scholarly journals Learning High-Level Policies for Model Predictive Control

2020 ◽  
Author(s):  
Yunlong Song ◽  
Davide Scaramuzza
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Level

Model Predictive Control for Lateral Position of Endless Metal Belts

2013 ◽  
Vol 198 ◽  
pp. 525-532
Author(s):  
Stefan Grosswindhager ◽  
Klemens Schulmeister ◽  
Martin Kozek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Research Work ◽  
Lateral Position ◽  
Operating Conditions ◽  
Space Representation ◽  
Control Approach ◽  
State Space Representation ◽  
Lateral Disturbance ◽  
High Level

Endless metal belts play an important role in advanced processing lines or belt machines for many production processes. In contrast to standard conveyor lines metal belts must be run over cylindrical return drums due to the high elastic modulus of the belts material and the usually high level of pre-stress. Since cylindrical return drums do not provide passive lateral guidance (self-centering) they have to be actively adjusted by swiveling drum axes. In this work a suitable control scheme is presented to guarantee a set lateral position at the return drums even in the presence of a lateral disturbance force. Since the lateral dynamics of the endless belt show strong coupling between all inputs and all outputs a multivariate control approach with inherent decoupling capabilities is needed. Moreover, a number of technological constraints must be fulfilled for all operating conditions such as limited swivel angles and the maximum allowable tensile stress in the belt. In this research work a constrained model predictive control (MPC) is therefore designed to overcome the aforementioned problems. The model is based on a description in the spatial domain (belt travel) which renders the model independent of operating speed. Using this model a multi-input multi-output (MIMO) MPC-scheme is derived also in state-space representation. Moreover, the control explicitly considers constraints on the control inputs and on the maximum allowable belt stress.

A Method for Incorporating Nonlinear Tire Behavior Into Model Predictive Control for Vehicle Stability

2010 ◽  
Author(s):  
Craig E. Beal ◽  
J. Christian Gerdes
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problem ◽  
Longitudinal Force ◽  
Vehicle Control ◽  
Vehicle Stability ◽  
Computing Power ◽  
Force Coupling ◽  
Convex Problem ◽  
High Level

Given the increase in computing power over the last decade, model predictive control has received renewed attention as a technique for accomplishing high-level vehicle control. However, tire nonlinearities present a challenge for rapidly solving the optimization problem required to do model predictive control. This paper presents an approach which extracts the tire nonlinearities outside the MPC optimization, leaving a convex problem that can be solved rapidly and with guaranteed optimality. Experimental results are presented from an MPC controller using this technique that demonstrate the controller’s ability to handle tire nonlinearities during highly dynamic manuevers that saturate the tires and induce lateral-longitudinal force coupling effects.

Sign in / Sign up

Export Citation Format

Share Document