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In order to get natural and intuitive physical interaction in the pose adjustment of the minimally invasive surgery manipulator, a hybrid variable admittance model based on Fuzzy Sarsa(λ)-learning is proposed in this paper. The proposed model provides continuous variable virtual damping to the admittance controller to respond to human intentions, and it effectively enhances the comfort level during the task execution by modifying the generated virtual damping dynamically. A fuzzy partition defined over the state space is used to capture the characteristics of the operator in physical human-robot interaction. For the purpose of maximizing the performance index in the long run, according to the identification of the current state input, the virtual damping compensations are determined by a trained strategy which can be learned through the experience generated from interaction with humans, and the influence caused by humans and the changing dynamics in the robot are also considered in the learning process. To evaluate the performance of the proposed model, some comparative experiments in joint space are conducted on our experimental minimally invasive surgical manipulator.

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Abstract-Traditional droop-controlled system has assumed that generators can always generate the powers demanded from them. This is true with conventional sources, where fuel supplies are usually planned in advance. For renewable sources, it may also be possible if energy storage is available. Energy storage, usually as batteries, may however be expensive, depending on its planned capacity. Renewable sources are therefore sometimes installed as non-dispatch-able sources without storage. This may not be viable for remote grids, where renewable sources may be the only or major type of sources. In those cases, traditional droop scheme may not work well when its demanded power cannot be met by some renewable sources due to intermittency. When that happens, the system may become unstable with some sources progressively brought out of generation. To avoid such occurrence, an enhanced dual droop scheme is proposed for general two-stage converters with front rectifiers or dc-dc converters for conditioning powers from renewable sources and rear inverters for channeling powers to remote grids. Unlike the traditional droop scheme, the proposed dual droop scheme uses both dc-link voltage and generated powers for determining the required control actions, which have subsequently been proven stable by small-signal analysis. Experimental results have also verified the effectiveness of the dual droop scheme.