Robust adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems with unknown control directions

Doudou, Sofiane; Khaber, Farid

doi:10.1177/01423312211015114

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Since Nussbaum proposed the concept of Nussbaum gain control, this method has been widely used in control design. For different systems and control objectives, designers usually combined Nussbaum gain control with other different control technologies to design controllers to achieve control purposes [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

Neural network control of fractional-order chaotic systems with unknown control direction

Wang,

Chen

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Neural network control of fractional-order chaotic systems with unknown control direction

Wang,

Chen

2024

Heliyon

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“…In the past few decades, since nonlinear terms inevitably appeared in practical engineering field, how to solve this problem has attracted extensive attention. In this case, fuzzy logic systems (FLS) (Li et al, 2014; Ling et al, 2019; Liu et al, 2016a; Ma and Ma, 2020) and neural networks (NNs) (Bai et al, 2019; Doudou and Khaber, 2021; Li et al, 2019; Zerari et al, 2018) were presented to approximate uncertain parameters or unknown functions. In practice, limited by the complexity of the system itself and some external conditions, the state variables of the system are often immeasurable or partly measurable only.…”

Section: Introductionmentioning

confidence: 99%

Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

Liu

2023

Transactions of the Institute of Measurement and Control

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In this article, the issue of adaptive event-triggered tracking control is investigated for time-delay nonlinear systems with input saturation and external disturbances. In the whole process of control design, the radial basis function neural networks are utilized to approximate uncertain nonlinearities. To estimate the unknown states, a neural network-based observer is constructed. Pade approximation method is adopted to eliminate the effect of input delay. A smooth non-affine function is introduced to replace input saturation, and an auxiliary variable is employed to obtain the actual control input. An event-triggered strategy is designed to reduce the utilization of communication and computation resources. Moreover, the command filtering technique is applied to handle the issue of “explosion of complexity” in the conventional backstepping method. The designed controller can assure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Therefore, the proposed event-triggered neural control scheme can not only save network resources, but also improve the robustness of the system by dealing with input constraints and external disturbances. Finally, two simulation examples are given to verify the availability and feasibility of the designed controller.

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“…Despite existing numerous studies on non-linear control in the presence of unknown dead zone (Shahriari-Kahkeshi and Rahmani, 2019), unknown direction of control gain (Doudou and Khaber, 2021) and a combination both of them (Shojaei et al, 2018; Zhang et al, 2013), an observer-based self-organized fuzzy neural network has not been investigated practically.…”

Section: Introductionmentioning

confidence: 99%

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

Mawlani

Arbabtafti

2022

Transactions of the Institute of Measurement and Control

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In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case II is limited to [−1.5, 1.5] and [−1,1]mm, respectively.

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Robust adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems with unknown control directions

Cited by 6 publications

References 29 publications

Neural network control of fractional-order chaotic systems with unknown control direction

Neural network control of fractional-order chaotic systems with unknown control direction

Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

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