In this paper, we present an iterative learning method of compensating for position sensor errors. Unlike the previously known compensation algorithms, the method presented does need a special perfect position sensor or a priori information about error sources. To the best of our knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function itself rather than its special time-trajectory and does not require the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give a rigorous proof for its convergence and some experimental results.
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