Nonlinear identification of IPMC actuators employing RFNNNARX model and Particle Swarm Optimization Ionic Polymer Metal Composite (IPMC) is a new smart material that can be used as both sensors and actuators in numerous industrial and medical applications. However, IPMC actuators have many disadvantages, such as hysteresis nonlinearity, which make it difficult to model accurately. In this paper, we presented a precise dynamic nonlinear model with temporal operation for IPMC actuators based on a recurrent fuzzy neural network (RFNN) with interpretability, universal approximation ability and learning ability, a nonlinear autoregressive with exogenous input (NARX) structure with potential to predict future outputs, Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm with local and global optimization capability and recurrent least squares (RLS).Firstly, a suitable mathematical model with temporal relations using RFNNNARX structure was constructed. Then, AWPSO and RLS were employed to identify unknown parameters by using the collected experimental data. Finally, verification results were provided to show the capability of the presented method to effectively and accurately predict the actual displacement of IPMC actuators.

Modelless Active Compliance for Continuum Robots using Recurrent Neural Networks Endowing continuum robots with compliance while it interacts with the internal environment of the human body is essential to prevent damage to the robot and the surrounding tissues. Compared with passive compliance, active compliance has the advantages in terms of increasing the force transmission ability and improving safety with monitored force output. Previous studies have demonstrated that active compliance can be achieved based on a complex model of the mechanics combined with a traditional machine learning technique such as a support vector machine. This paper proposes a recurrent neural network (RNN) based approach that avoids the complexity of modeling while capturing nonlinear factors such as hysteresis, friction and delay of the electronics that are not easy to model. The approach is tested on a 3tendon singlesegment continuum robot with force sensors on each cable. Experiments are conducted to demonstrate that the continuum robot with an RNN based feedforward controller is capable of responding to external forces quickly and entering an unknown environment compliantly.