Underwater vehicles operate in dynamic environments where sudden changes of the working conditions occur from time to time. The need of an effective control action calls for refined techniques with a high degree of robustness with respect to large parametric variations and/or uncertainties. Supervised switching control seems to be theoretical frameworks where appropriate control strategies can be developed. In this chapter, a switching adaptive tracking control based on NNs is proposed and compared with an NN-based switching controller. The form of used nets is the RBFN, which has been used successfully in other control system applications (Antonini et al., 2006) and has favourable characteristics in terms of the best approximation property (Poggio & Girosi, 1990). On the basis of numerical tests, the NNSAC is able to cope with the large transient errors related to the considered mode-switch processes, when knowledge of the different possible vehicle configurations is poor. In fact, if the operative conditions are unknown, an NNSC cannot guarantee good control performance; the pre-computed controllers cannot cope with all environment and load conditions. Therefore, the integration of a switching control strategy with adaptive controllers is particularly well suited to cope with these unknown operative conditions and improve the performance of the overall control system when the different environments where the vehicle operates are not well known. © 2012 The Institution of Engineering and Technology.

Neural network-based switching adaptive control for a remotely operated vehicle / Cavalletti, Matteo; Ippoliti, Gianluca; Longhi, Sauro. - (2012), pp. 91-112. [10.1049/PBCE077E_ch5]

Neural network-based switching adaptive control for a remotely operated vehicle

CAVALLETTI, MATTEO;IPPOLITI, Gianluca;LONGHI, SAURO
2012-01-01

Abstract

Underwater vehicles operate in dynamic environments where sudden changes of the working conditions occur from time to time. The need of an effective control action calls for refined techniques with a high degree of robustness with respect to large parametric variations and/or uncertainties. Supervised switching control seems to be theoretical frameworks where appropriate control strategies can be developed. In this chapter, a switching adaptive tracking control based on NNs is proposed and compared with an NN-based switching controller. The form of used nets is the RBFN, which has been used successfully in other control system applications (Antonini et al., 2006) and has favourable characteristics in terms of the best approximation property (Poggio & Girosi, 1990). On the basis of numerical tests, the NNSAC is able to cope with the large transient errors related to the considered mode-switch processes, when knowledge of the different possible vehicle configurations is poor. In fact, if the operative conditions are unknown, an NNSC cannot guarantee good control performance; the pre-computed controllers cannot cope with all environment and load conditions. Therefore, the integration of a switching control strategy with adaptive controllers is particularly well suited to cope with these unknown operative conditions and improve the performance of the overall control system when the different environments where the vehicle operates are not well known. © 2012 The Institution of Engineering and Technology.
2012
Further Advances in Unmanned Marine Vehicles
9781849194792
9781849194808
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/74951
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