Control strategy and application of hysteretic chaotic neuron and neural network

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

Sustainability ◽

10.3390/su13116388 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6388

Author(s):

Karim M. El-Sharawy ◽

Hatem Y. Diab ◽

Mahmoud O. Abdelsalam ◽

Mostafa I. Marei

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Control System ◽

Distributed Generation ◽

Control Strategy ◽

Current Control ◽

Operating Conditions ◽

Pi Controllers ◽

Artificial Neural ◽

The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources

2011 International Conference on Clean Electrical Power (ICCEP) ◽

10.1109/iccep.2011.6036300 ◽

2011 ◽

Cited By ~ 15

Author(s):

G. Capizzi ◽

F. Bonanno ◽

C. Napoli

Keyword(s):

Neural Network ◽

Renewable Energy ◽

Energy Storage ◽

Recurrent Neural Network ◽

Control Strategy ◽

Renewable Energy Sources ◽

Energy Sources ◽

Battery Energy Storage ◽

Battery Energy

Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

Sustainability ◽

10.3390/su13063235 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3235

Author(s):

J. Enrique Sierra-García ◽

Matilde Santos

Keyword(s):

Neural Network ◽

Wind Turbine ◽

Control Strategy ◽

Neural Control ◽

Mapping Function ◽

Energy System ◽

Lookup Table ◽

Hybrid Strategy ◽

Pitch Control ◽

The Neural Network

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller

Modelling Measurement and Control A ◽

10.18280/mmc_a.931-405 ◽

2020 ◽

Vol 93 (1-4) ◽

pp. 31-38

Author(s):

Bilal Boudjellal ◽

Tarak Benslimane

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Energy ◽

Control Strategy ◽

Energy System ◽

Maximum Power ◽

Point Tracking ◽

Pi Controllers ◽

Artificial Neural ◽

Artificial Neural Network Ann

The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.