Abstract
This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with time-varying disturbances. While the fuzzy logic systems (FLSs) approximate the unknown dynamics, an event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of “algebraic loop,” seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of “jumps of virtual control laws” arising in the backstepping-based event-triggered control (ETC). The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. Estimation of optimal fuzzy weights and compound disturbances follows from the event-triggered update laws. While the satisfactory learning performance is achieved, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment verifies the effectiveness of the proposed control scheme.
Highlights
Plenty of industrial plants can be described by nonstrictfeedback nonlinear systems, such as the ball and beam system, the spring damper system, the remote manipulator, and the stirred tank reactor [1]
The problem To be distinguished from the composite learning, the of “complexity explosion” to differentiate the virtual thought to identify both the unknown dynamics and the control laws was avoided by introducing the filters in time-varying disturbances was named as “compound
It was integrated the reinforcement learning to the backstep- observed from the above literature that both the comping approach of the discrete nonstrict-feedback sys- pound learning and the composite learning were never tem, such that the suboptimal control performance can achieved in the event-triggered manner
Summary
Plenty of industrial plants can be described by nonstrictfeedback nonlinear systems, such as the ball and beam system, the spring damper system, the remote manipulator, and the stirred tank reactor [1]. To exempt the restrictive assumptions of the unknown dynamics in these literature, [2] fabricated the novel backsteppingbased control laws and parameter adaptation laws, in which the bounded property of fuzzy basis functions was utilized This method was applied to the switched nonstrict-feedback systems in [1,10], and combined with the finite-time control in [11]. By using the semirecurrent neural network- learning” in [30] While reviewing these literature, we s (NNs) as the one-step predictors, [12] designed the found that the compound learning was never applied to backstepping-based control laws for the discrete nonstrict- the nonstrict-feedback system. It was integrated the reinforcement learning to the backstep- observed from the above literature that both the comping approach of the discrete nonstrict-feedback sys- pound learning and the composite learning were never tem, such that the suboptimal control performance can achieved in the event-triggered manner
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