Name
Title | ||
|---|---|---|
Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management |
Abstract | ||
|---|---|---|
This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCST.2013.2272179 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
Markov processes,hybrid electric vehicles,learning (artificial intelligence),predictive control,quadratic programming,road vehicles,stochastic systems,HEV energy management,Markov chain,SMPCL controller,driver behavior,driver power request dynamics,driver-aware vehicle control,driver-predictive vehicle control,fuel efficiency,power allocation,quadratic programming,real-time learning,series hybrid electrical vehicle,state dimension models,stochastic MPC,stochastic dynamic programming,stochastic model predictive control with learning,stochastic optimization,Automotive controls,driver-machine interaction,energy management,model predictive control (MPC),optimization,real-time learning,stochastic control,stochastic control. | Stochastic optimization,Control theory,Markov process,Control theory,Model predictive control,Markov chain,Control engineering,Quadratic programming,Stochastic programming,Mathematics,Stochastic control | Journal |
Volume | Issue | ISSN |
22 | 3 | 1063-6536 |
Citations | PageRank | References |
36 | 1.73 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Cairano | 1 | 249 | 26.23 |
| Daniele Bernardini | 2 | 196 | 14.43 |
| Alberto Bemporad | 3 | 4353 | 568.62 |
| Ilya V. Kolmanovsky | 4 | 163 | 20.68 |