scholarly journals Parameter Matching Analysis of Hydraulic Hybrid Excavators Based on Dynamic Programming Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Shen ◽  
Jihai Jiang ◽  
Xiaoyu Su ◽  
Hamid Reza Karimi
Keyword(s):  
Dynamic Programming ◽  
General Principle ◽  
Hot Spot ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Hydraulic Hybrid ◽  
Parameter Matching ◽  
Calculation Results ◽  
Hybrid Excavator ◽  
Common Pressure Rail

In order to meet the energy saving requirement of the excavator, hybrid excavators are becoming the hot spot for researchers. The initial problem is to match the parameter of each component, because the system is tending to be more complicated due to the introduction of the accumulator. In this paper, firstly, a new architecture is presented which is hydraulic hybrid excavator based on common pressure rail combined switched function (HHES). Secondly, the general principle of dynamic programming algorithm (DPA) is explained. Then, the method by using DPA for parameter matching of HHES is described in detail. Furthermore, the DPA is translated into the M language for simulation. Finally, the calculation results are analyzed, and the optimal matching group is obtained.

Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Kyle Williams ◽  
Monika Ivantysynova
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Environmental Protection Agency ◽  
Dynamic Programming Algorithm ◽  
Hybrid Vehicle ◽  
Programming Algorithm ◽  
Differential Dynamic Programming ◽  
Hydraulic Hybrid ◽  
Behavior Learning

This paper develops a new computational approach for energy management in a hydraulic hybrid vehicle. The developed algorithm, called approximate stochastic differential dynamic programming (ASDDP) is a variant of the classic differential dynamic programming algorithm. The simulation results are discussed for two Environmental Protection Agency drive cycles and one real world cycle based on collected data. Flexibility of the ASDDP algorithm is demonstrated as real-time driver behavior learning, and forecasted road grade information are incorporated into the control setup. Real-time potential of ASDDP is evaluated in a hardware-in-the-loop (HIL) experimental setup.

Optimization in Trajectory Planning of Multi-Jointed Fingers in Dextrous Hand Designs

1991 ◽  
Author(s):  
A. Meghdari ◽  
H. Sayyaadi
Keyword(s):  
Dynamic Programming ◽  
Motion Control ◽  
Trajectory Planning ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Kinematics And Dynamics ◽  
Feasible Solutions ◽  
Dextrous Hand

Abstract An optimization technique based on the well known Dynamic Programming Algorithm is applied to the motion control trajectories and path planning of multi-jointed fingers in dextrous hand designs. A three fingered hand with each finger containing four degrees of freedom is considered for analysis. After generating the kinematics and dynamics equations of such a hand, optimum values of the joints torques and velocities are computed such that the finger-tips of the hand are moved through their prescribed trajectories with the least time or/and energy to reach the object being grasped. Finally, optimal as well as feasible solutions for the multi-jointed fingers are identified and the results are presented.

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