A major problem of numerical controllers is their robustness, i.e. the state read from the plant may not be in the controller table, although it may be close to some states in the table. For continuous systems, this problem is typically handled by interpolation techniques. Unfortunately, when the plant contains both continuous and discrete variables, the interpolation approach does not work well. To cope with this kind of systems, we propose a general methodology that exploits explicit model checking in an innovative way to automatically synthesize a (time-) optimal numerical controller from a plant specification and apply an optimized strengthening algorithm only on the most significant states, in order to reach an acceptable robustness degree. We implemented all the algorithms within our CGMunφ tool, an extension of the well-known CMunφ verifier, and tested the effectiveness of our approach by applying it to the well-known truck and trailer obstacles avoidance problem. © 2007 IEEE.
Automatic synthesis of robust numerical controllers / G., Della Penna; Daniele, Magazzeni; Alberto, Tofani; Benedetto, Intrigila; Melatti, Igor; Tronci, Enrico. - STAMPA. - (2007), pp. 4-4. (Intervento presentato al convegno 3rd International Conference on Autonomic and Autonomous Systems, ICAS 2007 tenutosi a Athens nel 19 June 2007 through 25 June 2007) [10.1109/conielecomp.2007.59].
Automatic synthesis of robust numerical controllers
MELATTI, IGOR;TRONCI, Enrico
2007
Abstract
A major problem of numerical controllers is their robustness, i.e. the state read from the plant may not be in the controller table, although it may be close to some states in the table. For continuous systems, this problem is typically handled by interpolation techniques. Unfortunately, when the plant contains both continuous and discrete variables, the interpolation approach does not work well. To cope with this kind of systems, we propose a general methodology that exploits explicit model checking in an innovative way to automatically synthesize a (time-) optimal numerical controller from a plant specification and apply an optimized strengthening algorithm only on the most significant states, in order to reach an acceptable robustness degree. We implemented all the algorithms within our CGMunφ tool, an extension of the well-known CMunφ verifier, and tested the effectiveness of our approach by applying it to the well-known truck and trailer obstacles avoidance problem. © 2007 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.