Name
Title | ||
|---|---|---|
Trajectory Optimization With Memetic Algorithms: Time-To-Torque Minimization Of Turbocharged Engines |
Abstract | ||
|---|---|---|
A general memetic trajectory optimization method is introduced. The method is comprised of an evolutionary algorithm (EA) for global optimization, followed by local optimization. The global optimization algorithm is biogeography-based optimization (BBO), which is an EA motivated by the migratory behavior of biological organisms. For local optimization, we start with identifying a local linearized model within the region of the BBO solution by approximating the linear model with Jacobian matrix, and then optimize trajectory using gradient method. The process iterates Jacobian learning and optimization until an optimal trajectory is identified. We apply this memetic algorithm to a time-to-torque minimization problem for a gasoline turbocharged direct injection automotive engine. The optimized trajectory demonstrates significant improvement over the intuitive bang-bang controls that were originally thought to deliver the fastest transient torque response. Simulation results show that BBO decreases time-to-torque by 48% relative to bang-bang controls, and adaptive optimization decreases time-to-torque by an additional 26%. These results have significant implications for improved automotive engine performance. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Memetic algorithm,Continuous optimization,Mathematical optimization,Adaptive optimization,Trajectory optimization,Evolutionary algorithm,Global optimization,Control theory,Computer science,Multi-swarm optimization,Metaheuristic |
DocType | ISSN | Citations |
Conference | 1062-922X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon, D. | 1 | 288 | 37.24 |
| Yan Wang | 2 | 2 | 1.05 |
| Oliver Tiber | 3 | 0 | 0.34 |
| Dawei Du | 4 | 529 | 32.83 |
| Dimitar Filev | 5 | 1249 | 147.45 |
| John Michelini | 6 | 52 | 6.15 |