Parallel elastic actuation is a highly promising concept for assisting preplanned trajectories such as repetitive tasks in industrial machines and robots. Nevertheless, due to the persisting challenges on spring lifetime, its full potential has yet to be leveraged in the industry. We propose a novel adaptive magnetic spring as a fatigue-free spring mechanism to enable variable stiffness actuators in long lifetime applications. The spring is designed to flexibly deal with variations in operating conditions, i.e., mass customization. We propose a co-design methodology which simultaneously optimizes the sizing of the magnetic spring (for the given machine and its operating conditions), together with the controls and ideal dynamic response of the system. This mechanism and the methodology are applied to a design problem of a weaving machine drivetrain, where the benefits of the adaptive magnetic spring are highlighted with respect to a fixed stiffness magnetic spring, and the current industrial benchmark without springs. Experimentally validated findings show a consistent and considerable improvement with respect to energy consumption and peak torque reduction of up to 47% and 64%, respectively, when comparing to the current industrial benchmark.

Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).

Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor may be installed at hundreds of customers. Because these tools perform distinct but highly similar tasks, it is interesting to be able to quickly produce a high-quality controller for machine $N+1$ given the controllers already produced for machines $1..N$. This is even more important when the controllers are learned through Reinforcement Learning, as training takes time, energy and other resources. In this paper, we apply Policy Intersection, a Policy Shaping method, to help a Reinforcement Learning agent learn to solve a new variant of a compressors control problem faster, by transferring knowledge from several previously learned controllers. We show that our approach outperforms loading an old controller, and significantly improves performance in the long run.