Neural-Network-Based Inverse Dynamic Solutions of an Asymmetric Parallel Robot

The dynamic models of parallel robots are more complicated due to the highly coupled kinematic chains compared with those of their counterparts-serial robot. The approximate dynamic models can be considered if the analytical solutions high cost in time due to the sophisticated calculations. To address this issue, this paper presents an alternative approach-the dynamic model based on the neural network. A three degrees-of-freedom parallel manipulator is introduced, followed by a detailed mathematical inverse dynamic model, which is replicated by three feedforward neural networks with various performance goals. A cascade forward neural network is also constructed to be compared with the feedforward neural network. The simulation results demonstrate the effectiveness of these proposed neural networks.