A Discrete Neuro Sliding Mode Control with Adaptive Switching Gain for Uncertain Nonlinear Systems

Raja, Ben Mohamed; Houda, Romdhane; Dehri, Khadija; Said, Nouri Ahmed

doi:10.1109/ssd.2018.8570562

Cited by 3 publications

(6 citation statements)

References 16 publications

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“…If D sd denotes the surge displacement given by AUV path planning, e surge is the deviation of surge displacement and expressed as e surge = D sd − D s (9) then switching surface of variable structure control for surge motion can be described as S 1 =ė surge + C surge e surge (10) where C surge is a positive constant. If exponential approaching mode is adopted and expressed aṡ…”

Section: Auv Path Tracking Controller Design a Auv Dynamic Modelmentioning

confidence: 99%

“…In view of these negative impacts, the original method of variable structure control needs to be modified in order to weaken chattering effect. The advantage of neuron lies in its self-learning and adaptive ability [10]. If approach speed of variable structure control is adjusted online through self-learning of adaptive neuron, the chattering problem in variable structure control system can be solved, which is significant for further improving the control performance of AUV path tracking.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV

Wang

Pan

2020

IEEE Access

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In order to improve path tracking performance of Beaver AUV, a new method for path tracking controller design is proposed basing on variable structure control theory and nonlinear dynamics model of Beaver. Switching surface of variable structure controllers is founded with corresponding error and error derivative of surge displacement and yaw angle. Exponential approach law is adopted to derive control algorithm for surge and yaw in the process of path tracking. Chattering phenomenon is restrained due to adaptive neurons which are used for approaching speed adjusting online. Simulation results of sinusoidal path tracking, circular path tracking and planning path tracking show that tracking precision of Beaver AUV is improved more evidently through variable structure control with adaptive approaching speed compared with variable structure control using fixed approaching speed. The method proposed in this article is effective and feasible. INDEX TERMS Autonomous underwater vehicle, neuron, path tracking, variable structure control.

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Section: Auv Path Tracking Controller Design a Auv Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV

Wang

Pan

2020

IEEE Access

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“…Recently, several robust hybrid methods have been developed for improving the robust performance of system in the presence of system uncertainties. In [14,15], a new adaptive discrete SMC control is employed for uncertain nonlinear systems. To guarantee the stability of the system, a neuron online estimation of the errors in the sliding function is proposed.…”

Section: Introductionmentioning

confidence: 99%

Robust model reference adaptive PI controller based sliding mode control for three-phase grid connected photovoltaic inverter

Moeti¹,

Asadi²

2021

Turk J Elec Eng & Comp Sci

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In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two stage grid connected photovoltaic (PV) inverter. Perturb & observe (P&O) algorithm is implemented in the boost part in order to extract the maximum power from the PV array. Firstly, the current dynamics with considering the system uncertainties are written in dq frame and are simplified by employing a decoupling system. Then, the MRAC-PI controller is designed based on a sliding mode control (SMC) to improve the robustness of the controller under system uncertainties. The parameters of PI controller are tuned based on SMC laws to tracks the desired reference model and provides fast and desirable dynamic response. The stability of the robust MRAC-PI is proven by using Lyapunov's theory and Barbalet's lemma. Finally, the performance of the proposed current controller and the conventional PI based voltage oriented control (VOC) during variations in filter inductance and solar irradiance are compared through simulation results. These results show the effectiveness of the proposed controller.

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“…In addition, almost all the control algorithms need to be implemented by computers, so it is more meaningful to study the control of discrete-time nonlinear systems. There are many control methods for discrete-time nonlinear systems, such as adaptive control (Mohammadpour and Tavasoli, 2019), predictive control (Hu and Ding, 2019), and sliding mode control (Ghabi and Dhouibi, 2018; Raja et al, 2018; Tsai et al, 2018; Xiong et al, 2019), to name just a few. Among them, discrete-time sliding mode control (DSMC) has been widely applied and studied owing to its strong robustness, quick response and simple design.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the latest researches are much more abundant. Raja et al (2018) adopts a new discrete neuro sliding mode control with adaptive switching gain for uncertain nonlinear systems to reduce the chattering. Ghabi and Dhouibi (2018) proposes a disturbance estimation scheme to enhance robustness and applies it to a cart-inverted pendulum system.…”

Section: Introductionmentioning

confidence: 99%

Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

Cao

Gao

Zhao

2020

Transactions of the Institute of Measurement and Control

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This paper proposes a data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems. In this scheme, the control design does not depend on the mathematical model of the controlled system. The nonlinear system can be transformed into a dynamic linear data system by a novel dynamic linearization method. A recursive learning control algorithm is designed for the nonlinear system that can drive the sliding variable reach and remain on the sliding surface only by using output and input data. Moreover, the chattering is reduced because there is no non-smooth term in MFSMLC. After the strict stability analysis, the effectiveness of MFSMLC is validated by MATLAB simulations.

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A Discrete Neuro Sliding Mode Control with Adaptive Switching Gain for Uncertain Nonlinear Systems

Cited by 3 publications

References 16 publications

Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV

Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV

Robust model reference adaptive PI controller based sliding mode control for three-phase grid connected photovoltaic inverter

Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

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