[1]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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Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 5 Page number: 602-607 Column: 学术论文—智能系统 Public date: 2014-10-25
Title:
Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm
Author(s):
TUO Shouheng;  DENG Fang’an;  YONG Longquan
School of Mathematics and Computer Science, Shaanxi University of Technology, XI’an 723000, China
Keywords:
teaching-learning-based optimization algorithm; LQR controller; optimal control; active suspension; particle swarm optimization; genetic algorithm
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201304071
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.
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