任意初始状态下非正则系统的迭代学习控制设计

迭代学习控制已广泛应用于各种机器人控制系统,但目前的方法大多数都假设系统具有零初始误差。在实际工程应用中,迭代学习的初始状态往往会发生漂移,现有的学习算法不能正确地使用。针对具有非零初始误差的非正则线性离散系统,研究了其迭代学习算法,提出了两种新型的初始状态的学习方法,利用2 D系统理论,对迭代学习进行了2 D分析,以保证所提出算法的稳定性。由于不需要假设系统初始误差为零,该算法更符合工程实际,仿真验证了算法的有效性。

Iterative learning control design for irregular linear system with an arbitrary initial state

Iterative learning control techniques are widely applied to robotic systems. Most of the proposed algorithms can be only used when the initial learning error are zero. However, in practical engineering, the initial learning states will shift from the desired initial states. As a result, it is necessary to develop some new learning algorithms to deal with such situations. Two new-type initial state learning schemes are developed for irregular linear discrete-time system with non-zero initial error. Based on 2-D system theory, iterative learning control algorithms for arbitrary initial state are analyzed on 2-D notion. The proposed algorithms can be applied to practical situations without assumption of zero initial error for dynamic systems. The simulation results show that the proposed algorithms are very effective.