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

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

Writing to Think

The purpose of this volume is to honor the work and thought of Robert C. Rubel, Captain, U.S. Navy (Ret.). Since his retirement from the Navy, Robert (a.k.a. “Barney”) Rubel has held senior positions in the Center for Naval Warfare Studies (CNWS), in the Naval War College, in Newport, Rhode Island—first as deputy dean, then as chairman of the War Gaming Department, and finally (since 2006) as dean. During this period, not only has he presided effectively over a complex (and in many ways anomalous) institution, but he has found the time to create a substantial body of published writings about naval warfare and war, or strategy generally. In the process, he has quietly established himself as one of the Navy’s most innovative and wide-ranging thinkers.https://digital-commons.usnwc.edu/usnwc-newport-papers/1040/thumbnail.jp