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Cucker-Smale Flocking With Inter-Particle Bonding Forces
Jaemann Park,Kim, H J,Seung-Yeal Ha IEEE 2010 IEEE transactions on automatic control Vol.55 No.11
<P>The Cucker-Smale (CS) flocking model is an interacting particle system, in which each particle updates its velocity by adding to it a weighted average of the differences of its velocity with those of other particles. It has been shown that by using the C-S model, the velocities of particles converge to a common value despite the absence of a central command. In this note, we make an extension of the C-S model by introducing additional interaction terms between agents which we refer to as the inter-particle bonding force, in order to incorporate collision avoidance between agents, and at the same time achieve tighter spatial configurations. The proposed inter-particle bonding force makes use of position and velocity information of other agents in order to achieve such separation and cohesion. With the inter-particle bonding forces and the velocity-alignment term of the original C-S model, we show the emergent behavior of asymptotic flocking to spatial equilibrium configurations.</P>
Two distributed guidance approaches for rendezvous of multiple agents
Jaemann Park,Je Hyun Yoo,H. JinKim 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
Consensus refers to the agreement upon some specific variable between the agents of the system. In this paper, we adopt consensus techniques in order to derive distributed guidance laws which make multiple agents rendezvous at a desired target point. Such rendezvous maneuver can be applied to a salvo attack of multiple missiles, or cooperative surveillance of multi-UAVs. We propose two distributed rendezvous methods that use different consensus variables, namely the distance-to-go and time-to-go, in order to derive the guidance laws. We show the effectiveness of the proposed methods through numerical simulations.
Jaemann Park,Bongju Lee,H. Jin Kim 한국산업응용수학회 2013 한국산업응용수학회 학술대회 논문집 Vol.8 No.1
Control of nonlinear systems require some explicit representation of the nominal plant dynamics. However, for highly complex systems obtaining such mathematical form may not be possible. In these situations, researchers have applied online learning techniques in order to achieve the control objective. Echo state networks (ESN), that are a class of recurrent neural networks, have provided promising results in this online learning area. However, the online learning of ESNs has been restricted to using recursive least squares (RLS) which has its own drawbacks such as numerical instability due to round-off errors, slow tracking capability for time-varying parameters, and high sensitivity to initial conditions of the algorithm. In this work, we investigate various algorithms that can be used for the online training of ESNs. The algorithms are evaluated numerically based on the tracking performance for a given reference trajectory and their computational complexity.
모델 예측 기법 기반 무인 항공기의 편대 비행 제어 알고리즘
박재만(Jaemann Park),신종호(Jongho Shin),김현진(Hyoun Jin Kim) 제어로봇시스템학회 2008 제어·로봇·시스템학회 논문지 Vol.14 No.12
This paper studies the feasibility of using the nonlinear model predictive control as a formation flight control algorithm for unmanned aerial vehicles. The optimal control inputs for formation flight are calculated through the cost function which incorporates the relative positions of the individual vehicles to maintain a desired formation and also the inequality constraints on inputs and states using the Karush-Kuhn-Tucker conditions. In the nonlinear model predictive control setting, the optimal control inputs are implemented in a receding horizon manner, which is suitable for dealing with dynamic constraints. Numerical simulations are executed for the validation of the proposed scheme.
Online Learning Control of Hydraulic Excavators Based on Echo-State Networks
Park, Jaemann,Lee, Bongju,Kang, Seonhyeok,Kim, Pan Young,Kim, H. Jin IEEE 2017 IEEE transactions on automation science and engine Vol.14 No.1
<P>Note to Practitioners-Motivated by the fact that obtaining useful mathematical models of hydraulic excavators may be impractical or too costly, this paper proposes an online learning control technique for the position control of hydraulic excavators. The proposed control technique uses remote control valve (RCV) signals and measurements of the joint angles to learn the dynamics of the excavator in an online manner, and the RCV inputs required to track the desired trajectory are generated simultaneously. As a result of online learning, the controller compensates for the changes in the plant dynamics over time, caused by factors, such as fluid temperature change or component wear. In this paper, we have implemented and validated the proposed controller on a 21-ton class hydraulic excavator. The proposed online learning control framework can also be applied to a wide range of control applications, where a mathematical model of the plant is absent or impractical to obtain.</P>