Active fault-tolerant anti-input saturation control of a cross-domain robot based on a human decision search algorithm and RBFNN

Abstract

This article presents a cross-domain robot (CDR) that experiences drive efficiency degradation when operating on water surfaces, similar to drive faults. Moreover, the CDR mathematical model has uncertain parameters and non-negligible water resistance. To solve these problems, a radial basis function neural network (RBFNN)-based active fault-tolerant control (AFTC) algorithm is proposed for the robot both on land and water surfaces. The proposed algorithm consists of a fast non-singular terminal sliding mode controller (NTSMC) and an RBFNN. The RBFNN is used to estimate the impact of drive faults, water resistance, and model parameter uncertainty on the robot and the output value compensates the controller. Additionally, an anti-input saturation control algorithm is designed to prevent driver saturation. To optimize the controller parameters, a human decision search algorithm (HDSA) is proposed, which mimics the decision-making process of a crowd. Simulation results demonstrate the effectiveness of the proposed control methods.

An adaptive control framework based multi-modal information-driven dance composition model for musical robots

“…Driving the robot (steering, acceleration, etc.) through the obtained music information to express the elements conveyed by the music (Das et al, 2023 ; Wang et al, 2023 ). This method first obtains Musical Instrument Digital Interface (MIDI) data file data, segments the music file, clusters these blocks based on note blocks and turbulence values using a K-Means algorithm, and generates robot paths.…”