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Optimization of formation for multi-agent systems based on LQR

Frontiers of Information Technology & Electronic Engineering 2016年第2期17卷 96-109页

作者： Chang-bin YU Yin-qiu WANG Jin-liang SHAO School of Automation Hangzhou Dianzi University Hangzhou 310018 China Research School of Engineering The Australian National University Canberra ACT 0200 Australia School of Mathematics Science University of Electronic Science and Technology of China Chengdu 611731 China

In this paper,three optimal linear formation control algorithms are proposed for first-order linear multiagent systems from a linear quadratic regulator(LQR) perspective with cost functions consisting of both interaction energy cost and individual energy cost,because both the collective ob ject(such as formation or consensus) and the individual goal of each agent are very important for the overall ***,we propose the optimal formation algorithm for first-order multi-agent systems without initial physical *** optimal control parameter matrix of the algorithm is the solution to an algebraic Riccati equation(ARE).It is shown that the matrix is the sum of a Laplacian matrix and a positive definite diagonal ***,for physically interconnected multi-agent systems,the optimal formation algorithm is presented,and the corresponding parameter matrix is given from the solution to a group of quadratic equations with one ***,if the communication topology between agents is fixed,the local feedback gain is obtained from the solution to a quadratic equation with one *** equation is derived from the derivative of the cost function with respect to the local feedback *** examples are provided to validate the effectiveness of the proposed approaches and to illustrate the geometrical performances of multi-agent systems.

关键词： Linear quadratic regulator (LQR) Formation control Algebraic Riccati equation (ARE) optimalcontrol Multi-agent systems

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Steering control for underwater gliders

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Frontiers of Information Technology & Electronic Engineering 2017年第7期18卷 898-914页

作者： You LIU Qing SHEN Dong-li MA Xiang-jiang YUAN School of Aeronautic Science and Engineering Beihang University Beijing 100191 China Institution of China Academy of Aerospace Aerodynamics Beijing 100074 China

Steering control for an autonomous underwater glider （AUG） is very challenging due to its changing dynamic char- acteristics such as payload and shape. A good choice to solve this problem is online system identification via in-field trials to capture current dynamic characteristics for control law reconfiguration. Hence, an online polynomial estimator is designed to update the yaw dynamic model of the AUG, and an adaptive model predictive control （MPC） controller is used to calculate the optimal control command based on updated estimated parameters. The MPC controller uses a quadratic program （QP） to compute the optimal control command based on a user-defined cost function. The cost function has two terms, focusing on output reference tracking and move suppression of input, respectively. Move-suppression performance can, at some level, represent energy-saving performance of the MPC controller. Users can balance these two competitive control performances by tuning weights. We have compared the control performance using the second-order polynomial model to that using the filth-order polynomial model, and found that the tbrmer cannot capture the main characteristics of yaw dynamics and may result in vibration during the flight. Both processor-in-loop （PIL） simulations and in-lake tests are presented to validate our steering control performance.

关键词： Autonomous underwater glider (AUG) Online system identification Steering control Adaptive control optimalcontrol Energy saving control Processor-in-loop (PIL)

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