scholarly journals Adaptive Traction Drive Control Algorithm for Electrical Energy Consumption Minimisation of Autonomous Unmanned Aerial Vehicle

2019 ◽  
Vol 15 (2) ◽  
pp. 62-70
Author(s):  
Aleksandr Korneyev ◽  
Mikhail Gorobetz ◽  
Ivars Alps ◽  
Leonids Ribickis
Keyword(s):  
Optimal Control ◽  
Energy Efficiency ◽  
Adaptive Control ◽  
Control System ◽  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Control Algorithm ◽  
Target Function ◽  
Traction Drive ◽  
Aerial Vehicle

AbstractThe paper aims at researching and developing an adaptive control system algorithm and its implementation and integration in the control system of the existing unmanned aerial vehicle (UAV). The authors describe the mathematical model of UAV and target function for energy consumption minimisation and possible searching algorithms for UAV optimal control from an energy efficiency perspective. There are two main goals: to minimise energy consumption and to develop and investigate an adaptive control algorithm for UAV traction drive in order to increase energy efficiency.The optimal control algorithm is based on two target function values, when comparing and generating corresponding control signals. The main advantage of the proposed algorithm is its unification and usability in any electrical UAV with a different number of traction drives, different or variable mass and other configuration differences without any initial manual setup. Any electric UAV is able to move with maximal energy efficiency using the proposed algorithm.

scholarly journals Synthesis of control system with guaranteed accuracy for unmanned aircraft

2020 ◽  
pp. 66-70
Author(s):  
О.В. Збруцький ◽  
А.С. Довгополий ◽  
О.Е. Кописов ◽  
O.O. Білобородов
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Dynamic Model ◽  
Unmanned Aerial Vehicle ◽  
Synthesis Method ◽  
Actual State ◽  
Forecast Horizon ◽  
Guaranteed Accuracy ◽  
Aerial Vehicle ◽  
The Impact

Unmanned aerial vehicle must be controllable and fend off disturbing influences. The quality and effectiveness of fulfilling these tasksare completely determined by the instrumentation system and the software of the control system, which ensures the safety and reliability of the unmanned aerial vehicle. The synthesis of adaptive control algorithms in the presence of disturbances in most cases suggests the use of a certain disturbance model and is associated with the use of integral regulators, which increases the order of the system, and sometimes they are substantially non-linear. Adaptive algorithms use both predictive methods for assessing the dynamics of an object. For the synthesis of the control system, was considered the dynamic model of an unmanned aerial vehicle as a solid. Then the linearized equations of transverse motion can be represented by a system of differential equations. The study performed the synthesis of control system regulators by integrating the equations of the dynamic model of the object for a given control program, made a forecast of the movement of an unmanned aerial vehicle for a certain final period of time. By optimization of program control, was taking into account the restrictions imposed on the control and adjustable variables, the adjustable variables of the forecasting model approach the corresponding control signals on the forecast horizon. At the calculation step, which is a fixed small part of the forecast horizon, the optimal control found was realized and the actual state of the unmanned aerial vehicle was measured at the end of the step. The forecast horizon moves forward one step, and this procedure is repeated. The efficiency of the model predictive control as synthesis method for unmanned aerial vehicle’s control system is analyzed. A new synthesis method of adaptive control system with guaranteed accuracy under an arbitrary external disturbance is shown. The method is based on evaluating the effect of disturbances, predicting the behavior of the system and compensating the impact on the control object by the formation of a law for changing the parameters of the additional controller, which does not directly affect the quality of the control system.The results of control system’s mathematical modeling for aircraft and multicopter types of unmanned aerial vehicles are presented.

Optimal Control of the Industrial Furnace System

2011 ◽  
Vol 383-390 ◽  
pp. 3077-3080
Author(s):  
Xin Tong Tang ◽  
Chang Qing Cai
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Energy Consumption ◽  
Industrial Furnace ◽  
Gas Temperature ◽  
Furnace Temperature ◽  
Control Temperature ◽  
Target Control ◽  
Fuel Rate ◽  
General Goal

Control system of industrial furnace is optimized based on the aspect of the combustion. General goal of the control system is to achieve the lowest fuel with the constraints of ensuring the target control temperature of the equipment. And in different output and different fuel quantity conditions, the air-fuel rate is automatically optimized to achieve the goal of energy consumption combined with gas temperature of furnace temperature, oxygen and many parameters.

Modeling and control of a quadrotor tail-sitter unmanned aerial vehicles

2021 ◽  
pp. 095965182110504
Author(s):  
Hongbo Xin ◽  
Yujie Wang ◽  
Xianzhong Gao ◽  
Qingyang Chen ◽  
Bingjie Zhu ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Euler Angle ◽  
Level Flight ◽  
Aerial Vehicles ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Hover Flight ◽  
And Control

The tail-sitter unmanned aerial vehicles have the advantages of multi-rotors and fixed-wing aircrafts, such as vertical takeoff and landing, long endurance and high-speed cruise. These make the tail-sitter unmanned aerial vehicle capable for special tasks in complex environments. In this article, we present the modeling and the control system design for a quadrotor tail-sitter unmanned aerial vehicle whose main structure consists of a traditional quadrotor with four wings fixed on the four rotor arms. The key point of the control system is the transition process between hover flight mode and level flight mode. However, the normal Euler angle representation cannot tackle both of the hover and level flight modes because of the singularity when pitch angle tends to [Formula: see text]. The dual-Euler method using two Euler-angle representations in two body-fixed coordinate frames is presented to couple with this problem, which gives continuous attitude representation throughout the whole flight envelope. The control system is divided into hover and level controllers to adapt to the two different flight modes. The nonlinear dynamic inverse method is employed to realize fuselage rotation and attitude stabilization. In guidance control, the vector field method is used in level flight guidance logic, and the quadrotor guidance method is used in hover flight mode. The framework of the whole system is established by MATLAB and Simulink, and the effectiveness of the guidance and control algorithms are verified by simulation. Finally, the flight test of the prototype shows the feasibility of the whole system.

scholarly journals Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2904 ◽  
Author(s):  
Hyebin Park ◽  
Yujin Lim
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Control Algorithm ◽  
Access Network ◽  
Mobility Prediction ◽  
Switching Operation ◽  
Cloud Radio Access Network ◽  
Radio Access ◽  
Power Control Algorithm ◽  
Heterogeneous Cloud

In 5G networks, heterogeneous cloud radio access network (H-CRAN) is considered a promising future architecture to minimize energy consumption and efficiently allocate resources. However, with the increase in the number of users, studies are performed to overcome the energy consumption problems. In this study, we propose a power control algorithm with mobility prediction to provide a high-energy efficiency for 5G H-CRAN. In particular, the proposed algorithm predicts UE mobility in vehicular mobility scenarios and performs remote radio head (RRH) switching operations based on % prediction results. We formulate an optimization problem to maximize the energy efficiency while satisfying the outage probability requirement. We then propose an RRH switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method. Simulation results demonstrate the improved energy efficiency compared with those of existing RRH switching-operation algorithms.

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