This article focuses on the vibration issue of flexible two-link manipulators (FTLMs) with input saturation. An efficient system model is represented by a set of ordinary differential equations (ODEs) based on the assumed mode method (AMM). Subsequently, a reinforcement learning (RL)-based adaptive vibration control strategy, which is a model-free control approach, is proposed by employing the actor–critic algorithm structure. Additionally, an auxiliary system is constructed to tackle the influence of input saturation, ensuring trajectory tracking while achieving vibration suppression. Furthermore, the stability of the closed-loop system under RL control is examined using the Lyapunov direct method, which demonstrates the semi-global uniform ultimate boundedness (SGUUB) of tracking and vibration errors. Finally, to verify the effectiveness and superiority of the proposed RL strategy, the comparative simulations and experimental studies are conducted on the Quanser experimental platform. The experimental results demonstrate that RL control reduces steady-state errors by 40% and 96.6% against PSF control and by 50% and 97.2% against neural network (NN) control, respectively.

Focused on the scientific issues of collision avoidance and compliant operation of physical human-robot interaction (pHRI) systems, this paper proposes a reinforcement learning (RL) strategy based on admittance control to achieve compliant collision avoidance and accurate trajectory tracking of pHRI. Firstly, a differentiable reference trajectory is generated using a soft saturation function with an admittance model. Subsequently, a reinforcement learning strategy based on an actor-critic structure is implemented to address dynamic uncertainty and enhance tracking performance and compliance. Different from existing studies, a reinforcement learning admittance controller containing an integral barrier Lyapunov function (IBLF) is constructed to attain accurate tracking while ensuring that the end-effector achieves the position constraints. Lyapunov stability theory is employed to proof that all states of the closed-loop system remain semiglobally uniformly ultimately bounded (SGUUB). Finally, a suite of tests on Baxter robot experimental platform have been conducted to validate the superiority of the proposed algorithm compared with adaptive impedance control and conventional admittance control.