Publications

This article studies the coordination control problem of networked manipulators, where manipulators are actuated to cooperatively transport an unknown object along a prescribed trajectory. It is assumed that the accurate position and velocity information of the trajectory is known only to a subset of manipulators. A two-layer control scheme consisting of an estimation layer and a control layer is developed. At the estimation layer, prescribed-time distributed estimators are designed to estimate the position and velocity of the trajectory. The estimation errors are shown to converge to zero in pre-specified time. At the control layer, an event-driven distributed controller with a dynamic triggering mechanism is proposed, where nonuniform time-varying communication delays are considered. A distinctive feature of the proposed controller is that both control updates and communication occur at certain event instants, and thus control update burden and communication costs are reduced. Moreover, two adaptive time-varying control gains are designed so that the controller can be implemented without requiring any inequality condition or global information. Finally, a simulation platform based on MATLAB Robotics Toolbox is designed and implemented to verify the effectiveness and superiority of the proposed two-layer control scheme.

Feed interruptions in intelligent feeders seriously disrupt production continuity, leading to insufficient feed intake and triggering competitive feeding behavior among pigs. In current research on intelligent feeders, the monitoring of remaining feed predominantly relies on manual methods, indicating a lack of more effective automated monitoring approaches. However, the electrical signals generated during equipment operation possess rich representational capabilities and have been widely applied to guide production. Inspired by this, a two-stage remaining feed weight prediction model based on voltage and current signals is constructed in this study. The model leverages the ability of wavelet transform to simultaneously extract time-domain and frequency-domain features from electrical signals, enabling it to capture both short-term fluctuations and long-term trends. In the first stage, wavelet features are used to predict the feed fines ratio, which serves as an input for the subsequent stage, significantly enhancing the accuracy of remaining feed weight prediction. In the second stage, a transformer-based model incorporating time–frequency attention mechanisms and uncertainty analysis is employed to output the remaining feed weight and its corresponding confidence interval. This study quantifies the contribution of different wavelet features to the prediction results by performing wavelet feature importance ranking and SHAP mean value ranking, thereby enhancing the model's interpretability. Additionally, uncertainty analysis was conducted on the model. The results demonstrate a high agreement between the predicted intervals and actual values, and a significant positive correlation was observed between prediction uncertainty and actual errors. Comparative experiments show that the WaveResNet outperforms both DNN and ResNet models, achieving an MAE of 1.4950, R M S E of 2.6467, and an R 2 of 0.9296. The WaveTransformer model outperforms conventional deep learning models—including RNN, LSTM, GRU, TCN, and Transformer—achieving an MAE of 1.6663, an R M S E of 2.3719, and an R 2 of 0.9574. The model demonstrates lightweight architecture and low latency during practical deployment, effectively meeting the real-time requirements of pig farm applications. • A novel two-stage model named WRN-WTF is proposed for predicting the remaining feed weight in intelligent feeders. • The Wavelet transform is applied to decompose electrical signals and extract time–frequency features. • The feed fines ratio is used as an input feature to enhance prediction accuracy. • Uncertainty analysis is incorporated to quantify the model's predictive confidence. • WRN-WTF outperforms similar models and has significant practical applicability.

The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are grouped into leaders and followers. Under this framework, the controllability of the controlled network can be decomposed into two independent problems: the controllability of the isolated leader subsystem and the controllability of the extended follower subsystem. Some necessary and/or sufficient conditions for the controllability of the leader-follower network are derived based on matrix theory and graph theory. In particular, it is shown that a single-leader network is controllable if it is a directed path or cycle, but it is uncontrollable for a complete digraph or a star digraph in general. Furthermore, some approaches to improving the controllability of a heterogenous network are presented. Some simulation examples are given for illustration and verification.

SUMMARY This paper is concerned with the problem of consensus in directed networks of multiple agents with intrinsic nonlinear dynamics and sampled‐data information. A new protocol is induced from a class of continuous‐time linear consensus protocols by implementing data‐sampling technique and a zero‐order hold circuit. On the basis of a delayed‐input approach, the sampled‐data multi‐agent system is converted to an equivalent nonlinear system with a time‐varying delay. Theoretical analysis on this time‐delayed system shows that consensus with asymptotic time‐varying velocities in a strongly connected network can be achieved over some suitable sampled‐data intervals. A multi‐step procedure is further presented to estimate the upper bound of the maximal allowable sampling intervals. The results are then extended to a network topology with a directed spanning tree. For the case of the topology without a directed spanning tree, it is shown that the new protocol can still guarantee the system to achieve consensus by appropriately informing a fraction of agents. Finally, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results and the dependence of the upper bound of maximal allowable sampling interval on the coupling strength. Copyright © 2012 John Wiley & Sons, Ltd.

The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higher-order interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an early-warning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the cross-border portfolio investment network of long-term debt securities, the proliferation of a wide range of over-the-counter-traded financial derivative products, such as credit default swaps, can be described in terms of gross-market values and notional outstanding amounts, which are associated with increased levels of market interdependence and systemic risk. Third, considering the flow-network of goods traded between G-20 economies, network statistics provide better proxies for key economic measures than conventional indicators. For example, it is shown that a country's gate-keeping potential, as a measure for local power, projects its annual change of GDP generally far better than the volume of its imports or exports.