Back to Search
Start Over
A Newton method with always feasible iterates for Nonlinear Model Predictive Control of walking in a multi-contact situation
- Source :
- Humanoids, IEEE-RAS 2016-International Conference on Humanoid Robots (Humanoids), IEEE-RAS 2016-International Conference on Humanoid Robots (Humanoids), Nov 2016, Cancun, Mexico. pp.932-937, ⟨10.1109/HUMANOIDS.2016.7803384⟩
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- International audience; In this paper, we present a Nonlinear Model Predictive Control scheme, which is able to generate walking motions in multi-contact situations. Walking up and down stairs with an additional hand support is a typical example, which we address in simulation. Computing such a nonlinear control scheme is usually done with a Newton method, a potentially time-consuming procedure involving iterative linearizations. We propose here a Newton method which is specifically designed to provide at each iteration a feasible solution, always satisfying the (nonlinear) dynamic balance constraints. This results in a significant reduction in computation time, by minimizing the number of necessary iterations to reach a feasible solution.
- Subjects :
- 0209 industrial biotechnology
Mathematical optimization
Computer science
Computation
020207 software engineering
02 engineering and technology
Nonlinear control
symbols.namesake
Nonlinear system
Model predictive control
020901 industrial engineering & automation
Control theory
Iterated function
0202 electrical engineering, electronic engineering, information engineering
symbols
[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Reduction (mathematics)
Dynamic balance
Newton's method
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
- Accession number :
- edsair.doi.dedup.....62d91126e7e4e88baae4af917a64bb49
- Full Text :
- https://doi.org/10.1109/humanoids.2016.7803384