基于强化量子行为粒子群优化的多目标状态反馈控制器设计
Multi-Objective Design of State Feedback Controllers Using Reinforced Quantum-Behaved Particle Swarm Optimization
AI总结：
本文提出一种新颖的多目标设计方法，利用量子行为粒子群优化算法（QPSO）优化LQR控制器配置，结合模拟退火和高斯邻域选择机制改进初始化方案，引入动态加权准则生成Pareto最优解，通过实验验证其在控制性能上的优越性。
摘要：
本文提出了一种新颖且通用的多目标设计范式，利用量子行为粒子群优化（QPSO）算法确定给定问题中LQR控制器的最佳配置，考虑一组竞争性目标。本文的主要贡献包括：（1）基于模拟退火算法和高斯邻域选择机制的改进初始化方案；（2）结合膜计算算法优势的局部搜索策略；（3）引入动态加权准则，将软约束和硬约束与控制目标动态结合，为设计者提供一组Pareto最优解，并根据实际偏好选择目标解。所提方法在两个控制基准上通过敏感性分析和全因子参数选择与梯度法、七种元启发式算法和试错法进行比较，实验结果表明，所提方法在控制器努力、暂态响应指标和稳态相关指标上均优于对手方法。
英文摘要：
In this paper, a novel and generic multi-objective design paradigm is proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal configuration of the LQR controller for a given problem considering a set of competing objectives. There are three main contributions introduced in this paper as follows. (1) The standard QPSO algorithm is reinforced with an informed initialization scheme based on the simulated annealing algorithm and Gaussian neighborhood selection mechanism. (2) It is also augmented with a local search strategy which integrates the advantages of memetic algorithm into conventional QPSO. (3) An aggregated dynamic weighting criterion is introduced that dynamically combines the soft and hard constraints with control objectives to provide the designer with a set of Pareto optimal solutions and lets her to decide the target solution based on practical preferences. The proposed method is compared against a gradient-based method, seven meta-heuristics, and the trial-and-error method on two control benchmarks using sensitivity analysis and full factorial parameter selection and the results are validated using one-tailed T-test. The experimental results suggest that the proposed method outperforms opponent methods in terms of controller effort, measures associated with transient response and criteria related to steady-state.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
