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A new condition is derived that guarantees robust stability for a set of stable, linear time-invariant plants controlled by using a simplified model predictive control algorithm (SMPC).
A joint network-channel code construction is derived that guarantees full diversity, irrespective of the parameters of the LDPC code (the degree distributions).
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A state-feedback law is derived that can guarantee stability of the closed-loop system over a wide range of operation conditions.
By defining appropriate Lyapunov Krasovskii functional, sufficient conditions are derived that guarantee the desirable performance.
Bounds on the feedback gains, and learning rate parameters are derived that guarantee the origin of the closed loop system is semi-globally, uniformly bounded in expectation (SGUBE).
For a number of scenarios we derive conditions that guarantee the global stability and minimal convergence rates for the formation of coordinated behaviors of crowds.
Also the values of τ 1 and τ 2 cannot be chosen freely, we will derive an inequality that guarantees monotonicity of the transfer functions.
Future work could be conducted in order to derive a method that guarantees global optimality by introducing e.g. selection into the Benders' cuts or by convexifying the subproblems, as discussed in Section 3.3.
In addition, a backstepping formulation is derived using contraction theory that guarantees the boundedness of state response under bounded disturbances such as wind.
In fact, in the Cox-Jaynes approach, the "axioms" of probability aren't axioms, but get derived from desiderata that guarantee this kind of internal consistency of one's calculations.
We derive the conditions that guarantee the second-order accuracy of the velocity reconstruction and demonstrate its efficacy on arbitrary polygonal mesh topologies.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com