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.

Zhen Bi, Ningyu Zhang, Yida Xue, Yixin Ou, Daxiong Ji, Guozhou Zheng, Huajun Chen. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Data-driven fault diagnosis methods suffer from restrictive assumptions that hinder adaptability to varying work conditions, rendering offline modes insufficient. Addressing this challenge, a bioinspired spike neural network (bio-SNN) is proposed, featuring an innovative online learning mode. The network employs a novel spike encoding method with data compression for efficiently transforming time series data into spike sequences. This encoding method involves converting 1-D series data into a 2-D spectrogram using a filter bank, incorporating a stochastic spike rate for a more flexible representation compared to precise spike rates. The application of a biologically plausible learning rule, specifically spike timing-dependent plasticity (STDP), enhances the adaptability of the network. A horizontal inhibition and homeostasis mechanism are also introduced, facilitating effective online updating of synaptic weights. Experimental results on two well-established fault datasets showcase the advantages of the bio-SNN method over existing approaches, highlighting its potential for robust and adaptive fault diagnosis in practical scenarios.

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data , and motor imagery electroencephalogram (EEG) data demonstrate the superior performance of the proposed method.

The problem of designing waveform and quantisation thresholds is studied in a multistatic radar setting, where distributed receivers are connected to a fusion centre via capacity constraints. Different from the previous cloud radio‐multistatic radar system which utilises an additive quantisation Gaussian noise model, a real quantisation system is designed. The authors first optimise the waveform without quantisation. Then they compress the received signal at receivers into a scalar without any performance degradation. Furthermore, the scalar quantiser is adopted and quantisation thresholds are designed. Numerical simulations are performed and the effectiveness of combined waveform and thresholds optimisation strategy is demonstrated.

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.