论文标题
优化具有改良PSO算法的3-DOF直升机的自适应模糊逻辑控制器
Optimizing an Adaptive Fuzzy Logic Controller of a 3-DOF Helicopter with a Modified PSO Algorithm
论文作者
论文摘要
本文研究了具有三个自由度（3-DOF）的直升机系统的控制器优化。为了控制系统，我们将模糊逻辑与自适应控制理论相结合。该系统广泛非线性，对控制器的参数高度敏感，这是研究这些参数对控制器性能的影响的真正挑战。使用元启发式算法来确定这些参数是一个有前途的解决方案。本文建议使用修改的粒子群优化（MPSO）算法来优化控制器。该算法显示出执行全局搜索并找到合理搜索空间的高能力。该算法根据其适应性函数值修改每个粒子的搜索空间，并将弱粒子替换为新粒子。这些修改导致了更好的准确性和收敛速度。在优化3-DOF直升机的自适应模糊逻辑控制器时，我们通过将MPSO算法与标准PSO和其他六种众所周知的元启发式算法进行比较来证明MPSO算法的效率。在系统受到不确定性和干扰的同时，通过计算机模拟显示了所提出的方法的有效性。我们通过比较MPSO和标准PSO优化控制器时的结果来证明该方法的优势。
This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). To control the system, we combined fuzzy logic with adaptive control theory. The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effect on the controller's performance. Using metaheuristic algorithms for determining these parameters is a promising solution. This paper proposes using a modified particle swarm optimization (MPSO) algorithm to optimize the controller. The algorithm shows a high ability to perform the global search and find a reasonable search space. The algorithm modifies the search space of each particle based on its fitness function value and substitutes weak particles for new ones. These modifications have led to better accuracy and convergence rate. We prove the efficiency of the MPSO algorithm by comparing it with the standard PSO and six other well-known metaheuristic algorithms when optimizing the adaptive fuzzy logic controller of the 3-DOF helicopter. The proposed method's effectiveness is shown through computer simulations while the system is subject to uncertainties and disturbance. We demonstrate the method's superiority by comparing the results when the MPSO and the standard PSO optimize the controller.
