Adaptive tracking control for a class of stochastic non‐linear systems with input delay: a novel approach based on multi‐dimensional Taylor network

Adaptive Tracking Control of a Class of Nonlinear Systems with Input Delay and Dynamic Uncertainties Using Multi-dimensional Taylor Network

“…In the simulation, the reference signal is chosen as y d = 0.5 (sin(t) + sin (0.5t)), which is investigated commonly in the work, see, for example [16,43,44]. The design parameters are chosen as follows: r 1 = 10, r 2 = 10, η 1 = 5, η 2 = 10.…”

“…Although in [23,42], adaptive MTN-based approaches for nonlinear systems have been proposed, they are only investigated the nonlinear systems without considering the existence of input delay. Authors in [43] only addressed the tracking control problem for nonlinear systems with input delay without dynamic uncertainties. The mentioned above adaptive control approaches cannot solve the input delay and dynamic uncertainties problems simultaneously.…”

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

“…The MTN has been applied to the control problems of nonlinear 1 School of Mathematics and Physics, Qingdao University of Science and Technology, China 2 systems due to its excellent approximation ability and many valuable results have been obtained in recent years (Han et al, 2021a(Han et al, , 2021bYan et al, 2018b;Zhu et al, 2020). Base on the above researches, the method was also generalized to the control problem of stochastic nonlinear systems (Han, 2020a(Han, , 2020bYan, 2018, 2020;Yan et al, 2018a). For example, for a class of stochastic nonlinear systems with input dead-zone, proposed a MTN control method via backstepping technique.…”

“…For example, for a class of stochastic nonlinear systems with input dead-zone, proposed a MTN control method via backstepping technique. Han (2020aHan ( , 2020b) also studied the control problems of a class of stochastic nonlinear systems with input saturation constraints and input delay, respectively. Some novel MTNbased adaptive control strategies were proposed.…”

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

“…22–24 Despite that many meaningful achievements have been obtained for nonlinear stochastic systems, these approximation-based control methods have some innate shortcomings. 25,26 For the NNs, the long training time leads to the poor real-time performance, and the selection of the NN structure is considered as a fuzzy and experiential process. For the FLSs, no applicable general techniques are available for stability analysis.…”