Iterative learning control (ILC) is a novel control arithmetic, which doesn't depend on the precise model. It can generate input signal and reduce error through repeating learning so that the output of the system can approximate to the expectation. The research of ILC is more significant for solving control precision problem with highly nonlinear, complexity and difficulty in modeling.In this dissertation, firstly basic knowledge about iterative learning control is introduced, including its history, mathematic description and some common ILC schemes etc. And the convergence conditions for open-loop, closed-loop and open-closed-loop ILC schemes are discussed separately. Especially, the author gives some simulation examples between different ILC schemes. Finally, the integrated control method is used in this paper, which the parameters of iterative controller are optimized by neural network (NN), so the iterative control law can be optimized accordingly. Fixed learning gain will make the learning speed slower and the iterative times more. Neural network can reduce the huge computing burden and fasten the convergent rate of right value largely compared with least square method. So the algorithm that the parameters of controller are optimized by NN is adopted in this paper. This method not only improves disturbance and robustness of the controlled system, but also fully exerts the intellectualized virtue of ILC without precise model.The optimized control method is applied nonlinear system. The simulation result indicates, compared ILC process with the fix learning gain, the method used in this paper is better with less iterative double-quicker convergence and more high tracking precision, and it assures that the output trajectory can track the anticipant trajectory precisely with less iterative times. |