Segment-wise learning control for trajectory tracking of robot manipulators under iteration-dependent periods

This paper is concerned with the amplitude boundedness problem of adaptive iterative learning control (AILC) for robot manipulators operating with iteration-dependent periods. By introducing virtual memory slots for storing historical data, a practical AILC method is proposed to achieve the segment-wise learning. This method requires less memory storage for historical information of previous iterations, especially in comparison with that of the conventional AILC methods using point-wise learning strategies. It is shown that not only the energy boundedness but also the amplitude boundedness of estimates and inputs of practical AILC can be guaranteed. Moreover, the practical AILC method can achieve the perfect tracking objective regardless of iteration-dependent periods when the robot manipulators have a persistent full learning property. In addition, a solution to the visual manipulator platform is provided and deployed based on Coppeliasim and Matlab, which helps to show the amplitude boundedness of learning results and the perfect tracking performances of the proposed practical AILC method for robot manipulators.