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汽车ACC系统算法仿真研究 被引量:6
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作者 熊坚 王秀圣 刘丁 《重庆交通大学学报(自然科学版)》 CAS 北大核心 2017年第9期108-114,共7页
巡航车辆的跟随特性和安全车距控制是ACC控制系统算法的核心。ACC算法采用分层式控制结构,以线性二次型算法为上层控制器核心,根据传感器测得车辆信息进行运算,通过间距控制策略和控制算法得到期望加、减速度;下层控制器以逆车辆动力学... 巡航车辆的跟随特性和安全车距控制是ACC控制系统算法的核心。ACC算法采用分层式控制结构,以线性二次型算法为上层控制器核心,根据传感器测得车辆信息进行运算,通过间距控制策略和控制算法得到期望加、减速度;下层控制器以逆车辆动力学为基础,以MATLAB为平台建立节气门和制动力控制模型,得到期望节气门开度和制动力,实现车辆加速、减速运动控制。通过MATLAB和Car Sim联合仿真方式进行仿真实验,对经典线性二次型算法、改进线性二次型算法、综合线性二次型算法3种算法的间距策略进行仿真研究对比。对比3种算法的优缺点,并进行"走-停"工况进行验证。实验结果表明:改进后的算法更适合车辆自适应巡航特点,具有良好的平顺性与安全性,尤其较紧急制动工况下,停车平稳,提高了乘员舒适性与安全性。 展开更多
关键词 车辆工程 汽车ACC 线性二次型控制算法 仿真实验 车辆安全
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Optimization of formation for multi-agent systems based on LQR 被引量:4
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作者 Chang-bin YU Yin-qiu WANG Jin-liang SHAO 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2016年第2期96-109,共14页
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 interact... 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 system.First,we propose the optimal formation algorithm for first-order multi-agent systems without initial physical couplings.The 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 matrix.Next,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 unknown.Finally,if the communication topology between agents is fixed,the local feedback gain is obtained from the solution to a quadratic equation with one unknown.The equation is derived from the derivative of the cost function with respect to the local feedback gain.Numerical examples are provided to validate the effectiveness of the proposed approaches and to illustrate the geometrical performances of multi-agent systems. 展开更多
关键词 Linear quadratic regulator (LQR) Formation control Algebraic Riccati equation (ARE) OPTIMALCONTROL Multi-agent systems
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