Filter-type iterative learning control for distributed parameter system with variable tracking trajectory and sensor/actuator networks

The iterative learning control (ILC) problem of distributed parameter systems in sensor/actuator networks with random measurement noise and variable tracking trajectory is studied. A filter-type ILC algorithm is proposed in this paper. Filter-type ILC algorithm is based on the structure of system state and output equation to build an estimation equation, through which the system is directly controlled. Based on the basic mathematical analysis tools and Gronwall inequality, it is proved that the output error converges to mean square bounded under the control of filtered ILC algorithm. The effectiveness of filter-type ILC algorithm is verified by numerical simulation.