Robust Neural-Adaptive and Fuzzy-Adaptive Control of Flexible-Joint Robots with a Cerebellar Model Arithmetic Computer
| dc.contributor.advisor | Macnab, Chris J. B. | |
| dc.contributor.author | Razmi, Mohammadsaleh | |
| dc.contributor.committeemember | Pieper, Jeffery Kurt | |
| dc.contributor.committeemember | Westwick, David T. | |
| dc.date | 2019-06 | |
| dc.date.accessioned | 2019-01-25T19:19:13Z | |
| dc.date.available | 2019-01-25T19:19:13Z | |
| dc.date.issued | 2019-01-24 | |
| dc.description.abstract | The control of non-linear, non-minimum phase systems with a high degree of flexibility require a robust, stable controller. This thesis further develops and tests two recently proposed robust modifications of a weight update rule for direct adaptive control using the Cerebellar Model Articulation Controller (CMAC). Previous designs for robust adaptive CMAC control can stop weight growth before bursting can happen but in a very conservative way, which usually sacrifices so much performance that the method is no longer desirable. The main purpose of this thesis is to determine how to guarantee uniformly ultimately bound signals in adaptive non-linear control systems that have high performance. To ensure the validity of these methods, a Two-Link Flexible-Joints Robot (TLFJR) is utilized experimentally. TLFJR is a highly non-linear system which is prone to instability. Hence our novel robust controllers are important in the control of this system. Using our novel methods, TLFJR with added sinusoidal disturbance is controlled with a guarantee of both stability and performance. These techniques consists of two CMACs, namely robust and performance, working in parallel in a backsteppable control input form. Backstepping is used to handle all non-linearities of system with the availability of just one input. The robust controller is a CMAC with traditional weight update rules; here we use an e-modification with a conservative value of v. The performance CMAC is designed based on a novel stable weight update modification methods: the weight smoothing method(chapter 3) and a near-optimal method (chapter 4). When these methods are applied to the TLFJR experimentally, the results (chapter 5) show that the controller can stabilize the TLFJR with a guarantee of performance, as well. | en_US |
| dc.identifier.citation | Razmi, M. (2019). Robust Neural-Adaptive and Fuzzy-Adaptive Control of Flexible-Joint Robots with a Cerebellar Model Arithmetic Computer (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/36106 | |
| dc.identifier.uri | http://hdl.handle.net/1880/109847 | |
| dc.language.iso | en | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Neural-Adaptive Controller | en_US |
| dc.subject | Backstepping Controller | en_US |
| dc.subject | Fuzzy-Adaptive Controller | en_US |
| dc.subject | Flexible-Joint Robots | en_US |
| dc.subject.classification | Engineering | en_US |
| dc.title | Robust Neural-Adaptive and Fuzzy-Adaptive Control of Flexible-Joint Robots with a Cerebellar Model Arithmetic Computer | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true |