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SUMMARYNovel kinematic architectures can be alternatives for designing energy efficient robotic systems. In this work, the impact of kinematic redundancies in the energy consumption of a planar PKM, the 3PRRR manipulator, is experimentally verified. Because of the presence of the kinematic redundancies, the inverse kinematic problem presents infinity solutions. In this way, a redundancy resolution scheme based on the Model Predictive Control technique is proposed and exploited. It can be concluded that the energy consumption of the non-redundant parallel manipulator 3RRR for executing predefined tasks can be considerably reduced by the inclusion of kinematic redundancies.

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Measuring the operational torques during the lifetime of a wind turbine gearbox offers a valuable source of information for design, monitoring, predictive maintenance and control, and can help reduce operational expenses and downtime. In this paper we investigate indirect sensing as a valuable alternative to direct sensing, which is costly and intrusive. The resulting virtual torque sensor makes use of an Augmented Extended Kalman filter (AEKF), a physics based model, and measurements. The measurements considered are encoders on the high-and low speed shaft together with strain sensors, which are easy to install on the gearbox housing. We first discuss the theoretical background of the AEKF and methods for tuning the AEKF parameters. We then give an overview on the models used. Next, we focus on a sensor selection algorithm that can select the most performant (e.g. high signal-to-noise ratio) sensors. Finally, we validate the virtual torque sensor numerically. We show that with encoder measurements on the high speed shaft and 7 strain gauges placed on the housing we can achieve a Normalized Root Mean Squared Error (NRMSE) of 1,27 % and 0,02 % for the low-and high speed shaft torque, respectively. Without any strain gauges (i.e., only with the encoders), the estimation accuracy degrades to an NRMSE of 1,37 % and 0,12 %. A further analysis of the estimation results with different amounts of strain gauges shows that the amount of strain gauges can be reduced without a significant loss in estimation accuracy. These results show the potential of virtual torque sensing as a mean to obtain operational torque information for the design, monitoring and control of wind turbine gearboxes.