Optimal Control of Hopping Robot Based on Genetic Algorithm during Flight Phase

2017 ◽  
Author(s):  
Xiangyan Meng ◽  
Wenjie Ge ◽  
Yonghong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Flight Phase ◽  
Hopping Robot

Air Suspension Optimal Control Based on the Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 1066-1071
Author(s):  
Zhong Xin Li ◽  
Ji Wei Guo ◽  
Ming Hong Gao ◽  
Hong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Dynamic Model ◽  
Control Method ◽  
Control Model ◽  
Weight Matrix ◽  
Object A ◽  
Full Vehicle ◽  
Air Suspension ◽  
Optimal Control Method

Taking the full-vehicle eight-freedom dynamic model of a type of bus as the simulation object , a new optimal control method is introduced. This method is based on the genetic algorithm, and the full-vehicle optimal control model is built in the MatLab. The weight matrix of the optimal control is optimized through the genetic algorithm; then the outcome is compared with the artificially-set optimal control simulation, which shows that the genetic-algorithm based optimal control presents better performance, thereby creating a smoother ride and improving the steering stability of the vehicle.

scholarly journals Generation of Suitable Jumping Motion Pattern for Hopping Robot under Genetic Algorithm

2000 ◽  
Vol 120 (10) ◽  
pp. 1365-1371
Author(s):  
Yoshida Yoshiro ◽  
Kamano Takyua ◽  
Yasuno Takashi ◽  
Suzuki Takayuki ◽  
Harada Hironobu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Motion Pattern ◽  
Hopping Robot

Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

2020 ◽  
Vol 37 (6/7) ◽  
pp. 1049-1069
Author(s):  
Vijay Kumar ◽  
Ramita Sahni
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Resource Allocation ◽  
Real Life ◽  
Theoretic Approach ◽  
Numerical Illustration ◽  
Data Set ◽  
Allocation Model ◽  
Content Type ◽  
Software Firms

PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

scholarly journals Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm

Buildings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 195
Author(s):  
Nam-Chul Seong ◽  
Jee-Heon Kim ◽  
Wonchang Choi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Energy Savings ◽  
Energy Performance ◽  
Operating Conditions ◽  
Hvac System ◽  
Control Variables ◽  
Savings Rate ◽  
Multiple Variables ◽  
Control Set

Optimizing the operating conditions and control set points of the heating, ventilation, and air-conditioning (HVAC) system in a building is one of the most effective ways to save energy and improve the building’s energy performance. Here, we optimized different control variables using a genetic algorithm. We constructed and evaluated three optimal control scenarios (cases) to compare the energy savings of each by varying the setting and number and type of the optimized control variables. Case 1 used only air-side control variables and achieved an energy savings rate of about 5.72%; case 2 used only water-side control variables and achieved an energy savings rate of 16.98%; and case 3, which combined all the control variables, achieved 25.14% energy savings. The energy savings percentages differed depending on the setting and type of the control variables. The results show that, when multiple control set points are optimized simultaneously in an HVAC system, the energy savings efficiency becomes more effective. It was also confirmed that the control characteristics and energy saving rate change depending on the location and number of control variables when optimizing using the same algorithm.

scholarly journals Simulation and Optimal Control for the Koji Making Process Using Genetic Algorithm

1998 ◽  
Vol 31 (8) ◽  
pp. 147-152 ◽  
Author(s):  
H. Shiba ◽  
K. Matsuura ◽  
M. Hirotsune ◽  
M. Hamachi ◽  
C. Kumagai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control
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