Variable Learning Rate LMS Based Linear Adaptive Inverse Control
نویسندگان
چکیده
Abstract. Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algorithm. A water box temperature control example is quoted in this paper. Simulation results are carried out and show that the adaptive inverse control with variable learning rate LMS is better than that with the fixed learning rate LMS algorithm and the Variable Step-size Normalized LMS algorithm.
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