The paper deals with a methodology for the derivation of PI(D) type fixed structure controllers commonly embedded in motion control loops. A collocated control design problem is formulated, considering two different plant outputs. The goal is to tune the controller parameters to achieve both robustness in stability with respect to a feedback variable and optimal performance with respect to a second penalised output. The proposed method is validated by means of a lift control benchmark problem.

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.

The paper deals with the problem of anti-sway control in human-operated gantry cranes. The goal is to design a suitable algorithm aiming at minimization of unwanted transient and residual oscillations of the manipulated load. A finite horizon optimization is adopted for the derivation of an optimal open-loop control strategy. The novelty of the proposed approach comes from the combination of model-based predictive control and zero-vibration input shaping methods. This allows utilizing some key advantages from both fields in terms of performance, robustness, constraints definition and simplicity of implementation. Experimental case study demonstrates the proposed approach and compares it to conventional input-shaping method.

Iterative learning control (ILC) enables a perfect compensation for systems that perform the same task over and over again. The aim of this paper is to demonstrate practical applicability of two various state-of-the-art ILC algorithms to point-to-point positioning systems. A simple Frequency domain ILC approach is exploited focusing on systems with exactly repeating motion tasks. Furthermore, flexible ILC is employed to enable learning also for non-repeating tasks. Particular steps providing a seamless transfer from theory and algorithms to practical implementation in a real-time environment by means of industrial-grade SW and HW are given. They may serve as a practical example of a workflow suitable for a wide range of motion control applications. Potential benefits of the learning-type control in comparison with conventional feedback and feedforward control are discussed as well.