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In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain,...
It is well known that it is necessary to store the system input and output of previous trial for generating the input at the current trial in the design of iterative learning controllers. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data iterative learning controller is presented in this paper...
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