This paper combines a new structure of artificial neural networks (ANNs) with a 3rd-order numerical algorithm and proposes an improved hybrid method for solving forward kinematics problem (FKP) of parallel manipulators. In this method, an approximate solution of the FKP is first generated by the neural network. This solution is next considered as an initial guess for the 3rd-order numerical technique which solves the nonlinear forward kinematics equations and obtains the answer with a desired level of accuracy. To speed up the method, a new structure is proposed for designing the ANN which is called Same Class One Network. In this structure, the outputs of the ANN are classified into classes of similar variables with an individual network designed for each class. The proposed method is then applied to a planar 3-RPR parallel manipulator and a spatial 3-PSP parallel robot. The results show that using this method will lead to a 55% reduction in required iterations and a 20% reduction in the FKP analysis time, while maintaining a high level of solution accuracy.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -This paper aims to overcome some of the practical difficulties in assistive control of exoskeletons by developing a new assistive algorithm, called output feedback assistive control (OFAC) method. This method does not require feedbacks from force, electromyography (EMG) or acceleration signals or even their estimated values. Design/methodology/approach -The presented controller uses feedbacks from position and velocity of the output link of series elastic actuators (SEAs) to increase the apparent integral admittance of the assisted systems. Optimal controller coefficients are obtained by maximizing the assistance ratio subjected to constraints of stability, coupled stability and a newly defined comfort measure. Findings -The results confirm the effectiveness of using the inherent properties of SEAs for removing the need for extra controversial sensors in assistive control of 1 degree of freedom (1-DOF) SEA powered exoskeletons. The results also clearly indicate the successful performance of the OFAC method in reducing the external forces required for moving the assisted systems. Practical implications -As the provided experiments indicate, the proposed method can be easily applied to single DOF compliantly actuated exoskeletons to provide a more reliable assistance with lower costs. This is achieved by removing the need for extra controversial sensors. Originality/value -This paper proposes a novel assistive controller for SEA-powered exoskeletons with a simple model-free structure and independent of any information about interaction forces and future paths of the system. It also removes the requirement for the extra sensors and transforms the assistive control of the compliantly actuated systems into a simpler problem of position control of the SEA motor.