用遗传算法优化机器人臂滑模控制参数，提升轨迹跟踪精度与抗干扰能力。
Optimization of sliding control parameters for a 3-dof robot arm using genetic algorithm (GA)
- 用遗传算法自动搜索最优滑模控制参数
- 仿真显示轨迹跟踪更准，颤振现象明显减弱
- 适合需要高精度控制的机械臂系统设计
本文提出一种基于遗传算法（GA）优化滑模控制（SMC）参数的方法，用于三自由度机器人机械臂。在不确定和干扰条件下，SMC的目标是实现精确稳定的轨迹跟踪，但其性能高度依赖于参数选择，而人工调参困难且关键。为此，采用遗传算法自动寻找满足性能指标的最优参数组合。仿真结果表明，相比传统SMC和模糊滑模控制（Fuzzy-SMC），该方法显著提升了轨迹跟踪能力，并有效抑制了系统颤振现象。
原文摘要 · Abstract (English)
This paper presents a method for optimizing the sliding mode control (SMC) parameter for a robot manipulator applying a genetic algorithm (GA). The objective of the SMC is to achieve precise and consistent tracking of the trajectory of the robot manipulator under uncertain and disturbed conditions. However, the system effectiveness and robustness depend on the choice of the SMC parameters, which is a difficult and crucial task. To solve this problem, a genetic algorithm is used to locate the optimal values of these parameters that gratify the capability criteria. The proposed method is efficient compared with the conventional SMC and Fuzzy-SMC. The simulation results show that the genetic algorithm with SMC can achieve better tracking capability and reduce the chattering effect.
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