Neural-Network-Based Optimal Impedance Control for Robots in Physical Interaction With Soft Environments

With the growing demand for robots in emerging fields, such as smart medical and home services, their ability to interact with soft environments has received increased attention. Nevertheless, an overlooked issue is that the inadequate description of soft environments using a linear model may significantly diminish the accuracy of interaction control. In this article, a neural-network-based impedance control framework is proposed for robots to physically interact with soft environments and optimize interaction performance. Specifically, a nonlinear definition of soft environments is introduced based on the Hunt–Crossley (HC) model, with parameter identification utilizing a data-driven technique. Regarding system performance evaluated by a cost function, the determination of interaction behavior described by the impedance model is transformed into an optimal control problem. Moreover, to address model uncertainties, the original optimal control problem is redefined using a modified cost function with a constructed auxiliary system. Then, a critic network is employed to approximate the nonlinear optimal solution, thereby avoiding complicated mathematical derivations. Finally, the effectiveness of the proposed impedance adaptation strategy is validated through both simulations and experiments. Numerical results indicate that both the convergent cost and total cost are significantly reduced based on the proposed method compared to the linear-model-based impedance control, particularly for materials with viscoelastic properties, achieving a reduction of up to 30%.