Pneumatic artificial muscle (PAM), featuring good flexibility and safety, has been widely used in rehabilitation and bionic robots. However, the complex hysteretic nonlinearities and uncertainties of the PAM cause great difficulties and challenges to the accurate modeling and controller design, especially when confronted with unknown external disturbances in applications. This paper proposes a robust control strategy with disturbance compensation for the hysteresis compensation and trajectory tracking of PAMs. Considering the high hysteretic nonlinearity of the PAM, a modified Prandtl-Ishlinskii model is used as a feedforward hysteresis compensator. For the linearized system, adaptive set-membership filtering (ASMF) is used to estimate the nonlinear terms and external disturbances of the overall system. A sliding mode controller (SMC) with disturbance compensation is designed and cascaded to the feedforward hysteresis compensator in series. The stability of the closed-loop system is theoretically proved. The proposed method guarantees that the tracking error of the PAM system is bounded. Finally, the effectiveness and robustness of the proposed controller are verified via a series of experiments on an in-house built testbench for PAMs. Note to Practitioners —With the increasing demand on human-robot interaction, the safety and compliance of robots have become one key requirement. PAM is a compliant actuator, exhibiting good flexibility, safety, and clean energy. PAM is widely used in rehabilitation robots, whereas its strong hysteresis nonlinearity and sensitivity to external disturbances affect its motion accuracy. This paper proposes an ASMF-based discrete SMC, which uses an inverse hysteresis model to compensate for the strong hysteresis of the PAM and uses ASMF to estimate the lumped disturbance of the system. Compared with the other filters, ASMF is unique in that its estimation error is bounded, which is very useful in the stability proof of the overall system. The effectiveness of the proposed controller is experimentally verified. Experimental results show that PAM’s hysteresis can be efficiently compensated, and the influence of external disturbances can be attenuated by the proposed controller, resulting in improved motion accuracy and robustness. In future work, efforts will be directed towards the modeling and control of PAMs in multi-DOF robots.

The hysteretic nonlinearity of pneumatic artificial muscle (PAM) is the main factor that degrades its tracking accuracy. This paper proposes an efficient hysteresis compensation method based on the active modeling control (AMC). Firstly, the Bouc–Wen model is adopted as the reference model to describe the hysteresis of the PAM. Secondly, the modeling errors are introduced into the reference model, and the unscented Kalman filter is used to estimate the state of the system and the modeling errors. Finally, a hysteresis compensation strategy is designed based on AMC. The compensation performances of the nominal controller with without AMC were experimentally tested on a PAM. The experimental results show that the proposed controller is more robust when tracking different types of trajectories. In the transient, both the overshoot and oscillation can be successfully attenuated, and fast convergence is achieved. In the steady-state, the proposed controller is more robust against external disturbances and measurement noise. The proposed controller is effective and robust in hysteresis compensation, thus improving the tracking performance of the PAM.

Hysteresis is a typical nonlinear characteristic that exists in mechanical systems, which brings significant challenges to the robust tracking control of twisted tendon-sheath systems. In this paper, an active data-driven model is proposed to describe the hysteresis phenomenon of a twisted tendon-sheath system based on the Koopman operator, and a robust controller is designed to cancel the effect of the model error and deal with the physical constraints in practical applications. First, by utilizing the Koopman theory, an active data-driven model is built to describe the twisted tendon-sheath hysteresis system in a straightforward linear form. Then, an active model is proposed based on a modified set-membership filter to estimate the finite-dimensional approximation error. Furthermore, a robust controller is developed by taking advantage of both the magnitude and bound of the model error (obtained by the active model) to enhance the control performance while considering security constraints. To the best of our knowledge, the rule-based constraint term is first considered in the data-driven model-based control scheme to prevent potential instabilities for the twisted tendon-sheath system. The theoretical stability of the closed-loop system is proven by using the barrier Lyapunov theory to ensure the security boundary. Extensive experiments are also carried out on a self-built robotic ureteroscopy prototype to demonstrate the superior tracking performance and robustness of the proposed method. Note to Practitioners —This paper is motivated by the accurate transmission problems of twisted tendon-sheath hysteresis systems, which aims to provide a precise active modeling method and a robust controller for the robotic-assisted instrument (e.g., endoscope, catheter, etc.) twisting in the sheath/orifice. Most existing studies on tendon-sheath hysteresis systems realize trajectory tracking controllers by using parametric-model-based compensation, which still lacks a practical data-driven modeling approach to characterize the hysteresis phenomenon in the linear form, and ignore the security constraints of tendon outputs. Based on the set-membership filter, this paper builds an active Koopman-based model, which is a practical method to follow for systems characterized by complex dynamics. Subsequently, by employing the constructed active model and a rule-based term to handle output constraints, a robust controller is elaborately designed to realize accurate tracking control for twisted tendon-sheath hysteresis systems. In particular, no priori knowledge of the complex dynamics is required in the implementation and gains selection of the proposed controller, which holds theoretically and practically significance for various tendon-sheath hysteresis systems. A series of comparative hardware experiments further validate the effectiveness and robustness of the suggested control scheme. In future work, we will aim to extend the applicability of the proposed active modeling and control scheme to interventional procedures of endoscopic operation robots for complex steerings with varying sheath configurations.

Due to the existence of unknown rigid–flexible coupling characteristics, the robust and accurate tracking control of the twisted tendon-sheath actuated system (TAS) is a challenging problem. In this research, a novel adaptive control scheme is proposed to achieve accurate trajectory tracking of the twisting angle of TAS. For the first time, a “hybrid” model is built by combining the rigid-link dynamics with the hysteresis phenomenon model, accounting for the unknown rigid–flexible coupling effects. Then, time-delay estimation is utilized to evaluate the lumped dynamics of the system. Based on the estimation results, a barrier function-based adaptive method is designed for the estimation, of which the updating speed is constrained and intuitive to adjust. With the estimation of the lumped uncertainties and adaptive law, a model-free control scheme is designed for TAS. Finally, the stability of the closed-loop system is theoretically guaranteed by the Lyapunov-based analysis. The effectiveness and robustness of the proposed controller are adequately validated through a series of experiments on a robotic ureteroscopy prototype. To the best of the authors' knowledge, it is the first thorough investigation of control issues related to “flexible endoscope twisting in catheter” in the field of medical robots.

Tendon–sheath artificial muscle (TSAM) is a type of artificial muscle, which is widely used in robotic flexible endoscopies. The characteristics of the tendon force/position transmission were studied recently, but the control issue of the TSAM twist is still barely concerned. In this article, an effective robust control scheme is proposed for the TSAM twist , which achieves satisfactory tracking performance. First, a rigorous model was developed for the TSAM twist with consideration of parameters uncertainty and actuator input constraints, moreover, the disturbance of the discontinuous friction (Coulomb friction) was analyzed. Next, by applying some signal operation methods, a nonlinear robust controller was designed. In addition, the stability of the control strategy was proven theoretically by utilizing Lyapunov-based theory. Finally, a series of hardware experiments were conducted on a robotic flexible ureteroscope test-bed. The feasibility of the proposed model, the effectiveness and robustness of the designed control method were extensively validated.

Image reconstruction in electrical impedance tomography (EIT) is ill-posed, manifested in the difficulty of estimating the dense conductivity distribution with less information. In the actual measurement, the restricted number of electrodes placed around the sensitive field is the main reason for the limited number of measured data. Aiming at this challenge, this article proposed a virtual channel based data augmentation (DA) method for EIT. By combining virtual channel technology and deep learning, we obtained a prior model for DA. A dataset containing 16000 samples was generated through EIT numerical simulations for model training. Then, the trained DA model was introduced before image reconstruction, and the distribution of conductivity changes was calculated with the sensitivity matrix-based algorithms. Both simulations and experiments proved that the proposed DA method can effectively improve the imaging quality without increasing physical electrodes. With the proposed method, the 8-electrode EIT system achieved comparable measurement performance with the 16-electrode EIT system.