Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration
This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/3/309 |
| _version_ | 1827751994209599488 |
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| author | Haojie Zhu Mou Chen Zengliang Han Mihai Lungu |
| author_facet | Haojie Zhu Mou Chen Zengliang Han Mihai Lungu |
| author_sort | Haojie Zhu |
| collection | DOAJ |
| description | This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method. |
| first_indexed | 2024-03-11T07:04:45Z |
| format | Article |
| id | doaj.art-684280d4bdc74b3a99768903f3fce1ea |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T07:04:45Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-684280d4bdc74b3a99768903f3fce1ea2023-11-17T08:59:17ZengMDPI AGAerospace2226-43102023-03-0110330910.3390/aerospace10030309Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence DemonstrationHaojie Zhu0Mou Chen1Zengliang Han2Mihai Lungu3School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaFaculty of Electrical Engineering, University of Craiova, 200692 Craiova, RomaniaThis paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.https://www.mdpi.com/2226-4310/10/3/309UAHIPSOIRLfire-control commandswarm intelligence |
| spellingShingle | Haojie Zhu Mou Chen Zengliang Han Mihai Lungu Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration Aerospace UAH IPSO IRL fire-control command swarm intelligence |
| title | Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration |
| title_full | Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration |
| title_fullStr | Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration |
| title_full_unstemmed | Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration |
| title_short | Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration |
| title_sort | inverse reinforcement learning based fire control command calculation of an unmanned autonomous helicopter using swarm intelligence demonstration |
| topic | UAH IPSO IRL fire-control command swarm intelligence |
| url | https://www.mdpi.com/2226-4310/10/3/309 |
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