Implementation and Analysis of Nonlinear Model Predictive Controller on Embedded Systems for Real-Time Applications

2019 ◽  
Author(s):  
Saket Adhau ◽  
Sayli Patil ◽  
Deepak Ingole ◽  
Dayaram Sonawane
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Nonlinear Model ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Real Time Applications

Nonlinear Model Predictive Controller for an Unmanned Ground Vehicle on Variable Terrain

2015 ◽  
Author(s):  
Andrew Eick ◽  
David Bevly
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Control Algorithm ◽  
Target Location ◽  
Tire Model ◽  
Unmanned Ground Vehicle ◽  
Model Predictive Controller ◽  
Ground Vehicle ◽  
Rollover Risk ◽  
Predictive Controller

Rough, off-road terrain contains multiple hazards for an unmanned ground vehicle (UGV). In this paper, hazards are classified into three groups: obstacles, rough traversable terrain, and rough untraversable terrain. These three types of hazards create a rollover risk for a UGV. A nonlinear model predictive controller (NMPC) that is capable of navigating a UGV through these hazards is presented. The control algorithm features a nonlinear tire model which more accurately captures the dynamics of the UGV when compared to a linearized tire model, and has a fast enough run time for real time implementation. On an actual vehicle, the UGV is assumed to be equipped with a perception based sensor, such as a Light Detection And Ranging (LiDAR) unit, to provide information of the terrain roughness, grade, and elevation. This information is used by the NMPC to safely control the vehicle to a target location. However, for the purposes of this paper, control inputs and terrain are simulated in Car-Sim [1], and the feasibility of real time implementation is investigated.

scholarly journals Real-Time Nonlinear Model Predictive Controller for Multiple Degrees of Freedom Wave Energy Converters with Non-Ideal Power Take-Off

2021 ◽  
Vol 9 (8) ◽  
pp. 890
Author(s):  
Ali S. Haider ◽  
Ted K. A. Brekken ◽  
Alan McCall
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Wave Energy ◽  
Degrees Of Freedom ◽  
Nonlinear Effects ◽  
Integrated Treatment ◽  
Viscous Drag ◽  
Model Predictive Controller ◽  
Highly Nonlinear ◽  
Predictive Controller

An increase in wave energy converter (WEC) efficiency requires not only consideration of the nonlinear effects in the WEC dynamics and the power take-off (PTO) mechanisms, but also more integrated treatment of the whole system, i.e., the buoy dynamics, the PTO system, and the control strategy. It results in an optimization formulation that has a nonquadratic and nonstandard cost functional. This article presents the application of real-time nonlinear model predictive controller (NMPC) to two degrees of freedom point absorber type WEC with highly nonlinear PTO characteristics. The nonlinear effects, such as the fluid viscous drag, are also included in the plant dynamics. The controller is implemented on a real-time target machine, and the WEC device is emulated in real-time using the WECSIM toolbox. The results for the successful performance of the design are presented for irregular waves under linear and nonlinear hydrodynamic conditions.

Event-triggered nonlinear model predictive control for trajectory tracking of unmanned vehicles

2021 ◽  
pp. 095440702199216
Author(s):  
Kai Zou ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Xiaoqiang Sun
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Unmanned Vehicles ◽  
Tracking Accuracy ◽  
Model Predictive Controller ◽  
Time Performance ◽  
Predictive Controller ◽  
Event Triggered

In order to increase the real-time performance of lateral trajectory tracking of unmanned vehicles, this paper designs an event-triggered nonlinear model predictive controller, which can save computation resource to a large extent while the tracking accuracy is still guaranteed. Firstly, a simplified vehicle is established using a two-degree-of-freedom dynamics model. Then, according to the theory of model predictive control, a nonlinear model predictive controller (NMPC) is designed. Since traditional NMPCs often have poor real-time control performance, this paper introduces an event-triggered mechanism, which allows the remaining elements of the control variables in the control horizon to be applied to the system once a specific condition is satisfied. Finally, the proposed controller is established by Matlab/Simulink, and the different trigger conditions are compared and verified in a double lane change maneuvers Then a system for evaluation is designed to quantify the performance of the controller in different trigger conditions. For further verification of the proposed controller, a Hard-in-the-loop simulation system based on Xpack package is established to conduct an HIL experiment. The results show that compared with traditional nonlinear model predictive control, our method offers greatly improved real-time performance while the tracking accuracy is guaranteed.

scholarly journals A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

2020 ◽  
Vol 69 (5) ◽  
pp. 4935-4946 ◽  
Author(s):  
Ningyuan Guo ◽  
Basilio Lenzo ◽  
Xudong Zhang ◽  
Yuan Zou ◽  
Ruiqing Zhai ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Nonlinear Model ◽  
Model Predictive Controller ◽  
Motion Optimization ◽  
Predictive Controller

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

2021 ◽  
pp. 095965182110320
Author(s):  
Mervin Joe Thomas ◽  
Shoby George ◽  
Deepak Sreedharan ◽  
ML Joy ◽  
AP Sudheer
Keyword(s):  
Real Time ◽  
Parallel Manipulator ◽  
Control Strategies ◽  
Proportional Integral Derivative ◽  
Real Time Control ◽  
Model Predictive Controller ◽  
End Effector ◽  
Proportional Integral ◽  
Time Control ◽  
Predictive Controller

The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

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