Titlebook: Real-time Iterative Learning Control; Design and Applicati Jian-Xin Xu,Sanjib K. Panda,Tong Heng Lee Book 2009 Springer-Verlag London 2009

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Introduction to ILC: Concepts, Schematics, and Implementation,ve basic configurations of ILC for linear processes, including the previous cycle learning, current cycle learning, previous and current cycle learning, cascade learning, and incremental cascade learning. Next, we focus on ILC for non-linear processes, make clear two conditions intrinsic to ILC appl

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ILC for Process Temperature Control: Application to a Water-heating Plant,chieved through filter-based iterative learning control. The design of the ILC parameters is conducted in the frequency domain, which guarantees the convergence property in the iteration domain. The filter-based ILC can be easily added on to existing control systems. To clearly demonstrate the featu

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ILC for Electrical Drives: Application to a Switched Reluctance Motor,uted among the phases using a torque-sharing function. Following that, the phase torque references are converted to phase current references by a torque-to-current converter and the inner current control loop tracks the phase current references. SRMtorque is a highly non-linear and coupled function

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Calibration of Micro-robot Inverse Kinematics Using Iterative Learning Approach,nverse kinematics. Based on the iterative learning theory, the new identificationmechanism is able to approximate the actual parameters iteratively using previous estimates and corresponding kinematics mapping. A novel property of the new identification method is that, by using only one precise cali