Dynamic surface control for a class of stochastic non‐linear systems with input saturation

Saturated robust adaptive control for uncertain non‐linear systems using a new approximate model

“…Remark 11: It is worth noting that, in this example, the upper bound of |g 1 | = 1 + 0.1x 1 2 is not easy to establish. Therefore, some existing DSC schemes proposed in [37][38][39][40][41][42][43][44][45] may be not able to design controller for system (41). On the contrary, from the above simulation results, a nice tracking performance is provided by using the proposed control scheme.…”

“…Remark 7: In most existing DSC schemes for non-linear systems preceded by input saturation [37][38][39][40][41][42][43][44][45], the coupling terms g i z i e i in (36) are decoupled to |g i | z i 2 and |g i | e i 2 by using Young's inequality, the corresponding surface gains k i and the time constants τ i are always constrained by the upper bounds of |g i |, therefore, the upper bounds of |g i | are required to be a priori known for control design. However, in the proposed approach here, with the utilisation the modified linear filters, the coupling terms g i z i e i are completely cancelled, which deduces that the assumption on the upper bounds of |g i | is not required.…”

“…In [43], the authors addressed adaptive control for a class of MIMO non-affine pure-feedback systems with input saturation and time delays, an adaptive neural DSC approach was obtained by integrating mean value theorem and the auxiliary system. To handle the effects of input saturation and stochastic disturbance, an adaptive DSC approach was proposed for a class of stochastic nonlinear systems using the existing dead zone-based model of saturation [44]. For interconnected non-linear systems in the presence of input saturation, a decentralised neural DSC method was presented via a novel truncated adaptation design [45].…”

Observer‐based adaptive fuzzy dynamic surface control of non‐linear non‐strict feedback system

“…Moreover, Zhai et al [27] presented a robust adaptive fuzzy tracking control approach and Sung et al [28] proposed a robust stabilisation method via the DSC technique for the uncertain non-linear systems with unknown time delays. In addition, Yan et al [29] extended the results to stochastic nonlinear systems. Note that the systems in [23][24][25][26][27][28] are all in a pure feedback or strict feedback form.…”

Adaptive neural tracking control for near-space vehicles with stochastic disturbances

“…Furthermore, some meaningful results were presented for stochastic nonlinear systems on the basis of the quartic Lyapunov function in the literature. [43][44][45][46][47][48][49][50][51][52][53][54][55] It is well known that mechanical systems are often subjected to stochastic disturbances, which significantly affect the performance. By reasonably introducing random noise, a method to construct stochastic Lagrangian control systems was given and an adaptive tracking controller was designed by Cui et al 56 Using geometric stochastic feedback control, a robust asymptotic stabilization of rigid body attitude dynamics with stochastic input torque was studied by Samiei et al 57 In earlier study, 58 a new robust stochastic control methodology was developed for unmanned aerial vehicles.…”