The paper proposes a parameter design of a reaction force observer (RFOB) under existence of modeling error/parameter fluctuation. Observer-based sensorless-force-control is a good approach to reduce phase lag in control systems. Hence, the performance improvement can be easily attained by such a technique. However, the RFOB does not always guarantee accuracy of estimated value and adds incorrect compliance on the system. Due to insufficient report about RFOB design, its calibration is conducted based on the designer's own experience. To calibrate the RFOB and achieve the correct force control quantitatively, the structure of the observer-based force control and physical interpretation of control loops should be revealed simply. The paper presents a condition to achieve the correct force control and design methodology of observers, thereby, providing a robust performance against parameter variation.

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The article reports a sprout of 3‐degree‐of‐freedom (DOF) control. The limitation of feedback control, including 2‐DOF control, has been raised for a long time; however, a suitable control theory for overcoming those limitations has still not been formulated. An underlying reason is that the disturbance‐suppression performance and noise sensitivity cannot be decoupled. Several researches have tried to tackle this problem using a hardware approach, as a noise level depends on a hardware configuration. It should be noted that the hardware design expands spatiotemporal resolution of a system. This approach helps in reducing noise, namely, it works as the 3rd‐DOF for a control system. Therefore, an improvement in the hardware design could be a new angle for solving a mixed‐sensitivity problem. This article quantitatively presents the relation between noise‐reduction performance and the spatiotemporal resolution, and provides a foothold for the 3‐DOF control.

This paper presents a design methodology of a Kalman filter for a multirate control system with a fast input system. There are many applications whose sensory system is slower than the input system in a power system. The sensory system is required to perform fast sensing while ensuring a low signal-to-noise ratio, although it requires additional time compared with that of a low-resolution converter. The Kalman filter is an effective tool in such a situation and it estimates parameters while eliminating noise. Because the optimality of this filter is ensured when the system model and noise variance on the system are well identified, rigorous discretization should be considered on the multirate control system. With this in mind, the paper presents the design method of a multirate Kalman filter as is the case for a single-rate controller. Further, the noise variance and oversampling ratio of the multirate controller is confirmed using the Monte Carlo method. Numerical simulation of a voltage inverter theoretically validates the design methodology.