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It is shown that all the closed-loop signals are bounded and the output tracking error converges to zero asymptotically in spite of input quantization, disturbances and possibly infinite number of faults.
The designed controller guarantees that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the output of the closed-loop system converges to a small neighborhood of the desired output.
It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters.
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It is proved that all the signals of the closed-loop systems are bounded and the system output can follow the given bounded reference signal.
The proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the system output satisfies the prescribed performance.
Finally, it is proven that all the signals in the closed-loop system are bounded and the system output is not violated by using Lyapunov stability analysis method.
The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal.
It is proven that the proposed algorithm can guarantee that all the signals in the closed-loop system are bounded and the system output can converge to a neighborhood of zero based on the Lyapunov analysis method.
Comparing the results from two simulations, all the signals of closed-loop system are globally uniformly ultimately bounded, and the system output can be regulated to a neighborhood near the equilibrium point despite of different experiment samples.
Based on the technique above, we design a switching law and construct adaptive fuzzy controllers of subsystems explicitly by a recursive design algorithm to guarantee that all the signals in the resulting closed-loop system remain bounded and the system output of the closed-loop system converges to a small neighborhood of the reference signal.
Based on dynamic surface design and common Lyapunov function method, an adaptive approximation design methodology for the decentralized adaptive tracking is proposed to ensure that the total controlled closed-loop system is asymptotically bounded and the local output tracking performance is maintained within preselected time-varying bounds independent of switched interactions.
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