Adaptive multi-dimensional Taylor network tracking control for a class of nonlinear systems

Adaptive multi‐dimensional Taylor network funnel control of a class of nonlinear systems with asymmetric input saturation

“…Once the theory of MTN is put forward, MTN has become one of the important design tools in solving nonlinear control problems because of its good approaching capability, and many significant results have been achieved. For example, authors in References 25,26 investigated some MTN tracking control schemes for nonlinear systems. Authors in References 27‐31 investigated some adaptive MTN tracking control schemes for stochastic nonlinear systems.…”

“…The main contribution of our work is as following: A new MTN‐based adaptive funnel controller design approach is firstly expanded in this paper for nonlinear systems with asymmetric input saturation. On the premise of the stability of the closed‐loop system, in order to achieve tracking control with prescribed transient behavior, a new adaptive MTN controller is developed via backstepping. Although the approximation‐based control schemes for nonlinear systems has been investigated in References 25,36,45, these cannot be directly applied to the system studied in this paper due to the presence of prescribed performance and asymmetric input saturation simultaneously. This paper first combines MTN method and funnel control together realizes the tracking control for nonlinear system with asymmetric input saturation.…”

Adaptive tracking control for a class of stochastic non‐linear systems with input saturation constraint using multi‐dimensional Taylor network

“…Among them, MTN‐based control approach shows great appliance foreground in the control problems of non‐linear systems, and many interesting results have been reported. For example, this method has been applied to single‐input single‐output (SISO) non‐linear system [22, 24], SISO time‐varying systems [25], MIMO non‐linear discrete systems [26], SISO stochastic non‐linear systems [27–29] and MIMO stochastic non‐linear systems [30, 31]. However, despite these advancements, few results on adaptive control of stochastic non‐linear systems with input constraints have been reported to date.…”

“…Specifically, the contributions of this paper are characterised by the following aspects: (i) A novel adaptive MTN control scheme is proposed to solve the tracking control problem of a class of stochastic non‐linear systems with input saturation, which is first applied MTN‐based control approach to this problem. Meanwhile, based on backstepping technique, an adaptive tracking controller is constructively designed by using signum function and MTN approach. (ii) Although in [24, 28, 29], MTN‐based adaptive control approaches for SISO non‐linear systems were developed, they only investigated the problem of non‐linear systems without input constraints and cannot solve the input saturation problems directly. In addition, unlike in [39, 40], the tracking control problem was investigated instead of the stabilisation problem. (iii) In view of the fact that the middle layer of the MTN is composed of polynomials, only including addition and multiplication operations.…”

Adaptive decentralized tracking control of a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network

“…On the other hand, due to the excellent approximation characteristics of neural network (NN) or multi-dimensional Taylor network (MTN) or fuzzy logic systems (FLSs), the method of approximation-based NN or FLS control has received considerable attention, for example, the works (Chen and Ge, 2013; Ge et al, 2003; Han, 2018; Liu et al, 2011; Yin et al, 2017) discussed the adaptive control problem for some classes of nonlinear systems based on NN, the works (Han et al, 2019; Yan et al, 2018) discussed the adaptive control problem for some classes of nonlinear systems using MTN-based approaches, the works (Yin et al, 2016) discussed the adaptive control problem for some classes of nonlinear systems based on FLSs. However, most of the results focused on SISO systems.…”