An Online Model-Free Adaptive Tracking Controller for Cable-Driven Medical Continuum Manipulators

Continuum manipulators have demonstrated promising potential for flexible access and complicated operation and thus have been emerging and introduced in robot-assisted flexible endoscopy. However, due to their inherent structural compliance and strong nonlinearities, developing an accurate and robust control framework remains challenging. This paper proposes a model-free control method based on the Model-Free Adaptive Control (MFAC) algorithm to accomplish the trajectory tracking for two kinds of continuum manipulators by solely utilizing the robotic system’s real-time input/output data. The presented controller discretizes and dynamically linearizes the motion process of the continuum actuator to obtain a dynamic linearization data (DLD) model. This DLD model can be derived from a pseudo-partial derivative (PPD) matrix updated based on the I/O measurement data for the iterative operation. The stability of the presented MFAC controller can be mathematically guaranteed in theory to provide generality, and the control framework demonstrates a low computational cost and real-time control capability. The superior performance of the presented controller is firstly validated in MATLAB simulations and then compared with the other two controllers. Through experimental validation on two kinds of continuum manipulators, the model-free control framework shows high tracking accuracy and good robustness against the system uncertainty and external disturbances, as well as high transferability

Model predictive control of a robotically actuated delivery sheath for beating heart compensation

Minimally invasive surgery (MIS) during cardiovascular interventions reduces trauma and enables the treatment of high-risk patients who were initially denied surgery. However, restricted access, reduced visibility and control of the instrument at the treatment locations limits the performance and capabilities of such interventions during MIS. Therefore, the demand for technology such as steerable sheaths or catheters that assist the clinician during the procedure is increasing. In this study, we present and evaluate a robotically actuated delivery sheath (RADS) capable of autonomously and accurately compensating for beating heart motions by using a model-predictive control (MPC) strategy. We develop kinematic models and present online ultrasound segmentation of the RADS that are integrated with the MPC strategy. As a case study, we use pre-operative ultrasound images from a patient to extract motion profiles of the aortic heart valve (AHV). This allows the MPC strategy to anticipate for AHV motions. Further, mechanical hysteresis in the steering mechanism is compensated for in order to improve tip positioning accuracy. The novel integrated system is capable of controlling the articulating tip of the RADS to assist the clinician during cardiovascular surgery. Experiments demonstrate that the RADS follows the AHV motion with a mean positioning error of 1.68 mm. The presented modelling, imaging and control framework could be adapted and applied to a range of continuum-style robots and catheters for various cardiovascular interventions