Un-modeled Dynamics Increment Compensation Driven Nonlinear PID Control and Its Application
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摘要: 针对一类具有强非线性、机理不清且动态特性随不同运行条件而变化的复杂过程, 将基于数据的建模技术与基于模型的控制策略相结合, 提出了未建模动态及其未知增量补偿驱动的非线性PID控制方法. 所提的算法将一步超前最优控制策略应用于PID控制器的参数设计, 并结合非线性补偿技术进行综合设计, 从理论上给出了PID控制器参数以及非线性补偿器设计的一般原则和方法, 为解决传统PID控制器参数难于整定的问题提供了方法和途径. 在此基础上, 分析了闭环系统的稳定性和收敛性. 最后, 将所提的控制算法进行数值仿真实验以及Pendubot系统平衡控制的对比实验, 实验结果表明, 在Pendubot的精确摩擦力模型未知的情况下, 所提算法能有效地消除系统未知时变不确定性的影响, 并尽可能地减少Pendubot摆角的波动, 将摆角控制在规定的目标值范围内.Abstract: For a class of complex industrial process whose structure is unclear and the dynamic characteristics changing strongly with different operating conditions, unmodeled dynamics driven nonlinear PID control method is proposed in this paper, the algorithm combined the data modelling technologies and control strategy based on process model and is applied to the Pendubot balance control system. One step ahead of the optimal control strategy is used to design the parameters of the PID controller, which combined with the nonlinear compensation technology for integrated design. The general principle and method of choosing PID controller parameters and nonlinear compensator design are given theoretically, which provides ways and means to solve the problem that the traditional PID controller parameters are difficult to design. Then, the stability and convergence of the closed-loop system are analyzed. Finally, through the numerical simulation and the comparative experiment on Pendubot balance control system, the results show that the proposed algorithm can effectively eliminate the influence of the unknown time-varying uncertainty of the system when the accurate friction model of Pendubot is unknown, and reduce Pendubot angular fluctuations as far as possible, the swing angle is controlled within the specified target value range.
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Key words:
- Data driven /
- increment of unmodeled dynamics /
- PID controller /
- stability /
- Pendubot
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表 1 性能评价
Table 1 Performance indexes
绝对误差累积和 误差均方差 文献[30] 23 396.5 2.7 本文方法 8 156.1 1.8 表 2 性能评价
Table 2 Performance indexes
绝对误差累积和 误差均方差 常规PD 361.1 6.5 文献[30] 337.3 6.1 本文方法 204.3 4.2 -
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