Adaptive neural network control for uncertain dual switching nonlinear systems

Article Computer Science, Theory & Methods

Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

Jianhui Wang et al.

Summary: A fuzzy adaptive event-triggered finite-time constraint control method is proposed for uncertain nonlinear systems with input hysteresis. By limiting the communication resources, the system convergence is achieved in finite time and the bandwidth resources can be effectively saved.

FUZZY SETS AND SYSTEMS (2022)

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Article Automation & Control Systems

Resilient Adaptive Neural Control for Uncertain Nonlinear Systems With Infinite Number of Time-Varying Actuator Failures

Kaixin Lu et al.

Summary: Existing studies on adaptive fault-tolerant control for uncertain nonlinear systems with actuator failures are restricted to the establishment of system stability. This article proposes a new resilient adaptive neural control scheme to improve the bounded error control to asymptotic control, by introducing a new design of the Lyapunov function candidates, a projection-associated tuning functions method, and an alternative class of smooth functions.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

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Article Computer Science, Theory & Methods

Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

Jianhui Wang et al.

Summary: This study develops a fuzzy adaptive event-triggered finite-time constraint control method for uncertain nonlinear systems with input hysteresis, aiming to overcome the impact of uncertainties and nonlinear characteristics on the performance and convergence time of actual control systems. By using limited communication resources, the system convergence in finite time is ensured.

FUZZY SETS AND SYSTEMS (2022)

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Article Automation & Control Systems

An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems

Jianhui Wang et al.

Summary: An adaptive neural sliding mode control with ESO is proposed to improve the stability of control systems. By combining sliding mode control and ESO, the system shows superior tracking performance and anti-interference ability in simulations.

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS (2021)

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Article Engineering, Aerospace

Adaptive neural network control of nonlinear systems with unknown dynamics

Lin Cheng et al.

Summary: An adaptive neural network control approach is proposed in this study to achieve accurate and robust control of nonlinear systems with unknown dynamics. The study focuses on system transformation, control learning algorithm, and development of an adaptive neural network controller. Simulation results substantiate the effectiveness of the proposed techniques and controller's robustness.

ADVANCES IN SPACE RESEARCH (2021)

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Article Computer Science, Artificial Intelligence

Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks

Ivan Lopez-Sanchez et al.

Summary: This document presents the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor using a GRNN. The proposed scheme successfully estimates disturbances and unmodeled dynamic effects without requiring accurate knowledge of quadrotor parameters, leading to improved performance in trajectory tracking.

NEUROCOMPUTING (2021)

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Article Automation & Control Systems