In this work, we propose a direct-adaptive MRAC for relative-degree-unity SISO systems with unknown control direction. The proposed scheme, employing an original construction of the control law and the use of an adaptive observer, achieves the long-searched objective of injecting, through the input, the unmeasurable derivative of the output error. The output derivative injection is performed by a smart construction of the control input that features a Parameter-dependent Input Normalization (PIN). The PIN scheme does not make use of Nussbaum functions usually invoked in the direct-adaptive setting, does not require persistence of excitation of indirect adaptive schemes, does not require switching between multiple models, does not suffer from singularities and does not require to know a-priori bounds on the norm of the high-frequency gain and on the parameters. Effectiveness of the algorithm is illustrated by a numerical example.

Pin, G., Serrani, A., Wang, Y. (2022). Parameter-dependent Input Normalization: Direct-Adaptive control with Uncertain Control Direction. Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC51059.2022.9993011].

Parameter-dependent Input Normalization: Direct-Adaptive control with Uncertain Control Direction

Serrani A.;
2022

Abstract

In this work, we propose a direct-adaptive MRAC for relative-degree-unity SISO systems with unknown control direction. The proposed scheme, employing an original construction of the control law and the use of an adaptive observer, achieves the long-searched objective of injecting, through the input, the unmeasurable derivative of the output error. The output derivative injection is performed by a smart construction of the control input that features a Parameter-dependent Input Normalization (PIN). The PIN scheme does not make use of Nussbaum functions usually invoked in the direct-adaptive setting, does not require persistence of excitation of indirect adaptive schemes, does not require switching between multiple models, does not suffer from singularities and does not require to know a-priori bounds on the norm of the high-frequency gain and on the parameters. Effectiveness of the algorithm is illustrated by a numerical example.
2022
Proceedings of the IEEE Conference on Decision and Control
2674
2680
Pin, G., Serrani, A., Wang, Y. (2022). Parameter-dependent Input Normalization: Direct-Adaptive control with Uncertain Control Direction. Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC51059.2022.9993011].
Pin, G.; Serrani, A.; Wang, Y.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1062950
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