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This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.

A vast amount of energy can be extracted from the untapped low‐grade heat from sources below 100 °C and the Gibbs free energy from salinity gradients. Therefore, a process for simultaneous and direct conversion of these energies into electricity using permselective membranes was developed in this study. These membranes screen charges of ion flux driven by the combined salinity and temperature gradients to achieve thermo‐osmotic energy conversion. Increasing the charge density in the pore channels enhanced the permselectivity and ion conductance, leading to a larger osmotic voltage and current. A 14‐fold increase in power density was achieved by adjusting the ionic site population of covalent organic framework (COF) membranes. The optimal COF membrane was operated under simulated estuary conditions at a temperature difference of 60 K, which yielded a power density of ≈231 W m−2, placing it among the best performing upscaled membranes. The developed system can pave the way to the utilization of the enormous supply of untapped osmotic power and low‐grade heat energy, indicating the tremendous potential of using COF membranes for energy conversion applications.