RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to
Baoping Jiang1, Dongyu Liu1, Hamid Reza Karimi2
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Sensors (Basel, Switzerland)
|July 27, 2022
Summary
This study presents an adaptive neural network control for nonlinear dock cranes, addressing delays and disturbances. The method ensures robust and stable crane operation using passivity-based sliding mode control.
Area of Science:
- Control Engineering
- Robotics
- Artificial Intelligence
Background:
- Nonlinear systems, such as dock cranes, often face challenges like time-varying delays, external disturbances, and unknown nonlinearities.
- Effective control strategies are crucial for ensuring the stability and performance of these complex systems.
Purpose of the Study:
- To develop a robust passivity-based sliding mode control (SMC) strategy for nonlinear dock crane systems.
- To address system uncertainties including time-varying delays, external disturbances, and unknown nonlinearities.
- To enhance control performance using an adaptive neural network approach.
Main Methods:
- Establishment of the crane system's mathematical model using the generalized Lagrange formula.
- Design of an integral-type sliding surface and application of equivalent control theory to derive a sliding mode dynamic system.
- Development of an adaptive control law based on Radial Basis Function (RBF) neural networks to handle unknown nonlinearities.
- Formulation of linear matrix inequality (LMI) conditions for analyzing the passification performance of the sliding motion.
Main Results:
- A sliding mode dynamic system with satisfactory dynamic properties was obtained.
- An adaptive control law was designed to ensure finite-time sliding motion despite unknown nonlinearities.
- Feasible LMI conditions were developed for analyzing passification performance.
- Simulation studies confirmed the effectiveness of the proposed control method.
Conclusions:
- The proposed adaptive neural network-based passivity-based sliding mode control is effective for nonlinear dock crane systems.
- The method successfully addresses time-varying delays, external disturbances, and unknown nonlinearities.
- The developed control strategy ensures robust and stable operation, validated through simulations.
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