Advanced particle swarm optimization-based PID controller parameters tuning

Abolfazl Jalilvand, A. Kimiyaghalam, A. Ashouri, M. Mahdavi
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引用次数: 25

Abstract

PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm optimization (APSO) is proposed in this paper. This new algorithm is proposed to augment the original PSO searching speed. This study proposes to use the (APSO) for its fast searching speed. These advanced particle swarm optimization to accelerate the convergence. The algorithms are simulated with MATLAB programming. The simulation result shows that the PID controller with (APSO) has a fast convergence rate and a better dynamic performance.
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基于先进粒子群优化的PID控制器参数整定
PID参数优化是控制领域的一个重要问题。粒子群优化算法是一种强大的随机进化算法,用于在搜索空间中寻找全局最优解。然而,已有研究发现,标准粒子群算法在求解复杂优化问题时存在过早收敛和局部收敛的现象。为了解决这一问题,本文提出了一种先进的粒子群优化算法。该算法是为了提高原有粒子群算法的搜索速度而提出的。由于APSO算法的搜索速度快,本研究建议采用该算法。这些先进的粒子群优化加速了收敛。用MATLAB编程对算法进行了仿真。仿真结果表明,该PID控制器具有较快的收敛速度和较好的动态性能。
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