Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model
Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads th...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/7/831 |
| id |
doaj-f70e97924fad46bdb15c22777d327d9a |
|---|---|
| record_format |
Article |
| spelling |
doaj-f70e97924fad46bdb15c22777d327d9a2021-03-31T23:03:23ZengMDPI AGElectronics2079-92922021-03-011083183110.3390/electronics10070831Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm ModelIzzat Al-Darraji0Dimitrios Piromalis1Ayad A. Kakei2Fazal Qudus Khan3Milos Stojemnovic4Georgios Tsaramirsis5Panagiotis G. Papageorgas6Automated Manufacturing Department, University of Baghdad, Baghdad 10081, IraqDepartment of Industrial Design and Production Engineering, University of West Attica, 12244 Athens, GreeceMechanical Engineering Department, University of Kirkuk, Kirkuk 36001, IraqKing Abdulaziz University, Jeddah 21589, Saudi ArabiaSingidunum University, 11000 Belgrade, SerbiaAbu Dhabi Women’s Campus, Higher Colleges of Technology, Abu Dhabi 25026, United Arab EmiratesDepartment of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, GreeceAerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.https://www.mdpi.com/2079-9292/10/7/831aerial robot armsmodeling of aerial robot armsadaptive controllerrobust controllersliding mode controllerRBF neural network |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Izzat Al-Darraji Dimitrios Piromalis Ayad A. Kakei Fazal Qudus Khan Milos Stojemnovic Georgios Tsaramirsis Panagiotis G. Papageorgas |
| spellingShingle |
Izzat Al-Darraji Dimitrios Piromalis Ayad A. Kakei Fazal Qudus Khan Milos Stojemnovic Georgios Tsaramirsis Panagiotis G. Papageorgas Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model Electronics aerial robot arms modeling of aerial robot arms adaptive controller robust controller sliding mode controller RBF neural network |
| author_facet |
Izzat Al-Darraji Dimitrios Piromalis Ayad A. Kakei Fazal Qudus Khan Milos Stojemnovic Georgios Tsaramirsis Panagiotis G. Papageorgas |
| author_sort |
Izzat Al-Darraji |
| title |
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model |
| title_short |
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model |
| title_full |
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model |
| title_fullStr |
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model |
| title_full_unstemmed |
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model |
| title_sort |
adaptive robust controller design-based rbf neural network for aerial robot arm model |
| publisher |
MDPI AG |
| series |
Electronics |
| issn |
2079-9292 |
| publishDate |
2021-03-01 |
| description |
Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm. |
| topic |
aerial robot arms modeling of aerial robot arms adaptive controller robust controller sliding mode controller RBF neural network |
| url |
https://www.mdpi.com/2079-9292/10/7/831 |
| work_keys_str_mv |
AT izzataldarraji adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT dimitriospiromalis adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT ayadakakei adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT fazalquduskhan adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT milosstojemnovic adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT georgiostsaramirsis adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel AT panagiotisgpapageorgas adaptiverobustcontrollerdesignbasedrbfneuralnetworkforaerialrobotarmmodel |
| _version_ |
1724177100421726208 |