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An improved generalized predictive control algorithm based on the difference equation CARIMA model for the SISO system with known strong interference

机译:一种改进的基于具有已知强烈干扰的SISO系统差分式卡米模型的推广预测控制算法

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摘要

The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC.
机译:具有已知强烈干扰的单个输入单输出(SISO)系统被广泛用于各种场合。由于其强烈干扰,控制精度难以保证。为了解决这个问题,开发了一种改进的广义预测控制(IGPC)算法。 IGPC首先构建SISO系统的差分等式CARIMA(受控自动回归集成移动平均)模型,然后将系统视为两个输入单输出(TISO)系统,并计算其预测向量,然后将其转换为SISO系统并使用TISO系统预测向量计算SISO系统控制增量。添加了一个名为相位数的新参数以禁止控制滞后。进行仿真以进行传统GPC,PID控制,速度同步控制(VSC),模糊自适应PID控制(FAPID),基于模型的鲁棒PID控制(BPID)和IGPC的仿真。结果表明,与其他人相比,IGPC具有最佳性能。最后,开发了实验,证明了IGPC算法在SISO系统中具有更高的精度,具有已知的强干扰而不是VSC的系统。

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