A nonlinear model predictive control strategy based on dynamic fuzzy model using two-step optimization method

2002 ◽  
Author(s):  
Zhao Xianghai ◽  
Rong Gang ◽  
Wang Yin ◽  
Wang Shuqing
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Strategy ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Nonlinear Model Predictive Control

scholarly journals Trajectory Generation for Mobile Robots in a Dynamic Environment using Nonlinear Model Predictive Control

2021 ◽  
Author(s):  
Jonas Berlin ◽  
Georg Hess ◽  
Anton Karlsson ◽  
William Ljungbergh ◽  
Ze Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Long Range ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Trajectory Generation ◽  
Dynamic Environment ◽  
Optimization Method ◽  
Time Frame ◽  
Nonlinear Model Predictive Control ◽  
Robot Dynamics

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

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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