An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems
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ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems Jianhui Wang*, Peisen Zhu, Biaotao He, Guiyang Deng, Chunliang Zhang*, and Xing Huang Abstract: An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability. Keywords: Adaptive control, backstepping, extended state observer(ESO), neural network, sliding mode.
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INTRODUCTION
In practice, any control system inevitably exists uncertain disturbances, which arise from parametric uncertainties and the complex and changeable environments. The stability of the control system is severely influenced by uncertain disturbances. For example, the working performance of robots is greatly influenced by external disturbances [1], quadrotor unmanned aerial vehicles (UAVs) are extremely subjected to uncertain disturbances torque [2], parallel robots are strongly affected by exogenous disturbances [3]. In recent years, uncertain disturbances compensation has received considerable attention as the development of high-accuracy control [4–12]. Some scholars used the extended state observer to solve the control problem of underwater robots [4]. Using an Extended State Observer (ESO) to track both the uncertain disturbances and the state of control system [5]. Some scholars designed an output feedback sliding mode strategy to perfect the control accuracy of the inertial stabilization platform
[6]. Nevertheless, the control effect of the above methods for dealing with systems included nonlinear uncertainties is not efficient enough. In addition, many of the control theory studies use precise models. Selecting accurate models can reduce the complexity of theoretical analysis and facilitate the design of effective control methods. Nevertheless, in practical engineering applications, the control system must have nonlinear uncertainties which greatly affect the stability of the system [13]. Thus, many scholars devoted to the study of non-linear problems [14–20]. An adaptive fuzzy control method was utilized to ensure that the signals of the control system are bounded [21]. An adaptive fuzzy controller was
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