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The only decent argument in favour of first past the post is that it is designed to guarantee stable, single-party government.
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The neural controller uses a TSK-type CMAC neural network (TCNN) to approximate an ideal controller and the supervisory compensator is designed to guarantee system stable in the Lyapunov stability theorem.
Then, based on the stability results, a state feedback controller is designed to guarantee the asymptotically stable of the closed-loop systems.
Using the Lyapunov functional theory, a stochastically stable filter is designed to guarantee both the mean-square exponential admissibility and a prescribed level of H∞ performance for the singular Markovian jump time-delay systems with general unknown transition probabilities.
A state feedback controller is designed to guarantee the resulting close-loop system stochastically stable.
A novel IT2 fault detection filter is designed to guarantee the residual system to be stochastically stable and satisfy the predefined H∞ performance.
Using Lyapunov stability theory, a novel type of IT2 fault detection filter is designed to guarantee that the fault detection system is asymptotically stable with an H∞ performance.
Based on the criterion, the desired IT2 state-feedback controller is designed to guarantee that the closed-loop system is asymptotically stable with a prescribed H∞ performance condition and all the poles rest in the disk region.
Then, based on this, a filter is designed to guarantee that the filtering error system is mean-square asymptotically stable and its L2 L∞ performance satisfies a prescribed level.
A robust decentralized adaptive sliding mode controller is designed to guarantee the uncertain stochastic delayed Hopfield neural networks is globally asymptotically stable in probability.
Since the system is not unconditionally stable, a digital phase lag compensator is designed to guarantee the stability for larger feedback gains.
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