Optimized hierarchical radial basis function neural networks by developing coronavirus herd immunity optimizer for solid oxide fuel cells

A new blackbox technique has been presented in this article for model estimation of solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a hierarchical radial basis function (HRBF). The presented method is then developed by a new modified metaheuristic called developed coronavirus herd immunity algorithm (DCHIA). The suggested model has been named DCHIA-HRBF. The proposed model is then trained by some data and prepared for identification and prediction. The model is then analyzed and put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. The results show that the best cost for the performance index which is the network error, is achieved by the proposed developed coronavirus herd immunity algorithm with about 119.442, which is satisfying for the considered function and target against the other state-of-the-art methods. As a result, the simulation results specified that the suggested DCHIA-HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs. © 2023 John Wiley & Sons, Ltd.

Telerehabilitation with Exoskeletons using Adaptive Robust Integral RBF-Neural-Network Impedance Control under Variable Time Delays

With an unprecedented increase in the global aging population and with it, the age-related neuromuscular dysfunction diseases, there is an exorbitant and escalating need for physical rehabilitation. Delivering these services - especially to those that are most vulnerable - under the current COVID-19 pandemic restriction for physical-distancing, is an even greater challenge. Interest in telerehabilitation is spiking, and robotic telerehabilitation could drastically improve patients' access to Some of the major challenges in developing the control methods for these robots are identifying, estimating, and overcoming the effects of dynamic modeling uncertainties, nonlinearities, and disturbances. Having humans in the loop creates the additional need for safety and compliance. Telerehabilitation control methods have the added requirement of delivering telepresence and addressing communication delays which, if not managed, could result in ineffective therapy, destabilize the system, and even cause injury. In this paper, we present a novel adaptive robust integral Radial Basis Function Neural Network Impedance model (RBFNN-I) control method for telerehabilitation with robotic exoskeletons which compensates for dynamic modeling uncertainties in the presence of external human torques and time delays. One of the salient features of the proposed control system is the implementation of a new human torque regulator which improves telepresence. Stability proof using Lyapunov stability theory is shown for the proposed control method. An exoskeleton was designed and used for unilateral and bilateral telerehabilitation simulations. Excellent tracking performance, telepresence and stability was achieved in the presence of large, variable and asymmetric time delays and human torques.