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In this method, it is proposed that in a nonlinear dynamic system, a chaotic attractor can be formed by an appropriate usage of open-loop control until the system states converge within a specified area of attractive domain and then a state feedback control can be used to converge the system states to desired values. In the control of such systems, there are two phases of control. First phase uses an open-loop control such that the chaotic features attract the states to a desired area in the state space. Once the system has entered a specified area, open-loop control is cut off and the second phase of control is adopted. In the proposed second phase, an Fuzzy model-based control (FMC) is employed under state feedback control.
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Dynamics identification studies have been carried out using Runge-Kutta-Gill neural networks for a seven-link industrial robot manipulator called PA-10,manufactured by Mitsubishi heavy industries Ltd. Experimental data of the reference input of joint angle, its velocity, and their output data were used for the actual dynamics identification. Using the identified dynamics, a dynamic controller has been designed so that, for a given position and orientation vector of the tip of the manipulator, the controller computes a desired joint angular velocity vector that can be applied to the PA-10 standard servo controller without changing any hardware. This method can easily be extended for position-force control applications. Promising results are obtained to prove the ability of the proposed method.
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