Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains

Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains

The extra degree of freedom offered in hybrid electric vehicles have inspired many researchers to formulate and solve optimal control problems of various kinds. This paper presents an Approximate Dynamic Programming scheme that efficiently solves the optimal power split between the internal combustion engine and the electric machine in parallel hybrid powertrains. Gear switches and switches bet...

A New Hybrid Critic-training Method for Approximate Dynamic Programming

A variety of methods for developing quasi-optimal intelligent control systems using reinforcement learning techniques based on adaptive critics have appeared in recent years. This paper reviews the family of approximate dynamic programming techniques based on adaptive critic methods and introduces a new hybrid critic training method.

Approximate dynamic programming with parallel stochastic planning operators

Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs

Recent advances in symbolic dynamic programming (SDP) combined with the extended algebraic decision diagram (XADD) data structure have provided exact solutions for mixed discrete and continuous (hybrid) MDPs with piecewise linear dynamics and continuous actions. Since XADD-based exact solutions may grow intractably large for many problems, we propose a bounded error compression technique for XA...

The Linear Programming Approach to Approximate Dynamic Programming

The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach “fits” a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We dev...