初态偏移非线性系统跟踪控制的迭代学习算法

Iterative Learning Algorithms for Tracking Control of Nonlinear Systems with Initial Offset

基于迭代学习控制理论提出一种新的可变增益学习控制算法,并对系统初态的迭代学习律进行了讨论,利用算子理论证明在非线性系统中存在初态偏移时经过迭代学习后,输出仍能跟踪期望轨迹;对其收敛性进行数学证明,得到谱半径形式的迭代学习律收敛的充分条件,从而解决了可变学习增益迭代学习控制的初态偏移问题。最后在注塑机注射速度控制系统中与增益不可变迭代学习算法进行比较,仿真结果验证了算法的有效性。

An iterative learning control of variable gain is propsed based on iterative learning control theory,which increases iterative learning rule of the initial states of systems.By using the operator theory,it is proved that the output of the system can track the expected trajectory after the iterative learning of the nonlinear system with an initial state offset.The convergence is proved and the convergent condition for the spectral radius of the algorithm is given.Compared with the invariable gain iterative learning control of an injection molding machine the simulation results show the effectiveness of the proposed algorithm.