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A HCS comprises a higher level controller (HLC) and a lower level controller (LLC).
Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner.
The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication.
Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework.
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One key performance criterion is the ability to track brake clamp forces requested by higher level controllers such as antilock braking and electronic stability systems.
A hierarchical control architecture, consisting of a high level controller for trajectory tracking, and a control allocation algorithm, is developed and proved to be effective in tracking a desired trajectory while optimizing some cost related to actuator constraints.
The proposed approach takes advantage of the cascade structure of the system to design a hierarchical controller involving a High Level Controller devoted to position control and a Low Level Controller devoted to attitude stabilization.
The low level controller is designed to optimally regulate torque at each wheel based on the control inputs of the high level controller, and distribute required torque between the wheels via actuation system.
A hierarchical control system is designed where a high level controller (supervisor) is responsible for task decision, monitoring states of the vehicle, generating references for low level controllers, etc. and several low level controllers are responsible for attitude and altitude stabilization.
Planning has been traditionally limited to the high level controller of a robot.
The proposed model can be also used as a high level controller for a robotic dextrous hand during learning and execution of grasping tasks.
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