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Daxiong Ji

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2 papers

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IROS Conference 2025 Conference Paper

Reservoir Computing-Enhanced Tube-MPC: Real-Time Self-Healing Control for Robust AUV Path Following Under Dynamic Faults

Lie Xu 0002
Daxiong Ji
Yan Zhi Tan
Eng Wei Goh
Marcelo H. Ang

This paper presents a novel control framework that integrates reservoir computing (RC) with Tube model predictive control (Tube-MPC) for robust path following in quadrotor autonomous underwater vehicles (QAUVs) under sudden fault conditions. The proposed RC-Tube-MPC leverages the dynamic modeling capabilities of RC to efficiently approximate complex nonlinear behaviors, while Tube correction ensures robust performance despite model uncertainties and external disturbances. Comparative simulations demonstrate that RC-Tube-MPC outperforms alternative approaches in terms of path following accuracy and computational efficiency. Additionally, the influence of training data length on learning performance is analyzed, revealing that the proposed method maintains superior performance across various data regimes. Notably, in severe fault scenarios, such as a fault factor of 0. 3, RC-Tube-MPC uniquely restores convergence to the reference path. These results underscore the potential of the integrated RC-Tube-MPC approach for real-time control applications in dynamic, fault-prone underwater environments.

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ICRA Conference 2017 Conference Paper

A prey-predator model for efficient robot tracking

Fengzhen Tang
Bailu Si
Daxiong Ji

Tracking is a common topic in various areas of robotics research. Motivated by the hunting behavior of predators in nature, we propose a prey-predator model for efficient robot tracking. The head direction and speed of the pursuer is automatically adjusted according to the position and velocity of the prey. Under the situation with perception uncertainty, where the actual location of the prey is not observable, the pursuer predicts the location of the prey according to simple inference, an online adaptive autoregressive model, or an online adaptive echo state network. Simulation results demonstrate that the proposed prey-predator model is able to control the pursuer and to track the prey efficiently, even under perception uncertainty. Simple inference gives better results when the motion of the target is piecewise linear, while echo state network is more suitable when the dynamics of the target are more complex. The proposed prey-predator model thus provides an efficient method tracking targets with various statistical nature of trajectories for applications such as underwater robot tracking, human tracking and team formation.

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