Name
Abstract | ||
|---|---|---|
Two main issues associated with Model Predictive Control (MPC) are learning the unknown dynamics of the system and handling model uncertainties. In this paper, unknown Linear Time-Varying (LTV) system with external noise is represented by using probabilistic Gaussian Process (GP) models. In this way, we can explicitly evaluate model uncertainties as variances. As a result, it is possible to directly take obtained variances into account when planing the policy. In addition, through using analytical gradients that are available during the GP modelling process, the optimization problem in GP based MPC can be solved faster. The performance of proposed approach is demonstrated by simulations on trajectory tracking problem of a LTV system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/AMC.2016.7496359 | 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC) |
Field | DocType | ISSN |
External noise,Mathematical optimization,Control theory,Computer science,Model predictive control,Stochastic process,Control engineering,Gaussian process,Probabilistic logic,Time complexity,Optimization problem,Trajectory | Conference | 1943-6572 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Cao | 1 | 113 | 7.11 |
| Edmund Ming-Kit Lai | 2 | 120 | 58.89 |
| Fakhrul Alam | 3 | 20 | 9.06 |