The main limitation of the CMAC (Cerebellar Model Articulation Controller) network in realistic applications for complex automated systems (robots, automated vehicles, etc...) is related to the required memory size. It is pertinent to remind that the memory used by CMAC depends firstly on the input signal quantification step and secondly on the input space dimension. For real CMAC based control applications, on the one hand, in order to increase the accuracy of the control the chosen quantification step must be as small as possible; on the other hand, generally the input space dimension is greater than two. In order to overcome the problem relating the memory size, how both the generalization and step quantization parameters may influence the CMAC's approximation quality has been discussed. Our goal is to find an optimal CMAC structure for complex dynamic systems' control. Biped robots and Flight control design for airbreathing hypersonic vehicles are two actual areas of such systems. We have applied the investigated concepts on these two quite different areas. The presented simulation results show that an optimal or sub-optimal structure carrying out a minimal modeling error could be achieved. The choice of an optimal structure allows decreasing the memory size and reducing the computing time as well
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00665586 |
| Date | 02 March 2011 |
| Creators | Yu, Weiwei |
| Publisher | Université Paris-Est |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | fra |
| Detected Language | English |
| Type | PhD thesis |
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