Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy

Abstract

In this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basis function neural network (RBF NN) control is used to approximate the unknown nonlinear function in the system and the tracking error of the controller is limited to a small constant boundary by using the positive obstacle Lyapunov function. An adaptive controller for a class of pure-feedback systems is established, which based on the backstepping control theory and event-triggered control theory, it can ensure all the closed-loop signals are bounded and avoid the Zeno-behavior. The simulation results prove the effectiveness of the controller design.INDEX TERMS Pure-feedback nonlinear systems, event-triggered control, output constraints, RBF neural network.

Event-triggered adaptive multi-dimensional Taylor network tracking control for stochastic nonlinear systems

“…Remark 4: Compared with the event-triggered adaptive NNs controllers in the work by Li and Yang (2018), Wang et al (2019Wang et al ( , 2021b, and Zhang et al (2020), the event-triggered adaptive MTN controller designed in this paper can further alleviate the computational burden due to the simple structure of MTN, thus saving the consumption of network resource to a greater extent.…”

“…In response to the above problem, three event-triggered schemes were proposed for uncertain nonlinear systems in the work by Xing et al (2017), in which the controllers were designed based on event-triggered mechanism. Then, this controller design method has been developed for different systems, such as strict-feedback nonlinear systems (Li and Yang, 2018; Wang and Li, 2021), non-strict-feedback nonlinear systems (Hu et al, 2021; Wang et al, 2021a), pure-feedback nonlinear systems (Zhang et al, 2020), multi-input multi-output nonlinear systems (Wang et al, 2021b), and stochastic nonlinear systems (Liu et al, 2018; Wang et al, 2019; Xia et al, 2021). To our knowledge, there have been few reports on studying event-triggered adaptive control of stochastic nonlinear systems so far, especially it has not been reported under the framework of MTN approximation.…”

“…From the work by Han (2018) and Zhu et al (2020aZhu et al ( , 2020b, it is known that the controllers based on MTN can alleviate the computational burden. Therefore, compared with the event-triggered adaptive NNs controllers in the work by Li and Yang (2018), Wang et al (2019Wang et al ( , 2021b, and Zhang et al (2020), the controller designed in this paper can further alleviate the computational burden, thereby saving network resource to a greater extent.…”

Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

“…Therefore, the event‐triggered scheme was introduced, which can reduce the number of control tasks performed, avoid the unnecessary waste of communication resources and computing resources. Some nonlinear system controllers based on event‐triggered strategies are concerned in References 23‐28. Reference 23 concerned a finite‐time tracking control problem for a class of pure‐feedback nonlinear systems with event‐triggered strategy.…”

“…investigated the problem of a periodic event‐triggering mechanism for switched systems control under limited communication resources. An adaptive event‐triggered tracking controller was designed for a class of pure‐feedback nonlinear systems with output constraints in References 26,27 considered an event‐triggered controller for continuous‐time nonlinear systems 28 and studied the problem of fuzzy adaptive event‐triggered control for a class of pure‐feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Meanwhile, References 29‐31 are based on well‐designed event‐triggered control schemes for neutral‐type semi‐Markovian jump (SMJ) neural networks with partial mode‐dependent additive time‐varying delays (ATDs), semi‐Markov jump uncertain (SMJU) neutral‐type neural networks with distributed and additive time‐varying delays (TDs), and one class of neural networks with time‐varying delays (TDs) and Markov jump parameters (MJPs).…”