Fuzzy Adaptive PID Control of Large Aperture Fast Steering Mirror
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摘要: 快速反射镜能否精确稳定跟踪目标取决于良好的伺服控制性能。快速反射镜的通光口径越大,柔性支撑铰链和驱动器设计难度就越大,同时也会对伺服控制提出更高的要求。针对此问题,本文提出模糊自适应整定PID(proportional integral derivative)控制算法,该算法既能运用模糊推理进行自适应整定控制参数,又能继承传统PID控制器便于工程实现。本文对音圈电机(voice coil motor)驱动的ϕ500 mm大口径快速反射镜进行控制器设计且进行仿真实验,并将其结果与基于传统PID控制下的相比较。结果表明,基于模糊自适应整定PID控制的ϕ500 mm大口径快速反射镜的超调量为5.40%,调节时间51.0 ms,且抗干扰能力强于传统PID控制。此外,与传统PID控制相比,本文提出的控制方法提高了ϕ500 mm大口径快速反射镜的响应速度,减小了跟踪误差,提升了ϕ500 mm大口径快速反射镜系统的跟踪性能和鲁棒性。Abstract: The ability of a fast-steering mirror (FSM) to track a target accurately and steadily depends on its servo control performance. The larger the aperture of the FSM is, the more difficult it is to design the flexible supporting hinge and the driver; in addition, it will also demand greater requirements on the servo control. To solve this problem, this paper proposes a fuzzy adaptive tune(FAT) proportional integral derivative (PID) control algorithm, which not only uses fuzzy theory for adaptively tuning the control parameters, but also inherits the classic PID controller for engineering realization. In this study, we designed a controller for the ϕ500 mm FSM driven by a voice coil motor, conducted simulation experiments, and compared the results with the simulation results based on classic PID control. According to the results, the overshoot was 5.4%, the settling time was 51.0 ms based on FAT PID control, and the capacity of resisting disturbance was stronger than that of the classical PID control. In addition, compared with traditional PID control, the proposed control method improved the ϕ500 mm FSM response speed, decreased the tracking error, and improved ϕ500 mm FSM system tracking performance and robustness.
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Key words:
- fast steering mirror /
- large aperture /
- voice coil motor /
- fuzzy control /
- adaptive tuning PID
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表 1 数学模型的参数定义
Table 1. The parameter definition of the mathematical model
Symbol Parameter L VCM inductance R VCM internal resistance Ke Back EMF coefficient Kt Force sensitivity Kc Flexible hinge elastic coefficient Ka Amplification coefficient M Load mass c System damping coefficient 表 2 Kp的模糊规则表
Table 2. Fuzzy rules table of Kp
Kp ec NB NM NS Z PS PM PB e NB PB PB PM PM PS Z Z NM PB PB PM PS PS Z NS NS PM PM PM PS Z NS NS Z PM PM PS Z NS NM NM PS PS PS Z NS NS NM NM PM PS Z NS NM NM NM NB PB Z Z NM NM NM NB NB 表 3 Ki 的模糊规则表
Table 3. Fuzzy rules table of Ki
Ki ec NB NM NS Z PS PM PB e NB NB NB NM NM NS Z Z NM NB NB NM NS NS Z Z NS NB PM NS NS Z PS PS Z NM NM NS Z PS PM PM PS NM NS Z PS PS PM PB PM Z Z PS PS PM PB PB PB Z Z PS PM PM PB PB 表 4 Kd的模糊规则表
Table 4. Fuzzy rules table of Kd
Kd ec NB NM NS Z PS PM PB e NB PS NS NB NB NB NM PS NM PS NS NB NM NM NS Z NS Z NS NM NM NS NS Z Z Z NS NS NS NS NS Z PS Z Z Z Z Z Z Z PM PB NS PS PS PS PS PB PB PB PM PM PM PS PS PB 表 5 控制性能对比
Table 5. The comparison of control performance
Controller Control performance Transition process Overshoot/(%) Settling time/ms Raising time/ms Peaking time/ms Classic PID Dampled oscillation 7.10 112.0 35.5 81.0 Fuzzy PID Dampled oscillation 5.40 51.0 12.8 40.0 -
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