This article proposes a betterment process for the operation of a mechanical robot in a sense that it betters the next operation of a robot by using the previous operation's data. The process has an iterative learning structure such that the (k + 1)th input to joint actuators consists of the kth input plus an error increment composed of the derivative difference between the kth motion trajectory and the given desired motion trajectory. The convergence of the process to the desired motion trajectory is assured under some reasonable conditions. Numerical results by computer simulation are presented to show the effectiveness of the proposed learning scheme.
A systematic and iterative method of computing the capacity circumstances, it is exponentially decreasing. Finally, a few inequalities of arbitrary discrete memoryless channels is presented. The algorithm is that give upper and lower hounds on the capacity are derived. very simple and involves only logarithms and exponentials in addition to elementary arithmetical operations. It has also the property of mono
A new approach to the dynamic control of manipulators is developed from the viewpoint of mechanics. It is first shown that a linear feedback of generalized coordinates and their derivatives are effective for motion control in the large. Next, we propose a method for task-oriented coordinate control which can be easily implemented by a micro-computer and is suited to sensor feedback control. The proposed method is applicable even when holonomic constraints are added to the system. Effectiveness of the proposed method is verified by computer simulation.
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