Impedance control with fixed parameters lacks the flexibility to adapt to dynamic and uncertain environments, which may not meet the task requirements during robot-environment interaction. In this paper, a novel impedance learning control method is proposed to enhance robotic adaptability in unknown environments. First, an adaptive gradient learning strategy is designed to optimize step size in iterative learning process, leveraging historical gradients for dynamic adjustment. Then, a data-efficient adaptive model based on Streaming Sparse Gaussian Process (SSGP) is employed to accelerate impedance learning convergence. Additionally, it also can reduce computational complexity and improve generalization by online removing redundant data points, which utilizes prior data to estimate impedance parameters. The simulation results have demonstrated that the proposed method outperforms traditional iterative learning control approaches in convergence speed and generalization, verifying the feasibility and validity of the proposed method.

This article proposes a spatial learning control system for robots to achieve a desired behavior during interacting with unknown environments. In contacting with the environment, the force is estimated by a force observer, so sensing devices are not required. Motivated by the human interaction versatility, the reference trajectory of the robot is updating with a learning law such that the interacting force can be maintained at a desired level. Compared with the trajectory iteration algorithm based on time domain, which requires maintaining a fixed motion speed for each iteration, the proposed method can remove this limitation and have better feasibility. The adaptive controller with neural networks can compensate the uncertain dynamics of the system and ensure the control accuracy. Through Lyapunov's theory, the system is proved to be stable, and all the states are bounded. Comparative simulations and experiments are conducted on a robot platform to verify the effectiveness of the proposed method.

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%.

Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.

In this article, a robust control scheme is proposed for robots to achieve an optimal performance in the process of interacting with external forces from environments. The environmental dynamics are defined as a linear model, and the interaction performance is evaluated by a defined cost function, which is composed of trajectory errors and force regulation. Based on admittance control, the reference adaptation method is used to minimize the cost function and achieve the optimal interaction performance. To make the trajectory tracking controller robust to the unknown disturbance of internal system dynamics, an auxiliary system is defined and the approximation optimal controller is designed. Experiments on the Baxter robot are conducted to verify the effectiveness of the proposed method.

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme.

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.