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However, more efforts are needed in order to optimize the controlling parameters for best heat transfer enhancement.
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To solve this, the holistic control scheme is built to simplify the hierarchical structure of the controller and directly optimize the control inputs of system.
It is combined with nonlinear proportional-integral controllers to optimize the control parameters of the actuators.
Multi-response optimization technique was also employed to optimize the control factors.
Reinforcement learning is used to optimize the control strategy of the cuttlefish robot instead of manual adjustment.
Suitable parametric analyses are carried out to investigate and optimize the control devices.
To optimize the control measure, a series of experiments have been conducted.
This operation boundary can be used to design and optimize the control system.
Based on a linearized model, a linear quadratic regulator (LQR) is designed to optimize the control performance.
During autonomous drive, while PNC halts learning VNC keeps on correcting its neural weights to optimize the control performance.
Afterwards, making use of this FE model, a theoretical procedure is developed to optimize the control system.
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