带有初态学习的可变增益迭代学习控制

Iterative learning control of variable gain with initial state study

针对一类非线性系统提出一种新的学习控制算法,该算法在可变学习增益的迭代学习控制律基础上,增加了系统初态的迭代学习律.利用算子理论证明了系统在存在初态偏移时经过迭代学习后,其输出能够完全跟踪期望轨迹,同时得到了该算法谱半径形式的收敛条件.将该算法与传统迭代学习控制相比较可以看出,前者的收敛速度得到了较大提高,而且解决了可变学习增益迭代学习控制的初态偏移问题.仿真结果验证了该算法的有效性.

For a class of nonlinear systems, a new learning control algorithm is proposed, which increases iterative learning rule of initial state of system on the base of iterative learning control with variable learning gain. By using the operator theory, it is proved that the output of system can track the expected trajectory completely after the iterative learning of system with initial state disturbance, and the convergent condition for the spectral radius form of the algorithm is given. Compared with the tranditional iterative learning control, the proposed algorithm not only significantly improves the convergent speed, but also solves the initial state disturbance problem of the iterative learning control with variable learning gain. Simulation results show the effectiveness of the proposed algorithm.