Adaptive neural network control for uncertain strict-feedback nonlinear systems with unknown control coefficients: A fully actuated system approach | IEEE Conference Publication | IEEE Xplore

Adaptive neural network control for uncertain strict-feedback nonlinear systems with unknown control coefficients: A fully actuated system approach


Abstract:

In this paper, an adaptive control scheme is designed for a class of strict-feedback nonlinear systems with unknown control coefficients. Unlike the existing iterative de...Show More

Abstract:

In this paper, an adaptive control scheme is designed for a class of strict-feedback nonlinear systems with unknown control coefficients. Unlike the existing iterative design approaches, we apply a fully actuated system approach to reduce the algorithmic complexity. First, the considered strict-feedback nonlinear systems are transformed into fully actuated systems by state transformations. After that, we compensate for the uncertainties arising from the unknown control coefficients and unknown nonlinear functions by the neural network approximation technique. Then, an adaptive neural network controller based on a fully actuated system approach is designed. According to Lyapunov stability analysis, it is shown that all the signals of the closed-loop system are bounded and the system states converge to a small neighborhood of the origin under the designed controller. Moreover, by further enhancing the adaptive neural network mechanism, this paper extends the relevant results to more general uncertain strict-feedback nonlinear systems with unknown coefficients. Finally, the effectiveness of the proposed control scheme is demonstrated by a practical application example.
Date of Conference: 10-12 May 2024
Date Added to IEEE Xplore: 23 July 2024
ISBN Information:
Conference Location: Shenzhen, China

Funding Agency:


1 Introduction

With the development of technology, many practical engineering systems can be transfomed into strict-feedback systems, such as battery energy storage systems[1] and wind turbine systems[2]. Therefore, the control design of strict-feedback systems has been a hot research direction[3]-[5]. However, the uncertainties arising from the unknown control coefficients make the control design of strict feedback systems extremely difficult. Thus, the study of strict-feedback systems with unknown control coefficients is both challenging and meaningful. For the unknown control coefficients, the reference [6] overcame the lack of information about the virtual control coefficients by utilizing the observation gain matrix determined by the convex combination technique. The reference [7] constructed an adaptive backstepping procedure based on the fuzzy approximation capability and variable partition technique to compensate for uncertainties. The reference [8] approximated an unknown nonlinear function of a system using fuzzy-logic systems and designed a novel Lyapunov function to eliminate the requirement of lower bounds of the unknown virtual control coefficients in control laws. However, most of the current studies of strict-feedback systems with unknown control coefficients are limited to a backstepping design approach, which makes the design process more complex when the model order is large. In the existing studies, the fully actuated system approach has not been utilized. To further simplify the controller design process, the fully actuated system approach will be applied in this paper.

References

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