摘要
针对一类线性时不变广义系统,在基于Lebesgue-p范数的意义下,利用卷积的推广Young不等式,对开环D型以及开环PD型迭代学习算法的收敛性进行了严格的分析。理论分析表明,两种算法均可以实现在有限时间内系统实际输出对理想输出的跟踪。同时通过实验仿真进一步验证了理论分析的正确性,并且指出由于开环P型算法的引入,加快了系统迭代学习的收敛速度,有效提升了系统的性能。
For a class of linear time-invariant generalized systems, based on the Lebesgue-p norm, the generalized Young’s inequality of convolution is used to rigorously study the convergence of open-loopn D-type and open-loop PDtype iterative learning algorithms. Theoretical analysis shows that both algorithms can track the actual output of the system to the ideal output in a limited time. At the same time, the correctness of the theoretical analysis is further verified by experimental simulation, and it is pointed out that the introduction of the open-loop P-type algorithm accelerates the convergence speed of the system iterative learning and effectively improves the performance of the system.
出处
《工业控制计算机》
2022年第10期95-97,共3页
Industrial Control Computer
基金
国家自然科学基金(批准号:11975023)。
