Adaptive Radial Basis Function Neural Network Based Hierarchical Sliding Mode Controller for 2-Dimensional Double Pendulum Overhead Crane
| dc.contributor.advisor | Dereje Shiferaw (PhD) | |
| dc.contributor.author | Wosene Yirga | |
| dc.date.accessioned | 2024-06-17T15:48:37Z | |
| dc.date.available | 2024-06-17T15:48:37Z | |
| dc.date.issued | 2024-01 | |
| dc.description.abstract | Several control methods for an overhead crane modeled as a double pendulum with constant cable length have been published in various studies. Most of the proposed control methods were open-loop and linear control methods or nonlinear control methods that fully depended on the system model.However, the dynamic of an overhead crane is a complex nonlinear function of uncertain or unknown parameters, which reduces the performance of such control methods. In this thesis, an adaptive radial basis function neural network-based hierarchical sliding mode controller (ARBFNNHSMC) is designed to control a 2-dimensional overhead modeled as a double pendulum system with variable cable length using the Lagrange equation of motion. To reduce the chattering effect of the sliding mode controller as well as increase its robustness, ARBFNN is designed to estimate unknown or uncertain nonlinear functions in the system. The overall control law, which contains only some parts of the crane model, is designed, and the adaptation law is derived from the Lyapunov stability condition to update the weight of the network based on observed errors. The proposed control strategy and derived model are verified using MATLAB/Simulink software.For the same controller parameters,500% changes in model parameters are taken, and trolley displacement settling time and rising time for HSMC are 12.3 seconds and 6.95 seconds, respectively. On the other hand, the maximum hook’s and payload’s swing angles are around 1.34 deg and 1.9 deg for HSMC, and it is around 1.04 deg and 1.64 deg for ARBFNN-HSMC. The residual hook’s and payload’s swing angles are 0.0137 deg and -0.0319 deg, respectively, in the case of HSMC and -0.0011 deg and -0.0022 deg for ARBFNN-HSMC. This numerical result shows that ARBFNN-HSMC has better performance than HSMC for large parameter variations. In addition, the controller output of ARBFNN-HSMC is smoother than that of HSMC, as evidenced by the result. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3170 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | RBFNN | |
| dc.subject | HSMC | |
| dc.subject | 2D overhead crane | |
| dc.subject | Double pendulum | |
| dc.subject | Adaptive law | |
| dc.title | Adaptive Radial Basis Function Neural Network Based Hierarchical Sliding Mode Controller for 2-Dimensional Double Pendulum Overhead Crane | |
| dc.type | Thesis |