Cutting Process Model Design of Cutter Suction Dredger Based on Auto Regressive eXogenous and Radial Basis Function model

The dredging operation of the strander dredger is complex, and the mathematical model established according to its key equipment characteristics is not possible to describe such a system having time degeneration and non-linear. Therefore, based on the analysis of mud formation process of dredger, RBF-ARX model is used to model the cutting process, and mud concentration is taken as the output. This modeling method is a combination model based on the theory of Auto-Regressive eXogenous (ARX) model and Gauss radial basis function (Radial Basis Function) neural network (RBF). The comparison between the simulation results and the actual data shows that the model can accurately describe the dynamic characteristics of cutter suction dredger in the cutting process.

Rainfall-Runoff Models with Fractional Derivatives Applied to Kurau River Basin, Perak, Malaysia

Kurau River Basin (KRB), which covers an area of 322 km2 and is the main drainage artery pouring into Bukit Merah Reservoir (BMR), is located in Perak State of Malaysia. The study of rainfall-runoff processes in KRB is important because BMR plays a vital role in rice production, flood control, ecosystems, and tourism in the region. This study proposes a new approach to rainfall-runoff modeling based on the fractional calculus. A dataset of daily rainfall and streamflow has been acquired. Then, the standard linear autoregressive with exogenous input (ARX) model is identified from the dataset in the sense of least square error. We consider the ARX model as a discretized differential equation with fractional orders. Such a model with fractional derivatives is versatile to represent hysteresis, which is intrinsically linked to the real runoff processes in tropical catchment basins like KRB.

Event-Triggered-Based External Consensus Protocol of RBF-ARX-Model-Based Networked Multiagent Systems with Nonlinear Dynamics and Communication Delays

An event-triggered-based external consensus control protocol with delay compensation for networked multiagent systems (NMASs) with nonlinear dynamics and network-induced delays is discussed in this paper. An RBF-ARX modelling method is adopted to approximate a nonlinear system. By utilizing the RBF-ARX model, the locally linearized time series model can be obtained to describe the behaviour of agents with nonlinear characteristics. An event-triggered control protocol with communication delay compensation is proposed to reduce the effects of network-induced delays and the times of the agent’s control update and communication between others by applying the idea of a prediction strategy. The event-triggered mechanism for each agent only depends on the deviation between its own output and the delay-compensated output of neighboring agents. Then, a distributed event-triggered-based and delay-compensated external consensus control protocol is given. Under this proposed control protocol, both the consensus and stability of the system can be proved. Finally, a numerical simulation is performed to verify the availability of our results.