A fast Nonlinear Model Predictive Control strategy for real-time motion control of mechanical systems

2017 ◽  
Author(s):  
Yutao Chen ◽  
Davide Cuccato ◽  
Mattia Bruschetta ◽  
Alessandro Beghi
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Motion Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Strategy ◽  
Mechanical Systems ◽  
Nonlinear Model Predictive Control ◽  
Time Motion

Nonlinear Model Predictive Control of Axial Piston Pumps

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Paul Zeman ◽  
Wolfgang Kemmetmüller ◽  
Andreas Kugi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Strategy ◽  
Direct Method ◽  
Electronic Control Unit ◽  
Nonlinear Model Predictive Control ◽  
Stick Slip ◽  
Swash Plate ◽  
Axial Piston

Variable displacement axial piston units are the core components of many hydrostatic and hydraulic hybrid drive trains. Therein, the fast and accurate control of the swash plate angle, utilizing the full possible dynamics of the displacement system, is essential for a good performance of the overall drive train. This paper describes the development, implementation, and the experimental validation of a control strategy for the swash plate angle based on nonlinear model predictive control (NMPC). A tailored mathematical model, which serves as the basis for the NMPC, is described in the first part of the paper. Two versions of NMPC, an indirect and a direct method, are compared with respect to their numerical complexity and their capability of handling input and state constraints. An observer strategy, which is designed to obtain the nonmeasurable states and varying parameters of the system, completes the overall control strategy. To reduce the negative influence of stick–slip friction, the concept of dithering is applied in the experimental implementation. The differences of the NMPC methods are analyzed by simulation studies and experiments. Finally, the experimental results, using an industrial electronic control unit (ECU), prove the practical feasibility and the improved control accuracy and robustness in comparison to classical (nonlinear) control strategies.

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