This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

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This paper presents a hybrid input shaping method to eliminate residual vibration of multi-mode flexible systems. This method, initially designed for one degree of systems, is modified to apply on linear and nonlinear multi-mode systems. In this method, firstly the flexible system is uncoupled using modal analysis method, and then the parameters of the decoupled system are used to shape the command template signal. A ramp plus ramped cycloid plus ramped versine is proposed as the command template signal to be preshaped. The template function is preshaped to yield zero residual vibration for point to point motion and then the resulting trajectory is convolved with a sequence of two impulses to obtain a twice shaped input. The proposed method is applied to eliminate residual vibration of a linear multimass and flexible joint manipulator types of systems. Simulation results show that the oscillations are considerably decreased with a high degree of robustness in the presence of system parameters uncertainty.

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