arXiv subjects

Si Kheang Moeurn

Publications and source records attributed to Si Kheang Moeurn.

2 recordsLinked to original sources

arXiv2024

Multi Methods of Matrix Analysis Use for Control and Optimization system in Control Engineering

Matrix analysis plays a crucial role in the field of control engineering, providing a powerful mathematical framework for the analysis and design of control systems. This research report explores various applications of matrix analysis in control engineering, focusing on its contributions to system modeling, stability analysis, controllablity, observability, and optimization. The report also discusses specific examples and case studies to illustrate the practical significance of matrix analysis in addressing real-world control engineering challenges Analyze controllability. Informally, a system is controllable if we can construct a set of inputs that will drive the system to any given state. Analyze observability. Informally, observability means that by controlling the inputs and watching the outputs of a system we can determine what the states were. Optimal Control is a control method that aims to find the optimal control input to achieve the best performance of the system under certain constraints. This performance index can be the system output, energy consumption, time, etc.

math.OC↗

arXiv2024

Distributed Cooperative Formation Control of Nonlinear Multi-Agent System (UGV) Using Neural Network

The paper presented in this article deals with the issue of distributed cooperative formation of multi-agent systems (MASs). It proposes the use of appropriate neural network control methods to address formation requirements (uncertainties dynamic model). It considers an adaptive leader-follower distributed cooperative formation control based on neural networks (NNs) developed for a class of second-order nonlinear multi-agent systems and neural networks Neural networks are used to compute system data that inputs layer (position, velocity), hidden layers, and output layer. Through collaboration between leader-follower approaches and neural networks with complex systems or complex conditions receive an effective cooperative formation control method. The sufficient conditions for the system stability were derived using Lyapunov stability theory, graph theory, and state space methods. By simulation, the results of this study can be obtained from the main data of the multi-agent system in formation control and verified that the system can process consistency, stability, reliability, and accuracy in cooperative formation.

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