Real-Time Nonlinear Model Predictive Control of Robots Using a Graphics Processing Unit

In past robotics applications, Model Predictive Control (MPC) has often been limited to linear models and relatively short time horizons. In recent years however, research in optimization, optimal control, and simulation has enabled some forms of nonlinear model predictive control which find locally...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 5; no. 2; pp. 1467 - 1474
Main Authors: Hyatt, Phillip, Killpack, Marc D.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Complex systems
Computer simulation
control architectures and programming
deep learning in robotics and automation
model learning for control
Nonlinear control
Optimal control
Optimization
Optimization and optimal control
Predictive control
Quality control
Real time
Robot control
Robotics
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:In past robotics applications, Model Predictive Control (MPC) has often been limited to linear models and relatively short time horizons. In recent years however, research in optimization, optimal control, and simulation has enabled some forms of nonlinear model predictive control which find locally optimal solutions. The limiting factor for applying nonlinear MPC for robotics remains the computation necessary to solve the optimization, especially for complex systems and for long time horizons. This letter presents a new solution method which addresses computational concerns related to nonlinear MPC called nonlinear Evolutionary MPC (NEMPC), and then we compare it to several existing methods. These comparisons include simulations on torque-limited robots performing a swing-up task and demonstrate that NEMPC is able to discover complex behaviors to accomplish the task. Comparisons with state-of-the-art nonlinear MPC algorithms show that NEMPC finds high quality control solutions very quickly using a global, instead of local, optimization method. Finally, an application in hardware (a 24 state pneumatically actuated continuum soft robot) demonstrates that this method is tractable for real-time control of high degree of freedom systems.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2965393