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Thanks to this controller, the number of control updates could greatly reduce.
In this way the method converges over the subsequent control updates.
To keep such a CPS stable, feedback control with periodic computation tasks has been widely used, and its theoretical guarantee of stability has been made with periodic real-time task models that enforce strict periodic control updates.
Most pinning synchronization schemes for complex networks with or without time delays reported in the literature require continuous state measurements and control updates, which are not suitable for implementation on digital platforms.
Considering a differential-algebraic representation of the nonlinear system and applying the Lyapunov theory, a convex optimization problem in terms of LMI constraints is derived to ensure regional asymptotic stability of the origin while designing the trigger function parameters in order to minimize the control updates.
And values get far better mileage than internal control updates and policy or personnel changes.
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However, each central iteration of Algorithm 2 will trigger optimal values to be updated by the local controller and so the control update rate is dependent only on the central coordinator's update rate.
However, a variable control update rate can be used between the controller and the plant.
Periodical online model and control updating identification and optimal control enables the proposed controller to adapt to operating condition variations and system parameter uncertainties.
These filters are then deployed to choose the best number of bits per control update.
In order to reduce the computation time and the amount of inter-vehicle interaction, only one ADMM iteration is performed per control update.
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