[1]拓守恒,邓方安,雍龙泉.改进教与学优化算法的LQR控制器优化设计[J].智能系统学报,2014,9(5):602-607.[doi:10.3969/j.issn.1673-4785.201304071]
TUO Shouheng,DENG Fangan,YONG Longquan.Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(5):602-607.[doi:10.3969/j.issn.1673-4785.201304071]
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TUO Shouheng,DENG Fangan,YONG Longquan.Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(5):602-607.[doi:10.3969/j.issn.1673-4785.201304071]
改进教与学优化算法的LQR控制器优化设计
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第5期
页码:
602-607
栏目:
学术论文—智能系统
出版日期:
2014-10-25
- Title:
- Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm
- Keywords:
- teaching-learning-based optimization algorithm; LQR controller; optimal control; active suspension; particle swarm optimization; genetic algorithm
- 分类号:
- TP18
- 摘要:
- 为了快速有效地确定线性二次最优控制(linear quadratic regulator, LQR)问题中的加权矩阵Q和R,针对主动悬架LQR控制器权系数设计问题,提出一种改进的教与学优化算法进行LQR优化设计。算法对基本教与学优化算法中的"教"与"学"阶段进行了进一步的改进,同时提出一种"自我学习"策略。通过仿真实验表明,和基本教与学算法、粒子群算法、遗传算法相比,本文算法在对主动悬架LQR控制器优化时,具有收敛速度快,求解精度高和稳定性强等优势。
- Abstract:
- To determine the weighting matrix Q and R for a linear quadratic regulator (LQR), a modified teaching-learning-based optimization (MTLBO) algorithm is proposed to tune weighting factors for active suspension LQR controller. The "Teaching" phase and "learning" phase are modified using MTLBO based on the basic TLBO algorithm. A novel "self-learning" strategy is employed in MTLBO. The simulation results showed that the MTLBO algorithm has distinct advantages in convergence, precision and stability than basic TLBO, PSO and genetic algorithms.
- 参考文献/References:
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- 相似文献/References:
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PENG Yong,CHEN Yuqiang.Optimal design of the LQR controller based on the improved shuffled frog-leaping algorithm[J].CAAI Transactions on Intelligent Systems,2014,9():480.[doi:10.3969/j.issn.1673-4785.201305055]
[2]吴迪,贾鹤鸣,刘庆鑫,等.融合经验反思机制的教与学优化算法[J].智能系统学报,2023,18(3):629.[doi:10.11992/tis.202112043]
WU Di,JIA Heming,LIU Qingxin,et al.Teaching and learning optimization algorithm based on empirical reflection mechanism[J].CAAI Transactions on Intelligent Systems,2023,18():629.[doi:10.11992/tis.202112043]
备注/Memo
收稿日期:2013-04-24。
基金项目:国家自然科学基金资助项目(11401357);陕西省教育厅基金资助项目(14JK1141);汉中市科技局基金资助项目(2013hzzx-39).
通讯作者:拓守恒, 男, 1978年生, 副教授, CCF会员, 主要研究方向为智能优化算法和生物信息学。E-mail:tuo_sh@126.com.
更新日期/Last Update:
1900-01-01