Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller
Also available in printed version
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Master's thesis |
| Published: |
Universiti Teknologi Malaysia
2025
|
| Subjects: | |
| Online Access: | https://utmik.utm.my/handle/123456789/102260 |
| Abstract | Abstract here |
| _version_ | 1854975047447347200 |
|---|---|
| author | Noorfadilah Ibnihajar |
| author2 | Mohd. Fua'ad Rahmat, supervisor |
| author_facet | Mohd. Fua'ad Rahmat, supervisor Noorfadilah Ibnihajar |
| author_sort | Noorfadilah Ibnihajar |
| description | Also available in printed version |
| format | Master's thesis |
| id | utm-123456789-102260 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1022602025-08-20T18:27:07Z Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller Noorfadilah Ibnihajar Mohd. Fua'ad Rahmat, supervisor Electrical engineering Also available in printed version Traffic classification is viewed very importantly for traffic data collection to help ease traffic jam. Traffic classification is often not accurate due to problems such as occlusion and view angle. Besides that, traffic classification is also inaccurate in heavy traffic condition when vehicles are moving together and is also occluded by vehicles beside and in front on them. A three-dimensional model classification system is introduced for a more accurate vehicle classification as three-dimensional model imitates real life objects. The objective of this work is to use three-dimensional models to classify vehicles in different traffic conditions and multiple flow. In this project, the flow begins with using Gaussian Model Mixture Model to detect vehicles on the road. This is done by extracting its foreground mask which separates the object from its background. Morphological opening is performed to filter noise and also to fill up holes. The detection method has achieved an accuracy of 87.83%. The three-dimensional models are built for each of the vehicle class which are for bus, sedan cars and motorcycles. Histogram of Oriented Gradient (HOG) is then performed on the grayscale three-dimensional models and also on the vehicles detected from the video. The features extracted is then input to the Support Vector Machine (SVM) classifier to obtain the classification result. This classification method has achieved an accuracy of 88.41% atiff UTM 87 p. Thesis (Sarjana Kejuruteraan (Elektrik - Mekatronik dan Kawalan Automatik)) - Universiti Teknologi Malaysia, 2016 2025-04-10T05:10:47Z 2025-04-10T05:10:47Z 2016 Master's thesis https://utmik.utm.my/handle/123456789/102260 valet-20160814-120024 vital:90300 Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Electrical engineering Noorfadilah Ibnihajar Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| thesis_level | Master |
| title | Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| title_full | Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| title_fullStr | Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| title_full_unstemmed | Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| title_short | Optimal control of inverted pendulum (IP) system using linear-quadratic regulator (LQR) controller |
| title_sort | optimal control of inverted pendulum ip system using linear quadratic regulator lqr controller |
| topic | Electrical engineering |
| url | https://utmik.utm.my/handle/123456789/102260 |
| work_keys_str_mv | AT noorfadilahibnihajar optimalcontrolofinvertedpendulumipsystemusinglinearquadraticregulatorlqrcontroller |