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The controller gain design algorithm is presented by using the cone complementarity linearization (CCL) method.
A hybrid model is presented by using genetic algorithm and response surface methodology for optimization.
The convergence of the asymptotic control is presented by using Lyapunov stability theory.
The stability analysis of the model-based control scheme is presented by using Lyapunov theory.
In building design, result of data inputs is presented by using geometry.
In the second step, approximate motion generation synthesis of spherical dyad is presented by using least-square approximation.
A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach.
Secondly, the sufficient condition for globally bound of the state is presented by using ISS theory and LMI method.
The complementarity with other localization algorithms is presented by using their results as initialization stage for this algorithm.
Then, a fault diagnosis scheme is presented by using a bank of the proposed fault estimation filters.
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The initial process validation has been presented by using SU-8 material.
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