This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability.

The paper presents a new method for data-driven feedforward compensation of static and quasi-static forces acting on a multi-axis medical robotic manipulator. The proposed approach uses a look-up current calibration table (CCT) and an adaptive algorithm updating the CCT to ensure that the manipulator maintains accurate, fast, and safe performance over time. The key aspect of our control strategy is called data assimilation step, which involves modelling the CCT using an approximating function. We use the NURBS (Non-uniform rational basis spline) technique, which has desirable properties such as high accuracy and flexibility in approximating and even interpolating complex functions. The technique allows the manipulator to compensate for external disturbances such as gravity, friction and gear or cabling resistance. This can improve the precision and reduce the downtime of the manipulator due to periodic feedforward recalibration.

Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial ‘know-how’ of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale ‘Intelligent Motion Control under Industry4.E’ (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework’s applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.