Asymptotically tracking and dynamic regulation of SISO nonlinear system based on multi-dimensional Taylor network controller

Abstract

This paper deals with the problem of output feedback control of SISO nonlinear systems based on the multi-dimensional Taylor network (MTN) controller. It adopts the MTN controller as a network structure contr . oller and the differential geometry as an analysis tool. It proves that, when the given conditions are met, the r�ference sIgnal can be asymptotically tracked by the MTN controller. And also, it proves the MTN controller can reahze the closed-loop system dynamic regulation. Finally, an example is given to prove the correctness of above.

Adaptive multi‐dimensional Taylor network tracking control for SISO uncertain stochastic non‐linear systems

“…Based on this, Yan [44] proposed the idea of MTN optimal control. Since then, a series of extensions have been made for different non-linear systems [45][46][47]. However, Yan and Kang [45,46] focused just on deterministic non-linear systems, ignoring the influence of random factors.…”

“…Since then, a series of extensions have been made for different non-linear systems [45][46][47]. However, Yan and Kang [45,46] focused just on deterministic non-linear systems, ignoring the influence of random factors. In an attempt, Han and Yan [47] only gave some conclusions for stochastic non-linear systems without any demonstration.…”

“…The main contributions of this paper are summarised as follows: (i) A novel adaptive MTN tracking controller design approach for single-input single-output (SISO) uncertain stochastic non-linear systems is established. In our recent work, the control design approach using MTN is only for the deterministic systems [45,46]. However, stochastic disturbance and uncertainties exist in many practical systems, which are the source of instability.…”

Output‐feedback adaptive tracking control of stochastic nonlinear systems using multi‐dimensional Taylor network

“…Recently, a new network has been developed for the nonlinear system identification and prediction problems, [36][37][38] ie, the multi-dimensional Taylor network (MTN). This method was first generalized to the nonlinear control in the work of Sun and Yan, 39 since then, a series of extensions have been made for different systems, such as Yan and Kang 40,41 dealt with the problem of the output-feedback control of the SISO nonlinear systems. Zhang and Yan 42 discussed the problem of nonlinear time-varying system identification.…”

Optimal output-feedback tracking of SISO stochastic nonlinear systems using multi-dimensional Taylor network

“…Multi-dimensional Taylor network (MTN, whose idea was proposed by Hong-Sen Yan in 2010 and realization was completed by Bo Zhou who is Yan’s PhD student) was first presented by Zhou and Yan (2013), in which the structure of MTN was presented in detail. The MTN was used to solve the nonlinear system identification and prediction problems (Lin et al, 2014; Zhou and Yan, 2014a, 2014b) and to the tracking control and optimal control of nonlinear systems (Kang and Yan, 2015; Sun and Yan, 2015). However, Kang and Yan (2015) and Sun and Yan (2015) focused on deterministic nonlinear systems without stochastic disturbances and it is well known that stochastic disturbances often exist in many practical systems, while the idea of MTN optimal control was proposed by Yan (2010).…”