung ◽  
John T. Wen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Inverted Pendulum ◽  
Optimal Solution ◽  
Nonlinear Model Predictive Control ◽  
Prediction Horizon ◽  
Experimental Implementation ◽  
Gradient Based ◽  
Rotary Inverted Pendulum

This paper presents the experimental implementation of a gradient-based nonlinear model predictive control (NMPC) algorithm to the swing-up control of a rotary inverted pendulum. The key attribute of the NMPC algorithm used here is that it only seeks to reduce the error at the end of the prediction horizon rather than finding the optimal solution. This reduces the computation load and allows real-time implementation. We discuss the implementation strategy and experimental results. In addition to NMPC based swing-up control, we also present results from a gradient based iterative learning control, which is the basis our NMPC algorithm.

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