Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

2012 ◽  
Vol 48 (2) ◽  
pp. 947-950 ◽  
Author(s):  
Teodoro C. Bora ◽  
Leandro dos S. Coelho ◽  
Luiz Lebensztajn
Keyword(s):  
Optimization Approach ◽  
Motor Problem ◽  
Brushless Dc

Control With Optimal Energy Regeneration in Robot Manipulators Driven by Brushless DC Motors

2018 ◽  
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Motion Control ◽  
Pulse Width Modulation ◽  
Inverse Dynamics ◽  
Robot Manipulators ◽  
Optimization Approach ◽  
Brushless Dc Motors ◽  
Energy Regeneration ◽  
Brushless Dc ◽  
Optimal Current ◽  
Regenerative Energy

In this work, simultaneous energy regeneration and motion control for robot manipulators with brushless motors is considered. The robot has a number of semi-active joints connected to ultracapacitors, while the remaining joints are fully-active, powered from constant-voltage power supplies. A three-phase inverter is used to apply voltage to each motor, and the space vector pulse width modulation technique is used to generate voltage commands for the inverter. A PI controller is used to generate voltage commands for the inverter based on reference currents. A method is developed to obtain actual torque based on the desired torque generated by a virtual controller, which can be any suitable robot motion control algorithm, for instance inverse dynamics. A novel optimization approach is used to generate reference currents that maximize the amount of regenerative energy stored in the ultracapacitor and motor inductance subject to the torque demanded by the virtual controller. An explicit solution is found for the optimal current references and it is shown that the well-known choice of a zero direct current component in the direct-quadrature frame is sub-optimal relative to our energy optimization objective. A simulation using a 2-link planar manipulator with one active and one semi-active joint is used to illustrate the results.

Genetic Algorithm-Based Optimal Design of a Rolling-Flying Vehicle

2021 ◽  
pp. 1-35
Author(s):  
Tyler Jenkins ◽  
Stefan Atay ◽  
Gregory D. Buckner ◽  
Matthew Bryant
Keyword(s):  
Genetic Algorithm ◽  
Ad Hoc ◽  
Optimization Approach ◽  
Brushless Dc ◽  
Optimization Framework ◽  
Aerodynamic Model ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
The Impact ◽  
Vehicle Size

Abstract This work describes a design optimization framework for a rolling-flying vehicle consisting of a conventional quadrotor configuration with passive wheels. For a baseline comparison, the optimization approach is also applied for a conventional (flight-only) quadrotor. The vehicle range is maximized using a hybrid multi-objective genetic algorithm in conjunction with multi-physics system models. A low Reynolds-number blade element momentum theory aerodynamic model is used with a brushless DC motor model, a terramechanics model, and a vehicle dynamics model to simulate the vehicle range under any operating angle-of-attack and forward velocity. To understand the tradeoff between vehicle size and operating range, variations in Pareto-optimal designs are presented as functions of vehicle size. A sensitivity analysis is used to better understand the impact of deviating from the optimal vehicle design variables. This work builds on current approaches in quadrotor optimization by leveraging a variety of models and formulations from literature and demonstrating the implementation of various design constraints. It also improves upon current ad-hoc rolling-flying vehicle designs created in previous studies. Results show the importance of accounting for oft-neglected component constraints in the design of high range quadrotor vehicles. The optimal vehicle mechanical configuration is shown to be independent of operating point, stressing the importance of a well-matched, optimized propulsion system. By emphasizing key constraints that affect the maximum and nominal vehicle operating points, an optimization framework is constructed that can be used for RFVs and conventional multi-rotors.

Interactive multiobjective DEA target setting using lexicographic DDF

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano
Keyword(s):  
Efficient Frontier ◽  
Directional Distance Function ◽  
Fixed Number ◽  
Optimization Approach ◽  
Target Setting ◽  
Interactive Process ◽  
Decision Making Unit ◽  
Setting Approach ◽  
Promising Area ◽  
Current Region

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

The Current Carbon Sink Structure of China's Staple Crop Production and Its Optimization Approach

2016 ◽  
Vol 18 (1) ◽  
pp. 114
Author(s):  
She Wei ◽  
Huang Huang ◽  
Guan Chunyun ◽  
Chen Fu ◽  
Chen Guanghui
Keyword(s):  
Crop Production ◽  
Carbon Sink ◽  
Optimization Approach ◽  
Staple Crop

A New Type Brushless DC Rate Generator

1989 ◽  
Vol 109 (9) ◽  
pp. 693-693
Author(s):  
Hideomi Sekine ◽  
Masakazu Mizuide ◽  
Kazuhiko Suto ◽  
Tsugumasa Suto
Keyword(s):  
Brushless Dc ◽  
New Type
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