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The onboard subsystem controller based on PID control will achieve the motion control under the optimal command.
The proposed controller generates optimal command sequences dealing with the amount of thermal energy to be stored or released.
If the trajectory comprises multiple branches or successive flight sequences with dynamics changes and interior point constraints, discontinuities may occur in the optimal command law.
The expert system's structure was extensively presented by the authors in previous articles, as well as the ship stabilization algorithm which generates a optimal command sequence, from the perspective of energy consumption because the algorithm minimizes the water volumes transferred between the tanks to achieve the required position and orientation.
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Then the robust near-time-optimal command shaping technique is applied.
Here, we consider a hidden precision cost, analogous to the cost of motor commands in optimal control theory.
LQR-based clipped optimal control is used to command a magneto-rheological (MR) damper in semi-active control, where the MR damper is designed according to deterministic analysis of the active system.
However, there are profound differences between the two: a crucial theoretical difference explained at length in Friston (2011)—is that optimal control models generate optimal motor commands by minimising a cost function associated with movement.
In almost all cases an optimal control problem must be solved to find the vehicle command law along the trajectory.
Note that neither conventional motor control models nor optimal control schemes would predict that corticospinal projections should have modulatory properties (as a motor command must be driving, not modulatory).
The optimal control model has some commonalities with the active inference view, in that both propose that sensory inputs to motor cortex finesse its output: in optimal control theory, these inputs are state estimates that the optimal controller uses to optimise motor commands.
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