Research On Iterative Learning Control Algorithm

ILC ( Iterative Learning Control ) theory is one important branch of the intelligent control theory, and is applied to the controlled systems with properties of repetitive motion, such as industrial robot, numerical control machine, power system, etc.. ILC corrects control signals continuously with the deviation between output signal and a given target to improve the tracking performance by iterative control on the charged system.The main works are shown as follows:1. For a nonlinear control system, a new class of ILC algorithm is proposed and proved, based on vector plots analysis of the control process in the different situations that may arise and depended on the space angles between the amendment of the actual input of the previous control and the amendment of general P-type algorithm control. Simulation shows the algorithm is available.2. For a nonlinear control system, the ILC combined with other intelligent control, especially with fuzzy control, is researched, and a design of the ILC with fuzzy factor based on the vector plots analysis about the nonlinear systems is proposed. Simulation show the effectiveness of the method.3. For a class of singular linear systems with state and output disturbance, the condition of robust convergence for ILC is discussed under the action of closed-loop PD-type ILC algorithm. The boundedness of the tracking error of the systems is proved under boundedness of state and output disturbance, and output of the systems can track the desired trajectory asymptotically if all the disturbances are disappeared.4. For a class of discrete systems, a new ILC algorithm with two adjusting parametersαandβ, based on vector plots analysis, is proposed and convergence of the new algorithm is proved. The new algorithm is entirely different from the other algorithms and its convergent speed could be raised up by adjusting parameters.