Enhancing Underwater Robot Manipulators with a Hybrid Sliding Mode Controller and Neural-Fuzzy Algorithm

Abstract

The sliding mode controller stands out for its exceptional stability, even when the system experiences noise or undergoes time-varying parameter changes. However, designing a sliding mode controller necessitates precise knowledge of the object’s exact model, which is often unattainable in practical scenarios. Furthermore, if the sliding control law’s amplitude becomes excessive, it can lead to undesirable chattering phenomena near the sliding surface. This article presents a new method that uses a special kind of computer program (Radial Basis Function Neural Network) to quickly calculate complex relationships in a robot’s control system. This calculation is combined with a technique called Sliding Mode Control, and Fuzzy Logic is used to measure the size of the control action, all while making sure the system stays stable using Lyapunov stability theory. We tested this new method on a robot arm that can move in three different ways at the same time, showing that it can handle complex, multiple-input, multiple-output systems. In addition, applying LPV combined with Kalman helps reduce noise and the system operates more stably. The manipulator’s response under this controller exhibits controlled overshoot (Rad), with a rise time of approximately 5 ± 3% seconds and a settling error of around 1%. These control results are rigorously validated through simulations conducted using MATLAB/Simulink software version 2022b. This research contributes to the advancement of control strategies for robotic manipulators, offering improved stability and adaptability in scenarios where precise system modeling is challenging.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

“…(4) the equivalent gravity matrix is given by equation (11). (5) the underwater manipulator arm friction matrix is:…”

“…Liu et al in [10] proposed a sliding mode control method that combines a fuzzy controller and radial basis function neural network (RBFNN), addressing issues of inaccuracy, low speed, and severe chattering in robotic arm trajectory tracking. D A Pham et al in [11] combined radial basis function neural networks, sliding mode control, fuzzy logic, and Lyapunov stability theory in their design of a robotic control system. This approach provided higher stability and adaptability in scenarios where system modeling is challenging.…”

Development of an Adaptive Fuzzy-Neural Controller for Temperature Control in a Brick Tunnel Kiln

“…Based on the above literature review, the FLS is frequently chosen as an effective tool for system regulation addressed to various negative factors; for instance, an unmodeled dynamics system. Nevertheless, the FLS is designed depending on the experience of the engineer, which is not capable of self-learning proficiency [23][24][25][26]. In recent decades, neural networks (NN) have been extensively researched in control algorithms, possessing energetic self-learning capabilities.…”