用模糊控制与分布式补偿提升轮式机器人运动稳定性。
Optimizing Control Strategies for Wheeled Mobile Robots Using Fuzzy Type I and II Controllers and Parallel Distributed Compensation
- 采用类型II模糊控制器结合分布式补偿，增强系统鲁棒性。
- 实验表明类型II模糊控制器优于类型I和传统PID控制器。
- 适合机器人控制、智能驾驶等需要高精度路径规划的场景。
为实现轮式移动机器人在高精度、柔顺运动应用中的平滑轨迹控制，本文基于模糊理论与平行分布式补偿（PDC）设计了一种鲁棒控制器。研究对比了模糊类型I与类型II控制器性能，并与经典PID控制器进行比较。实验结果表明，类型II模糊控制器在抑制振荡、提升运动平滑性方面表现更优。通过引入非线性区域分段与局部近似策略，结合线性矩阵不等式（LMI）分析，实现了对每个模糊规则独立稳定性的验证。该方法有效管理了机器人运动学模型中的不确定性传播。此外，本文提出使用贝塞尔曲线（Bezier curve）表示机器人不同路径轨迹，便于规划与优化。
原文摘要 · Abstract (English)
Adjusting the control actions of a wheeled robot to eliminate oscillations and ensure smoother motion is critical in applications requiring accurate and soft movements. Fuzzy controllers enable a robot to operate smoothly while accounting for uncertainties in the system. This work uses fuzzy theories and parallel distributed compensation to establish a robust controller for wheeled mobile robots. The use of fuzzy logic type I and type II controllers are covered in the study, and their performance is compared with a PID controller. Experimental results demonstrate that fuzzy logic type II outperforms type I and the classic controller. Further, we deploy parallel distributed compensation, sector of nonlinearity, and local approximation strategy in our design. These strategies help analyze the stability of each rule of the fuzzy controller separately and map the if-then rules of the fuzzy box into parallel distributed compensation using Linear Matrix Inequalities (LMI) analysis. Also, they help manage the uncertainty flow in the equations that exist in the kinematic model of a robot. Last, we propose a Bezier curve to represent the different pathways for the wheeled mobile robot.
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