The Selected Innovative Solutions in UAV Control Systems Technologies

2014 ◽  
pp. 39-56 ◽  
Author(s):  
Dariusz Nowak ◽  
Grzegorz Kopecki ◽  
Marek Orkisz ◽  
Tomasz Rogalski ◽  
Paweł Rzucidło
Keyword(s):  
Control Systems ◽  
Innovative Solutions

scholarly journals CHALLENGES TO STATE-OF-THE-ART MOULD LEVEL CONTROL SYSTEMS AND INNOVATIVE SOLUTIONS

2017 ◽  
Author(s):  
Silvio Cecchini ◽  
Pawel Krajewski ◽  
Sebastian Michelic ◽  
Heinz Rumpler ◽  
Robert Zirkl ◽  
...  
Keyword(s):  
Control Systems ◽  
State Of The Art ◽  
Level Control ◽  
Innovative Solutions

scholarly journals Models and algorithms for optimizing the transportation process of heavy loads taking into account the use of innovative transport control systems

2020 ◽  
Vol 17 (2) ◽  
pp. 145-151
Author(s):  
M. A. Asaul ◽  
◽  
R. R. Safiullin ◽  
Keyword(s):  
Control Systems ◽  
Heavy Load ◽  
Complex Operation ◽  
Innovative Solutions ◽  
Transportation Process ◽  
Transport Control ◽  
Heavy Loads ◽  
Load Transportation ◽  
Transport Complex

The article considers models and algorithms for optimizing the heavy load transportation process in order to increase its efficiency by minimizing the amount of damage to motor roads. There have been developed some proposals aimed at improving the efficiency of the transport complex operation by introducing innovative solutions in terms of planning the transportation process of heavy loads.

scholarly journals Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

2011 ◽  
Vol 2 (2) ◽  
Author(s):  
Manuel Graña ◽  
Borja Fernandez-Gauna ◽  
Jose Manuel Lopez-Guede
Keyword(s):  
Reinforcement Learning ◽  
Control Systems ◽  
Robot Control ◽  
Robotic Systems ◽  
Future Research ◽  
Multi Agent Systems ◽  
Feedback Information ◽  
Agent Systems ◽  
Innovative Solutions ◽  
Multi Agent

AbstractReinforcement Learning (RL) as a paradigm aims to develop algorithms that allow to train an agent to optimally achieve a goal with minimal feedback information about the desired behavior, which is not precisely specified. Scalar rewards are returned to the agent as response to its actions endorsing or opposing them. RL algorithms have been successfully applied to robot control design. The extension of the RL paradigm to cope with the design of control systems for Multi-Component Robotic Systems (MCRS) poses new challenges, mainly related to coping with scaling up of complexity due to the exponential state space growth, coordination issues, and the propagation of rewards among agents. In this paper, we identify the main issues which offer opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.

The amplitude transition function and the manual and oculomotor control systems.

1988 ◽  
Vol 104 (3) ◽  
pp. 363-372 ◽  
Author(s):  
Nicholas C. Barrett ◽  
Denis J. Glencross
Keyword(s):  
Control Systems ◽  
Transition Function ◽  
Oculomotor Control
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