A data-driven control design approach for Multiple Input Multiple Output nonlinear systems is presented in this paper. The approach, called Nonlinear Inversion Control (NIC), is based on the identification of a polynomial prediction model of the system to control and the on-line inversion of this model. The main features of the NIC approach can be summarized as follows: it does not require a physical model of the plant to control which, in many real-world situations, may be difficult to derive; it can guarantee a priori properties such as closed-loop stability and tracking error accuracy; it is general, numerically efficient and relatively simple. Extensive simulations are carried out to test the numerical efficiency of the NIC approach. A simulated example of industrial interest is also presented, concerned with control of a robotic manipulator.
Control of MIMO nonlinear systems: A data-driven model inversion approach / Novara, C.; Milanese, M.. - In: AUTOMATICA. - ISSN 0005-1098. - 101:(2019), pp. 417-430. [10.1016/j.automatica.2018.12.026]
Control of MIMO nonlinear systems: A data-driven model inversion approach
Novara C.;Milanese M.
2019
Abstract
A data-driven control design approach for Multiple Input Multiple Output nonlinear systems is presented in this paper. The approach, called Nonlinear Inversion Control (NIC), is based on the identification of a polynomial prediction model of the system to control and the on-line inversion of this model. The main features of the NIC approach can be summarized as follows: it does not require a physical model of the plant to control which, in many real-world situations, may be difficult to derive; it can guarantee a priori properties such as closed-loop stability and tracking error accuracy; it is general, numerically efficient and relatively simple. Extensive simulations are carried out to test the numerical efficiency of the NIC approach. A simulated example of industrial interest is also presented, concerned with control of a robotic manipulator.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2800692